diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 7c9eeba..c368d22 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -15,4 +15,4 @@ jobs: - uses: actions/setup-python@v5 with: python-version: "3.10" - - uses: pre-commit/action@v3.0.1 \ No newline at end of file + - uses: pre-commit/action@v3.0.1 diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml index ab926fc..c1f831a 100644 --- a/.github/workflows/pylint.yml +++ b/.github/workflows/pylint.yml @@ -10,16 +10,16 @@ jobs: matrix: python-version: ["3.10"] steps: - - uses: actions/checkout@v4 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install pylint - pip install . - - name: Analysing the code with pylint - run: | - pylint $(git ls-files '*.py') + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pylint + pip install . + - name: Analysing the code with pylint + run: | + pylint $(git ls-files '*.py') diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index c77edad..7f50380 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: "v5.0.0" + rev: "v6.0.0" hooks: #- id: check-added-large-files #- id: check-case-conflict @@ -16,7 +16,7 @@ repos: - id: trailing-whitespace - repo: https://github.com/astral-sh/ruff-pre-commit - rev: "v0.6.9" + rev: "v0.16.0" hooks: - id: ruff args: ["--fix", "--show-fixes"] @@ -38,18 +38,18 @@ repos: - id: rst-inline-touching-normal - repo: https://github.com/rbubley/mirrors-prettier - rev: "v3.3.3" + rev: "v3.9.6" hooks: - id: prettier types_or: [yaml, markdown, html, css, scss, javascript, json] - repo: https://github.com/abravalheri/validate-pyproject - rev: "v0.20.2" + rev: "v0.25" hooks: - id: validate-pyproject additional_dependencies: ["validate-pyproject-schema-store[all]"] - repo: https://github.com/kynan/nbstripout - rev: "0.7.1" + rev: "0.9.1" hooks: - id: nbstripout diff --git a/.pypirc b/.pypirc index f54bf8e..31352ed 100644 --- a/.pypirc +++ b/.pypirc @@ -1,3 +1,3 @@ [testpypi] username = __token__ - password = pypi-AgENdGVzdC5weXBpLm9yZwIkYTQ4MjJlN2MtNmQ0Zi00NzZjLTlhY2UtNWI0MWNiZTYzMzkzAAIqWzMsIjI5ZTJlMDdjLTcwZjMtNDU1OS04OTQxLWUwOTkyODU0YjM3NyJdAAAGIKklg7VuhVvw_CiRTFlCr8lDphNlu1epHvXAW6kwFM-r \ No newline at end of file + password = pypi-AgENdGVzdC5weXBpLm9yZwIkYTQ4MjJlN2MtNmQ0Zi00NzZjLTlhY2UtNWI0MWNiZTYzMzkzAAIqWzMsIjI5ZTJlMDdjLTcwZjMtNDU1OS04OTQxLWUwOTkyODU0YjM3NyJdAAAGIKklg7VuhVvw_CiRTFlCr8lDphNlu1epHvXAW6kwFM-r diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 80b241b..2ebb8e8 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -13,9 +13,9 @@ build: # Build documentation in the "docs/" directory with Sphinx sphinx: - configuration: docs/source/conf.py - fail_on_warning: false - builder: html + configuration: docs/source/conf.py + fail_on_warning: false + builder: html # Optionally build PDF and ePub formats formats: diff --git a/README.md b/README.md index 9ca4cc8..8eea5b5 100644 --- a/README.md +++ b/README.md @@ -1,27 +1,19 @@ # cc_mapping# Cell Cycle Mapping Package - - [![PyPI version](https://badge.fury.io/py/cc-mapping.svg)](https://badge.fury.io/py/cc-mapping)## Step 1: Install Environment [![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)From the root directory of this repository: - - **Gaussian Mixture Model-based thresholding for single-cell gene expression analysis**``` conda env create -f .\environments\cc_mapping.yml `cc_mapping` provides robust statistical methods for categorizing cells based on gene expression levels using Gaussian Mixture Models (GMMs). Originally developed for cell cycle analysis, it's applicable to any single-cell RNA-seq thresholding task.``` - - ## Features## Step 2: Update Global Variables - - - 🎯 **Automatic thresholding** using GMM-based statistical inferenceDue to the fact this is not an actual package, whenever you want to use it, you will have to tell your computer where to look. You will need to update these two files: - 📊 **Single & sequential thresholding** for simple or complex categorization schemes @@ -34,17 +26,13 @@ conda env create -f .\environments\cc_mapping.yml - ⚙️ **Flexible configuration** with manual threshold overrides when neededReplace the variable 'cc_mapping_package_dir' with the path to the root directory for the cc_mapping repository. - - ## InstallationThis means that if you want to use the cc_mapping package in another folder, you should copy this GLOBAL VARIABLES folder into that directory and add this to the imports of your python scripts - - Install from PyPI using pip:``` import sys -```bashsys.path.append(os.getcwd()) +````bashsys.path.append(os.getcwd()) pip install cc-mapping @@ -90,7 +78,7 @@ adata = gmm.return_adata() # Visualize results fig = gmm.plot_density() fig.savefig('pcna_thresholding.png') -``` +```` ## Sequential Thresholding diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css index f51baa4..7e13e3e 100644 --- a/docs/source/_static/custom.css +++ b/docs/source/_static/custom.css @@ -2,20 +2,20 @@ /* Widen the primary sidebar (left sidebar) */ :root { - --pst-sidebar-primary-width: 5vw; /* 5% of viewport width */ - --pst-sidebar-secondary-width: 70em; /* Default is ~17rem - right sidebar "On this page" */ + --pst-sidebar-primary-width: 5vw; /* 5% of viewport width */ + --pst-sidebar-secondary-width: 70em; /* Default is ~17rem - right sidebar "On this page" */ } /* Force primary sidebar width (if variable doesn't work) */ .bd-sidebar-primary { - width: 20vw !important; - min-width: 0 !important; /* Remove any minimum width constraint */ + width: 20vw !important; + min-width: 0 !important; /* Remove any minimum width constraint */ } /* Increase main content area width */ .bd-container__inner.bd-page-width, .bd-page-width { - max-width: 95vw !important; /* Default is around 88rem/1408px, adjust as needed */ + max-width: 95vw !important; /* Default is around 88rem/1408px, adjust as needed */ } /* Alternative: Remove max-width entirely for full-width content */ @@ -27,243 +27,243 @@ /* Fix text wrapping in right sidebar TOC */ .bd-toc a, .bd-sidebar-secondary a { - white-space: normal !important; - word-wrap: break-word !important; - overflow-wrap: break-word !important; - text-overflow: clip !important; + white-space: normal !important; + word-wrap: break-word !important; + overflow-wrap: break-word !important; + text-overflow: clip !important; } /* Remove max-width constraints on sidebar secondary (API pages) */ .bd-sidebar-secondary, .bd-sidebar-secondary .bd-toc, div.bd-sidebar-secondary.bd-toc { - max-width: none !important; - width: 40% !important; + max-width: none !important; + width: 40% !important; } /* Ensure the TOC container takes full width */ .bd-toc nav, .bd-toc .toc-item, .bd-toc li { - max-width: none !important; - width: 40% !important; + max-width: none !important; + width: 40% !important; } /* Make sure links can use full width */ .bd-sidebar-secondary a.reference { - max-width: none !important; - width: auto !important; - display: block !important; + max-width: none !important; + width: auto !important; + display: block !important; } /* Specific styles for notebook/tutorial pages ONLY */ /* Target the visible nav section with flex-column class */ .bd-sidebar-secondary nav.visible.nav-section.flex-column { - min-width: 300px !important; - width: 100% !important; + min-width: 300px !important; + width: 100% !important; } /* Make notebook TOC items wider */ .bd-sidebar-secondary .toc-h2.nav-item.toc-entry, .bd-sidebar-secondary .toc-h3.nav-item.toc-entry { - width: 100% !important; - max-width: none !important; + width: 100% !important; + max-width: none !important; } /* Ensure links in notebook TOC can wrap and use full width */ .bd-sidebar-secondary .toc-h2 a.reference.internal.nav-link, .bd-sidebar-secondary .toc-h3 a.reference.internal.nav-link { - width: 100% !important; - max-width: none !important; - white-space: normal !important; + width: 100% !important; + max-width: none !important; + white-space: normal !important; } .intro-card { - margin: 15px 0; - height: 100%; - border: 1px solid rgba(0, 0, 0, 0.125); - border-radius: 0.5rem; + margin: 15px 0; + height: 100%; + border: 1px solid rgba(0, 0, 0, 0.125); + border-radius: 0.5rem; } .intro-card:hover { - box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15) !important; - transition: all 0.3s ease; + box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15) !important; + transition: all 0.3s ease; } .intro-card .card-title { - font-size: 1.5rem; - font-weight: 600; - margin-bottom: 1rem; + font-size: 1.5rem; + font-weight: 600; + margin-bottom: 1rem; } .intro-card .card-text { - color: #6c757d; - margin-bottom: 1.5rem; + color: #6c757d; + margin-bottom: 1.5rem; } .custom-button { - margin-top: auto; + margin-top: auto; } .custom-button a { - display: inline-block; - padding: 0.5rem 1.5rem; - background-color: #007bff; - color: white !important; - text-decoration: none; - border-radius: 0.25rem; - transition: background-color 0.15s ease-in-out; + display: inline-block; + padding: 0.5rem 1.5rem; + background-color: #007bff; + color: white !important; + text-decoration: none; + border-radius: 0.25rem; + transition: background-color 0.15s ease-in-out; } .custom-button a:hover { - background-color: #0056b3; - text-decoration: none; + background-color: #0056b3; + text-decoration: none; } /* Container spacing */ .container { - margin-top: 2rem; - margin-bottom: 2rem; + margin-top: 2rem; + margin-bottom: 2rem; } /* Feature list styling */ ul { - list-style-type: disc; - margin-left: 1.5rem; + list-style-type: disc; + margin-left: 1.5rem; } ul li { - margin-bottom: 0.5rem; + margin-bottom: 0.5rem; } /* Reduce indentation in right sidebar TOC to match scikit-learn */ /* Target the page TOC specifically */ .bd-toc .page-toc ul { - padding-left: 0 !important; - list-style: none; + padding-left: 0 !important; + list-style: none; } /* Remove indentation from nested lists */ .bd-toc .page-toc ul ul { - padding-left: 0.5rem !important; /* Minimal indentation like sklearn */ - margin-left: 0.8rem !important; + padding-left: 0.5rem !important; /* Minimal indentation like sklearn */ + margin-left: 0.8rem !important; } /* Remove padding from list items */ .bd-toc .page-toc li { - padding-left: 0.5rem !important; - margin-left: 0.5rem !important; + padding-left: 0.5rem !important; + margin-left: 0.5rem !important; } /* Remove padding from links */ .bd-toc .page-toc a { - padding-left: 0 !important; - display: block; + padding-left: 0 !important; + display: block; } /* Nested items get minimal left padding only */ .bd-toc .page-toc ul ul li a { - padding-left: 0.1rem !important; + padding-left: 0.1rem !important; } /* Additional overrides for Bootstrap nav classes */ .bd-toc .nav { - padding-left: 0 !important; + padding-left: 0 !important; } .bd-toc .nav-item { - padding-left: 0 !important; + padding-left: 0 !important; } .bd-toc .nav-link { - padding-left: 0 !important; + padding-left: 0 !important; } /* Override nested nav elements */ .bd-toc nav ul, .bd-toc nav.bd-toc-nav ul { - padding-left: 0 !important; - margin-left: 0 !important; + padding-left: 0 !important; + margin-left: 0 !important; } .bd-toc nav ul ul, .bd-toc nav.bd-toc-nav ul ul { - padding-left: 0.1rem !important; + padding-left: 0.1rem !important; } /* Hide Parameters and __init__ sections from the TOC */ .bd-toc a[href*="parameters"], .bd-toc a[href*="Parameters"], .bd-toc a[href*="__init__"] { - display: none !important; + display: none !important; } /* Reduce spacing in API documentation to match scikit-learn */ /* Tighten spacing around attributes and parameters */ dl.field-list { - margin-top: 0.25rem !important; - margin-bottom: 0.25rem !important; + margin-top: 0.25rem !important; + margin-bottom: 0.25rem !important; } dl.field-list > dt { - padding-top: 0 !important; - padding-bottom: 0 !important; - margin-bottom: 0 !important; + padding-top: 0 !important; + padding-bottom: 0 !important; + margin-bottom: 0 !important; } dl.field-list > dd { - margin-top: 0 !important; - margin-bottom: 0.25rem !important; - padding-left: 2rem !important; + margin-top: 0 !important; + margin-bottom: 0.25rem !important; + padding-left: 2rem !important; } /* Reduce spacing in definition lists (attributes section) */ dl.py.attribute, dl.py.property, dl.py.method { - margin-bottom: 0.5rem !important; + margin-bottom: 0.5rem !important; } /* Tighten up the Type field spacing */ dl.field-list dt { - font-weight: bold; - margin-top: 0 !important; + font-weight: bold; + margin-top: 0 !important; } /* Reduce spacing between parameter entries */ dd > p { - margin-top: 0 !important; - margin-bottom: 0 !important; + margin-top: 0 !important; + margin-bottom: 0 !important; } /* Reduce spacing in attribute/method sections */ .py.attribute > dt, .py.property > dt, .py.method > dt { - padding-top: 0.25rem !important; - padding-bottom: 0.25rem !important; - margin-bottom: 0.25rem !important; + padding-top: 0.25rem !important; + padding-bottom: 0.25rem !important; + margin-bottom: 0.25rem !important; } /* Reduce spacing in dd elements (descriptions) */ .py.attribute > dd, .py.property > dd, .py.method > dd { - margin-top: 0 !important; - margin-bottom: 0.5rem !important; - margin-left: 2rem !important; + margin-top: 0 !important; + margin-bottom: 0.5rem !important; + margin-left: 2rem !important; } /* Tighten spacing in parameter lists */ dl.field-list dd ul, dl.field-list dd ol { - margin-top: 0 !important; - margin-bottom: 0 !important; - padding-left: 1.5rem !important; + margin-top: 0 !important; + margin-bottom: 0 !important; + padding-left: 1.5rem !important; } dl.field-list dd ul li, dl.field-list dd ol li { - margin-top: 0 !important; - margin-bottom: 0 !important; + margin-top: 0 !important; + margin-bottom: 0 !important; } diff --git a/docs/source/api/cc_mapping.thresholding.rst b/docs/source/api/cc_mapping.thresholding.rst index 7808223..40689a4 100644 --- a/docs/source/api/cc_mapping.thresholding.rst +++ b/docs/source/api/cc_mapping.thresholding.rst @@ -23,10 +23,8 @@ Classes .. autosummary:: :toctree: generated :nosignatures: - + GMMThresholding SequentialGMM - - diff --git a/docs/source/api/cc_mapping.utils.rst b/docs/source/api/cc_mapping.utils.rst index e56adf4..5f018ed 100644 --- a/docs/source/api/cc_mapping.utils.rst +++ b/docs/source/api/cc_mapping.utils.rst @@ -20,8 +20,6 @@ General-purpose utility functions for data manipulation and analysis. .. autosummary:: :toctree: generated :nosignatures: - - - create_boolean_label_combination + create_boolean_label_combination diff --git a/docs/source/api/generated/cc_mapping.thresholding.GMMThresholding.rst b/docs/source/api/generated/cc_mapping.thresholding.GMMThresholding.rst index d2b2832..acd233a 100644 --- a/docs/source/api/generated/cc_mapping.thresholding.GMMThresholding.rst +++ b/docs/source/api/generated/cc_mapping.thresholding.GMMThresholding.rst @@ -8,13 +8,13 @@ :show-inheritance: :inherited-members: - - + + .. rubric:: Methods .. autosummary:: :nosignatures: - + ~GMMThresholding.categorize_samples ~GMMThresholding.determine_optimal_components ~GMMThresholding.determine_optimal_number_components @@ -28,9 +28,9 @@ ~GMMThresholding.plot_strip_plot_histogram_with_decision_boundaries ~GMMThresholding.return_adata ~GMMThresholding.return_thresholds - - - - - \ No newline at end of file + + + + + diff --git a/docs/source/api/generated/cc_mapping.thresholding.SequentialGMM.rst b/docs/source/api/generated/cc_mapping.thresholding.SequentialGMM.rst index bdcc9ee..b5d445a 100644 --- a/docs/source/api/generated/cc_mapping.thresholding.SequentialGMM.rst +++ b/docs/source/api/generated/cc_mapping.thresholding.SequentialGMM.rst @@ -8,13 +8,13 @@ :show-inheritance: :inherited-members: - - + + .. rubric:: Methods .. autosummary:: :nosignatures: - + ~SequentialGMM.determine_optimal_number_components ~SequentialGMM.generate_thresholding_report ~SequentialGMM.plot_bayesian_information_criterion_curve @@ -26,9 +26,9 @@ ~SequentialGMM.refine_labels_with_manual_thresholds ~SequentialGMM.return_adata ~SequentialGMM.threshold_entire_dataset - - - - - \ No newline at end of file + + + + + diff --git a/docs/source/api/generated/cc_mapping.utils.create_boolean_label_combination.rst b/docs/source/api/generated/cc_mapping.utils.create_boolean_label_combination.rst index 08689f9..b78780f 100644 --- a/docs/source/api/generated/cc_mapping.utils.create_boolean_label_combination.rst +++ b/docs/source/api/generated/cc_mapping.utils.create_boolean_label_combination.rst @@ -3,4 +3,4 @@ .. currentmodule:: cc_mapping.utils -.. autofunction:: create_boolean_label_combination \ No newline at end of file +.. autofunction:: create_boolean_label_combination diff --git a/docs/source/conf.py b/docs/source/conf.py index a9cb23e..89c1292 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -126,9 +126,10 @@ } # -- Generate API reference pages from templates ---------------------------- -import jinja2 # noqa: E402 -from pathlib import Path # noqa: E402 -from api_reference import API_REFERENCE # noqa: E402 +from pathlib import Path + +import jinja2 +from api_reference import API_REFERENCE # Define templates for API reference pages rst_templates = [ diff --git a/docs/source/index.rst b/docs/source/index.rst index a861643..2f34dcc 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -71,20 +71,19 @@ Features .. toctree:: :maxdepth: 1 :hidden: - + installation .. toctree:: :maxdepth: 2 :hidden: :caption: API Reference - + api/index .. toctree:: :maxdepth: 2 :hidden: :caption: Examples - - tutorials/index + tutorials/index diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 3db1ee0..43383ef 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -15,20 +15,20 @@ Use :class:`~cc_mapping.thresholding.GMMThresholding` for thresholding a single import scanpy as sc from cc_mapping.thresholding import GMMThresholding - + # Load your data adata = sc.read_h5ad("your_data.h5ad") - + # Create thresholder gmm = GMMThresholding() - + # Fit the model gmm.fit(adata, feature_key="PCNA") - + # Access results print(f"Threshold: {gmm.threshold}") print(f"Categories: {gmm.categories}") - + # Visualize gmm.plot() @@ -40,16 +40,16 @@ Use :class:`~cc_mapping.thresholding.SequentialGMM` for multiple features: .. code-block:: python from cc_mapping.thresholding import SequentialGMM - + # Create sequential thresholder seq_gmm = SequentialGMM() - + # Define features to threshold sequentially features = ["PCNA", "CDK2", "Geminin"] - + # Fit sequentially seq_gmm.fit(adata, feature_keys=features) - + # Visualize all steps seq_gmm.plot() @@ -62,10 +62,10 @@ The thresholding results are stored in the AnnData object: # Access categorized cells categories = adata.obs["PCNA_categories"] - + # Filter for high-expressing cells high_cells = adata[adata.obs["PCNA_categories"] == "High"] - + # Continue with downstream analysis sc.pl.umap(adata, color="PCNA_categories") diff --git a/docs/source/quickstart/basic_usage.rst b/docs/source/quickstart/basic_usage.rst index 0d062db..4d36386 100644 --- a/docs/source/quickstart/basic_usage.rst +++ b/docs/source/quickstart/basic_usage.rst @@ -20,7 +20,7 @@ Basic Workflow 1. **Load your data** as an AnnData object 2. **Choose a thresholder**: - + - :class:`~cc_mapping.thresholding.GMMThresholding` for single features - :class:`~cc_mapping.thresholding.SequentialGMM` for multiple features @@ -35,17 +35,17 @@ Quick Example import scanpy as sc from cc_mapping.thresholding import GMMThresholding - + # Load your data adata = sc.read_h5ad("your_data.h5ad") - + # Create and fit thresholder gmm = GMMThresholding() gmm.fit(adata, feature_key="PCNA") - + # Visualize gmm.plot() - + # Access results print(adata.obs["PCNA_categories"].value_counts()) diff --git a/docs/source/quickstart/index.rst b/docs/source/quickstart/index.rst index 3a84b8f..4a06a98 100644 --- a/docs/source/quickstart/index.rst +++ b/docs/source/quickstart/index.rst @@ -5,7 +5,7 @@ Get started quickly with cc-mapping thresholding tools. .. toctree:: :maxdepth: 2 - + installation basic_usage single_thresholding @@ -14,7 +14,7 @@ Get started quickly with cc-mapping thresholding tools. Overview -------- -The cc-mapping package provides powerful Gaussian Mixture Model-based thresholding +The cc-mapping package provides powerful Gaussian Mixture Model-based thresholding for cell cycle analysis. This guide will help you get started with the main features. Choose your workflow: diff --git a/docs/source/quickstart/sequential_thresholding.rst b/docs/source/quickstart/sequential_thresholding.rst index ecfc7c9..d15b2ca 100644 --- a/docs/source/quickstart/sequential_thresholding.rst +++ b/docs/source/quickstart/sequential_thresholding.rst @@ -32,7 +32,7 @@ Step-by-Step Guide import scanpy as sc from cc_mapping.thresholding import SequentialGMM - + # Load your data adata = sc.read_h5ad("your_data.h5ad") @@ -43,7 +43,7 @@ Step-by-Step Guide # Initialize seq_gmm = SequentialGMM() - + # Or customize seq_gmm = SequentialGMM( gmm_kwargs={'random_state': 42}, @@ -73,7 +73,7 @@ Step-by-Step Guide # Visualize all thresholding steps seq_gmm.plot() - + # Or visualize specific features seq_gmm.plot_hist_distribution_with_boundaries(feature_key="PCNA") @@ -84,7 +84,7 @@ Step-by-Step Guide # Get final cell categorization categories = adata.obs[seq_gmm.thresholding_events_key] - + # Get the updated AnnData object adata_result = seq_gmm.return_adata() diff --git a/docs/source/quickstart/single_thresholding.rst b/docs/source/quickstart/single_thresholding.rst index 22b1471..cf7442d 100644 --- a/docs/source/quickstart/single_thresholding.rst +++ b/docs/source/quickstart/single_thresholding.rst @@ -22,7 +22,7 @@ Step-by-Step Guide import scanpy as sc from cc_mapping.thresholding import GMMThresholding - + # Load your data adata = sc.read_h5ad("your_data.h5ad") @@ -33,7 +33,7 @@ Step-by-Step Guide # Initialize with default parameters gmm = GMMThresholding() - + # Or customize parameters gmm = GMMThresholding( gmm_kwargs={'n_components': 3, 'random_state': 42} @@ -54,7 +54,7 @@ Step-by-Step Guide # Create comprehensive visualization gmm.plot() - + # Or use specific plots gmm.plot_hist_distribution_with_boundaries() gmm.plot_bayesian_information_criterion_curve() @@ -66,10 +66,10 @@ Step-by-Step Guide # Get categorized cells categories = adata.obs["PCNA_categories"] - + # Get thresholds thresholds = gmm.return_thresholds() - + # Get the updated AnnData object adata_result = gmm.return_adata() diff --git a/docs/source/templates/api/module.rst.template b/docs/source/templates/api/module.rst.template index ca2f264..66a83b5 100644 --- a/docs/source/templates/api/module.rst.template +++ b/docs/source/templates/api/module.rst.template @@ -25,7 +25,7 @@ .. autosummary:: :toctree: generated :nosignatures: - + {% for item in section.autosummary %} {{ item }} {% endfor %} diff --git a/docs/source/tutorials/Sequential_Thresholding_Workflow.ipynb b/docs/source/tutorials/Sequential_Thresholding_Workflow.ipynb index 81fd694..1198414 100644 --- a/docs/source/tutorials/Sequential_Thresholding_Workflow.ipynb +++ b/docs/source/tutorials/Sequential_Thresholding_Workflow.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "db722da2", + "id": "0", "metadata": {}, "source": [ "# Sequential GMM Thresholding for Cell Cycle Staging\n", @@ -30,7 +30,7 @@ }, { "cell_type": "markdown", - "id": "0105d257", + "id": "1", "metadata": {}, "source": [ "---\n", @@ -41,7 +41,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0a34c75f", + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -49,9 +49,9 @@ "from urllib.request import urlretrieve\n", "\n", "import anndata as ad\n", - "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", "from cc_mapping.thresholding import SequentialGMM\n", "\n", @@ -60,8 +60,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "a4575792", + "execution_count": null, + "id": "3", "metadata": {}, "outputs": [], "source": [ @@ -77,19 +77,10 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "ae595e79", + "execution_count": null, + "id": "4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results directory created at: c:\\Users\\dap182\\Documents\\git\\cc_mapping\\notebooks\\results\n", - "Sequential results directory: c:\\Users\\dap182\\Documents\\git\\cc_mapping\\notebooks\\results\\sequential\n" - ] - } - ], + "outputs": [], "source": [ "# Create results directory structure for saving figures\n", "results_dir = cwd / \"results\"\n", @@ -105,43 +96,30 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "66f0c363", + "execution_count": null, + "id": "5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset: 6797 cells x 289 features\n", - "\n", - "Features we'll use:\n", - " - pRB (nuc median): Initial G0 vs cycling split\n", - " - pp21 (nuc median): Separate M phase\n", - " - Int_Intg_DNA_nuc: Split G1/S/G2\n" - ] - } - ], + "outputs": [], "source": [ "# Load and convert to AnnData\n", "csv = pd.read_csv(download_path, index_col=0, low_memory=False)\n", "csv.index = csv.index.astype(str)\n", "\n", "adata = ad.AnnData(\n", - " X=csv.values[:,:-10].astype(np.float32),\n", + " X=csv.values[:, :-10].astype(np.float32),\n", ")\n", "adata.var_names = csv.columns.values[:-10]\n", "\n", "print(f\"Dataset: {adata.n_obs} cells x {adata.n_vars} features\")\n", - "print(f\"\\nFeatures we'll use:\")\n", - "print(f\" - pRB (nuc median): Initial G0 vs cycling split\")\n", - "print(f\" - pp21 (nuc median): Separate M phase\")\n", - "print(f\" - Int_Intg_DNA_nuc: Split G1/S/G2\")" + "print(\"\\nFeatures we'll use:\")\n", + "print(\" - pRB (nuc median): Initial G0 vs cycling split\")\n", + "print(\" - pp21 (nuc median): Separate M phase\")\n", + "print(\" - Int_Intg_DNA_nuc: Split G1/S/G2\")" ] }, { "cell_type": "markdown", - "id": "26f88a2a", + "id": "6", "metadata": {}, "source": [ "### Filter Missing Values\n", @@ -151,26 +129,13 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "456d4158", + "execution_count": null, + "id": "7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cells with NaN values: 1 / 6797\n", - "After filtering: 6796 cells remaining\n" - ] - } - ], + "outputs": [], "source": [ "# Define the features we'll use for sequential thresholding\n", - "features_to_use = [\n", - " 'pRB (nuc median)', \n", - " 'pp21 (nuc median)', \n", - " 'Int_Intg_DNA_nuc'\n", - "]\n", + "features_to_use = [\"pRB (nuc median)\", \"pp21 (nuc median)\", \"Int_Intg_DNA_nuc\"]\n", "\n", "# Get feature indices\n", "feature_indices = [list(adata.var_names).index(f) for f in features_to_use]\n", @@ -190,7 +155,7 @@ }, { "cell_type": "markdown", - "id": "9f0b09b8", + "id": "8", "metadata": {}, "source": [ "---\n", @@ -202,32 +167,23 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "746af457", + "execution_count": null, + "id": "9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sequential thresholding object initialized!\n", - "Metadata will be stored in: adata.uns['sequential_gmm_thresholding']\n" - ] - } - ], + "outputs": [], "source": [ "# Initialize the sequential thresholding object\n", "gmm_kwargs = {\n", - " 'init_params': 'k-means++', \n", - " 'n_init': 10, \n", - " 'max_iter': 1000, \n", - " 'random_state': 42\n", + " \"init_params\": \"k-means++\",\n", + " \"n_init\": 10,\n", + " \"max_iter\": 1000,\n", + " \"random_state\": 42,\n", "}\n", "\n", "seq_gmm = SequentialGMM(\n", " adata=adata,\n", - " thresholding_events_key='sequential_gmm_thresholding',\n", - " gmm_kwargs=gmm_kwargs\n", + " thresholding_events_key=\"sequential_gmm_thresholding\",\n", + " gmm_kwargs=gmm_kwargs,\n", ")\n", "\n", "print(\"Sequential thresholding object initialized!\")\n", @@ -236,7 +192,7 @@ }, { "cell_type": "markdown", - "id": "b5109009", + "id": "10", "metadata": {}, "source": [ "### Exploratory Visualization\n", @@ -246,88 +202,53 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "448c420f", + "execution_count": null, + "id": "11", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ajKDt5trfRDtz6vLrZ9PqZu3UqTuyF154oQil+v9NNa6Lj/nuaPVgWJShvdqnQfuf6PBLbQt/8803Tdnq8wOh/X10Pem60JoSz529BiZX7ZeWvR4s9f+BXgRLw4u+hh589MCu153RM209CLnWR0HP1eu46AFJ+yBpeWvZ6GfT2iXP9aXDT3X96PrUg7qefRdHIGWpoVSHpep8WnYaVHU+rT3TGh9PgS6b1o5pfwZd79oBW8Oaa2iv1siMHj1agkWvq6HbnNZMuZpdNGRo2NCmTH+0f4V+Pg3P51OL59p36HdPv0NaS+N5BVbtXKrLouWltCx0aK82pWhfL11eLSutYdXtSMvH8yJ+BQl0e1L6Pdd1zLDeUqgER+6glA/tdQ2X0+GS8fHxZuif51BVpUN1db758+d7DWV8++23zfBWHcZYuXJlp1evXs6+ffu8nus7tFcdP37cGT16tNOwYUOnUqVKTrNmzcwQPR16GOhw0pSUFPM8HVrbsmVL8xn8DdPcsWOHGaqry6ePeQ7z3bx5s5OYmOhUrVrVueCCC5wuXbo4a9eu9Vt2GzduzLd8c3NznVq1ajk1atTwGspZnKG9WobLli1z2rZt6/5sCxcu9JrPVf6+0106d+7sVK9e3b0e/Q3t1ce7devmrFixwgmE53N1uHXNmjXN8FMd1ulv6LXvUMyjR4+adamfR4dba1m1b9/eDAf3lJGRYcpAh1jr813DOwtaD/kN7Q2kLJUOb9Zl0eGfsbGxzssvv+z3NfNbNt+hvS76fdEy0veuXbu2M2jQIOfgwYNe8+i60fLwld+QY3/08w0bNsx9v3fv3k5MTIxz8uTJfJ8zZMgQ893T9VLcob2+ZXjnnXe6v9P6fdDt66233nLOnDnjNe+MGTPMsGX9TmpZXnnllc4TTzxhhqgHOrQ30O1J31uHcz/99NMBfQ6UrAr6j+1AhLJLz3i0dkPPZgI9kynPtNZBa1201iclJaXYr6NnzTq0WkcKAYHS4dwjRowwNTmeF1PD/1+hVmtP9arE2tEdpQt9RoAg7/B0uLA21wAlTa9CrM0Q2lwCb9pcpn1kCCKlE31GgCBYv3696aOibfbaHq1t4kBJ09FfNn+MrjTT34dC6UXNCBAE2plRRxppR0YdpQEACBx9RgAAgFXUjAAAAKsIIwAAwKpS14FVL7ykV0vUq+fxY0YAAJQNeqUQvcidXt4gvx/qLDNhRINIoL8FAgAAShf9fSL98c0yHUZcP66lHybQ3wQBAAB26U9DaGVCYT+SWSbCiKtpRoMIYQQAgLKlOF0s6MAKAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrIu2+PYBwMzZ1Y0DzTRgYH/JlAVA6UDMCAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArOIKrACCctVUrpgKoLgII0AYC/TS7AAQSjTTAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCK64wACAquWQKguKgZAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFVcDh4oh7g0O4CyhJoRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAJSdMPLMM89IhQoVvG4tW7Z0P37q1CkZMWKE1KlTR6pWrSr9+/eXzMzMUCw3AAAI15qR1q1by+HDh923zz//3P3Y6NGjZfHixbJw4UJZvXq1HDp0SPr16xfsZQYAAOF80bPIyEipX7/+OdOzs7MlJSVF5s2bJ127djXTZs2aJa1atZJ169ZJhw4dgrPEAAAgvGtGdu3aJQ0bNpTLLrtMBg0aJPv37zfT09PT5fTp09K9e3f3vNqEExsbK2lpafm+Xm5uruTk5HjdAABA+ChSzUj79u1l9uzZ0qJFC9NEM378eLnhhhtk27ZtkpGRIVFRUVKzZk2v59SrV888lp+JEyea1/H13L/TJfqCqgUuz4SB8UVZfAAAUNbDSM+ePd3/b9u2rQknTZo0kQULFkjlypWLtQBJSUkyZswY932tGWncuHGxXgsAAITZ0F6tBWnevLns3r3b9CPJy8uTrKwsr3l0NI2/PiYu0dHRUr16da8bAAAIH+cVRk6cOCF79uyRBg0aSFxcnFSqVElWrlzpfnznzp2mT0lCQkIwlhUAAIR7M81jjz0mvXv3Nk0zOmx33LhxUrFiRbnjjjukRo0aMmzYMNPkUrt2bVPDMXLkSBNEGEkDAACCEkYOHjxogsexY8fkoosuko4dO5phu/p/NXnyZImIiDAXO9NRMomJiTJ16tSivAUAAAgzRQojqampBT4eExMjycnJ5gYAABAIfpsGAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgVaTdtwcA/8ambgyoaCYMjKcIgTKOmhEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWMXQXgBlGkOAgbKPmhEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAZTeMTJo0SSpUqCCjRo1yTzt16pSMGDFC6tSpI1WrVpX+/ftLZmZmMJYVAACUQ8UOIxs3bpQ33nhD2rZt6zV99OjRsnjxYlm4cKGsXr1aDh06JP369QvGsgIAgHKoWGHkxIkTMmjQIJk5c6bUqlXLPT07O1tSUlLk5Zdflq5du0pcXJzMmjVL1q5dK+vWrQvmcgMAgHAOI9oM06tXL+nevbvX9PT0dDl9+rTX9JYtW0psbKykpaX5fa3c3FzJycnxugEAgPARWdQnpKamyubNm00zja+MjAyJioqSmjVrek2vV6+eecyfiRMnyvjx44u6GAAAIBxrRg4cOCCPPPKIzJ07V2JiYoKyAElJSaZ5x3XT9wAAAOGjSGFEm2GOHDki11xzjURGRpqbdlJ99dVXzf+1BiQvL0+ysrK8nqejaerXr+/3NaOjo6V69epeNwAAED6K1EzTrVs32bp1q9e0oUOHmn4hTz75pDRu3FgqVaokK1euNEN61c6dO2X//v2SkJAQ3CUHAADhF0aqVasmbdq08ZpWpUoVc00R1/Rhw4bJmDFjpHbt2qaWY+TIkSaIdOjQIbhLDoShsann9tUCgLDrwFqYyZMnS0REhKkZ0ZEyiYmJMnXq1GC/DQCEJMhNGBhPyQJlLYysWrXK6752bE1OTjY3AACAwvDbNAAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrIu2+PQA1NnUjBQEgbFEzAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArOJXewGgGL+gPGFgPOUGBAk1IwAAwCrCCAAAsIowAgAArCKMAACAshNGpk2bJm3btpXq1aubW0JCgnz00Ufux0+dOiUjRoyQOnXqSNWqVaV///6SmZkZiuUGAADhGEYaNWokkyZNkvT0dNm0aZN07dpV+vTpI9u3bzePjx49WhYvXiwLFy6U1atXy6FDh6Rfv36hWnYAABBuQ3t79+7tdf+5554ztSXr1q0zQSUlJUXmzZtnQoqaNWuWtGrVyjzeoUOH4C45AAAI7z4jZ86ckdTUVDl58qRprtHaktOnT0v37t3d87Rs2VJiY2MlLS0t39fJzc2VnJwcrxsAAAgfRQ4jW7duNf1BoqOj5cEHH5T33ntPrrjiCsnIyJCoqCipWbOm1/z16tUzj+Vn4sSJUqNGDfetcePGxfskAAAgPMJIixYtZMuWLbJ+/XoZPny4DB48WL755ptiL0BSUpJkZ2e7bwcOHCj2awEAgDC4HLzWfjRt2tT8Py4uTjZu3ChTpkyRAQMGSF5enmRlZXnVjuhomvr16+f7elrDojcAABCezvs6I2fPnjX9PjSYVKpUSVauXOl+bOfOnbJ//37TpwQAAOC8a0a0SaVnz56mU+rx48fNyJlVq1bJsmXLTH+PYcOGyZgxY6R27drmOiQjR440QYSRNAAAIChh5MiRI3LPPffI4cOHTfjQC6BpELnpppvM45MnT5aIiAhzsTOtLUlMTJSpU6cW5S0AAECYKVIY0euIFCQmJkaSk5PNDQAAIBD8Ng0AALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrIu2+PQCUTWNTNwY034SB8SFfFqCso2YEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFVcZwQoBdeiAIBwRs0IAACwijACAACsIowAAACr6DMCFAN9QQAgeKgZAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAACUnTAyceJEiY+Pl2rVqkndunWlb9++snPnTq95Tp06JSNGjJA6depI1apVpX///pKZmRns5QYAAOEYRlavXm2Cxrp162T58uVy+vRp6dGjh5w8edI9z+jRo2Xx4sWycOFCM/+hQ4ekX79+oVh2AABQDkQWZealS5d63Z89e7apIUlPT5cbb7xRsrOzJSUlRebNmyddu3Y188yaNUtatWplAkyHDh2Cu/QAACC8+4xo+FC1a9c2fzWUaG1J9+7d3fO0bNlSYmNjJS0tze9r5ObmSk5OjtcNAACEj2KHkbNnz8qoUaPk+uuvlzZt2phpGRkZEhUVJTVr1vSat169euax/Pqh1KhRw31r3LhxcRcJAACEUxjRviPbtm2T1NTU81qApKQkU8Piuh04cOC8Xg8AAJTjPiMuDz30kCxZskQ+++wzadSokXt6/fr1JS8vT7KysrxqR3Q0jT7mT3R0tLkBAIDwVKSaEcdxTBB577335JNPPpFLL73U6/G4uDipVKmSrFy50j1Nh/7u379fEhISgrfUAAAgPGtGtGlGR8q8//775lojrn4g2tejcuXK5u+wYcNkzJgxplNr9erVZeTIkSaIMJIGAACcdxiZNm2a+du5c2ev6Tp8d8iQIeb/kydPloiICHOxMx0pk5iYKFOnTi3K2wDWjE3dSOkDQGkOI9pMU5iYmBhJTk42NwAAgMLw2zQAAMAqwggAALCKMAIAAKwijAAAgLJ30TMAQHBHaE0YGE+RImxRMwIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKH8pDWAj0x8oAACWPmhEAAGBVma4Z4ae5AQAo+6gZAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGBVmb7OCACUF1w3CeGMmhEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYFWn37YHzMzZ1I0UIAOFWM/LZZ59J7969pWHDhlKhQgVZtGiR1+OO48jYsWOlQYMGUrlyZenevbvs2rUrmMsMAADCOYycPHlS2rVrJ8nJyX4ff+GFF+TVV1+V6dOny/r166VKlSqSmJgop06dCsbyAgCAcG+m6dmzp7n5o7Uir7zyijz99NPSp08fM23OnDlSr149U4MycODA819iAAhjgTZNThgYH/JlAUplB9a9e/dKRkaGaZpxqVGjhrRv317S0tL8Pic3N1dycnK8bgAAIHwEtQOrBhGlNSGe9L7rMV8TJ06U8ePHB3MxUIpxVgcAKHVDe5OSkiQ7O9t9O3DggO1FAgAAZbVmpH79+uZvZmamGU3jovevuuoqv8+Jjo42N8ATQ3YBIHwEtWbk0ksvNYFk5cqV7mnaB0RH1SQkJATzrQAAQLjWjJw4cUJ2797t1Wl1y5YtUrt2bYmNjZVRo0bJs88+K82aNTPh5C9/+Yu5Jknfvn2DvewAACAcw8imTZukS5cu7vtjxowxfwcPHiyzZ8+WJ554wlyL5P7775esrCzp2LGjLF26VGJiYoK75AAAIDzDSOfOnc31RPKjV2WdMGGCuQEAAJT60TQAACC8hcUP5XFtCwDhhv0eyhJqRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFaFxQ/lofT8KBcAAL4IIwAQxgI5kZgwML5ElgXhi2YaAABgFWEEAABYRTMNCkV/EABAKBFGwhghAwBQGtBMAwAArKJmBAAQlFpURt2guKgZAQAAVhFGAACAVYQRAABgFX1GioH2UwAAgoeaEQAAYBU1I+UQ1w8BAJQlhJEQHsRpzgEAoHA00wAAAKsIIwAAwCqaaQAAQUHTNIqLmhEAAGAVNSOlAGcTAIBwRs0IAACwipqRMoTrhwAAyiNqRgAAgFWEEQAAYBXNNACAEkWnffiiZgQAAFhFGAEAAFYRRgAAgFWEEQAAYBUdWAEAZRodYss+akYAAIBVhBEAAGAVYQQAAFhFnxEAQKnE73GFD8IIAAAe6BBb8mimAQAAVlEzAgAICzT7hGEYSU5OlhdffFEyMjKkXbt28tprr8l1110XqrcDAKBMh5sJA+MlXIUkjMyfP1/GjBkj06dPl/bt28srr7wiiYmJsnPnTqlbt24o3hIAgLAwNsAQVJbCTQXHcZxgv6gGkPj4eHn99dfN/bNnz0rjxo1l5MiR8tRTTxX43JycHKlRo4Y8kfKJRF9QNdiLBgBAWJhQwmHEdfzOzs6W6tWr260ZycvLk/T0dElKSnJPi4iIkO7du0taWto58+fm5pqbi34IM/2Xk8FeNAAAwkZOTk5A8z337/SgvJ/ruF2cOo6gh5GjR4/KmTNnpF69el7T9f6OHTvOmX/ixIkyfvz4c6ZPeah3sBcNAICw8cIwO+97/PhxU0NSpkbTaA2K9i9x0SadH3/8UerUqSMVKlSwumylKd1qM9eBAweKXPUFyjJU2C4pz9KKbdNOWWqNiAaRhg0bFvl9gh5GLrzwQqlYsaJkZmZ6Tdf79evXP2f+6Ohoc/NUs2bNYC9WuaAbAmGEsixt2C4pz9KKbbPky7KoNSIhu+hZVFSUxMXFycqVK71qO/R+QkJCsN8OAACUcSFpptFml8GDB8u1115rri2iQ3tPnjwpQ4cODcXbAQCAMiwkYWTAgAHyww8/yNixY81Fz6666ipZunTpOZ1aERhtxho3btw5zVkoOsoyeCjL4KI8Kctw3i5Dcp0RAACAQPFDeQAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsKIJcnJyXLJJZdITEyM+ZXjDRs2FDj/woULpWXLlmb+K6+8Uj788EOvx999913p0aOH+zL6W7ZskXARzLI8ffq0PPnkk2Z6lSpVzGWN77nnHjl06JCEg2Bvl88884x5XMuyVq1a5gcz169fL+Eg2GXp6cEHHzTfc72GUzgIdlkOGTLElJ/n7eabb5ZwkByC7fLbb7+VW265xVx9Vb/r8fHxsn///qItmA7tRclKTU11oqKinDfffNPZvn27c9999zk1a9Z0MjMz/c7/xRdfOBUrVnReeOEF55tvvnGefvppp1KlSs7WrVvd88yZM8cZP368M3PmTB2q7Xz55ZdOOAh2WWZlZTndu3d35s+f7+zYscNJS0tzrrvuOicuLs4p70KxXc6dO9dZvny5s2fPHmfbtm3OsGHDnOrVqztHjhxxyrNQlKXLu+++67Rr185p2LChM3nyZKe8C0VZDh482Ln55pudw4cPu28//vijU96lhqAsd+/e7dSuXdt5/PHHnc2bN5v777//fr6vmR/CiAV6cBsxYoT7/pkzZ8yOZeLEiX7nv/32251evXp5TWvfvr3zwAMPnDPv3r17wyqMhLIsXTZs2GDKdN++fU55VhJlmZ2dbcpyxYoVTnkWqrI8ePCgc/HFF5tg16RJk7AII6EoSw0jffr0ccLNdSEoywEDBjh33XXXeS8bzTQlLC8vT9LT0011tUtERIS5n5aW5vc5Ot1zfpWYmJjv/OGipMoyOzvbVOOW5x9wLImy1PeYMWOGqcpt166dlFehKkv9ja+7775bHn/8cWndurWEg1Bul6tWrZK6detKixYtZPjw4XLs2DEpz/JCUJa6TX7wwQfSvHlzM13LU5t+Fi1aVOTlI4yUsKNHj8qZM2fOuTS+3tdL5/uj04syf7goibI8deqU6UNyxx13lOtfTA5lWS5ZskSqVq1q2pwnT54sy5cvN7/uXV6Fqiyff/55iYyMlIcffljCRajKUvuHzJkzx/yAq5br6tWrpWfPnua9yqujISjLI0eOyIkTJ2TSpEmmTD/++GO59dZbpV+/fqZMrf82DVAeaGfW22+/XZsyZdq0abYXp8zq0qWL6VCtO8OZM2eaMtVOrHoWhcDoGe2UKVNk8+bNppYO52fgwIHu/2unzLZt28rll19uaku6detG8QZIa0ZUnz59ZPTo0eb/+lt0a9eulenTp0unTp0CfSlqRkqanhFWrFhRMjMzvabr/fr16/t9jk4vyvzhIpRl6Qoi+/btM2fy5blWJNRlqb3rmzZtKh06dJCUlBRzdq9/y6tQlOWaNWvMWWhsbKwpP73ptvnoo4+akRHlVUntLy+77DLzXrt375by6sIQlKW+pm6LV1xxhdc8rVq1KvJoGpppSlhUVJTExcWZ6kHPdKn3ExIS/D5Hp3vOr/QAmd/84SJUZekKIrt27ZIVK1aY4dLlXUlul/q6ubm5Ul6Foiy1r8jXX39taphcNx12rv1Hli1bJuVVSW2XBw8eNH1GGjRoIOVVVAjKUl9Th/Hu3LnTa57vvvtOmjRpUrQFPO8usCjW8Kro6Ghn9uzZZrjU/fffb4ZXZWRkmMfvvvtu56mnnvIaXhUZGem89NJLzrfffuuMGzfunOFVx44dMyNoPvjgAzNaQd9D7+uQtfIs2GWZl5fn3HLLLU6jRo2cLVu2eA39y83NdcqzYJfliRMnnKSkJDM8+vvvv3c2bdrkDB061LyHjgYpz0LxHfcVLqNpgl2Wx48fdx577DGzXeroQx3Zdc011zjNmjVzTp065ZRnqSHYLnWouU6bMWOGs2vXLue1114zw4HXrFlTpGUjjFiiKyw2NtaM+dbhVuvWrXM/1qlTJzP0zNOCBQuc5s2bm/lbt25tQoenWbNmmRDie9ONp7wLZlm6hkb7u3366adOeRfMsvzll1+cW2+91Qwd1McbNGhggp4OlQ4Hwf6Oh2sYCXZZ/vzzz06PHj2ciy66yBxEtRz1ehuuA3J591oItsuUlBSnadOmTkxMjLkGzqJFi4q8XBX0n6LVpQAAAAQPfUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAACITf8H8VPMVQ0edKcAAAAASUVORK5CYII=", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Explore pRB distribution across all cells\n", "seq_gmm.plot_feature_distribution_exploratory(\n", - " feature='pRB (nuc median)',\n", - " hist_kwargs={'bins': 50, 'color': 'steelblue', 'alpha': 0.7},\n", + " feature=\"pRB (nuc median)\",\n", + " hist_kwargs={\"bins\": 50, \"color\": \"steelblue\", \"alpha\": 0.7},\n", ")\n", - "plt.title('Exploratory: pRB Distribution (All Cells)')\n", + "plt.title(\"Exploratory: pRB Distribution (All Cells)\")\n", "plt.show()\n", "\n", "# Or use the strip plot version for better visualization\n", "fig, (ax_strip, ax_hist) = seq_gmm.plot_feature_strip_plot_exploratory(\n", - " feature='pRB (nuc median)',\n", - " hist_kwargs={'bins': 50, 'color': 'steelblue'},\n", - " scatter_density=True\n", + " feature=\"pRB (nuc median)\",\n", + " hist_kwargs={\"bins\": 50, \"color\": \"steelblue\"},\n", + " scatter_density=True,\n", ")\n", - "plt.suptitle('Exploratory: pRB Distribution - Looking for 2 components')\n", + "plt.suptitle(\"Exploratory: pRB Distribution - Looking for 2 components\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "f3c4aef7", + "execution_count": null, + "id": "12", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial thresholding complete!\n", - "\n", - "Label distribution:\n", - "cell_cycle_phase\n", - "G1/S/G2/M 6101\n", - "G0 695\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "# Perform initial thresholding: G0 vs G1/S/G2/M\n", "seq_gmm.threshold_entire_dataset(\n", - " feature='pRB (nuc median)',\n", - " label_obs_save_str='cell_cycle_phase', # This column will store our labels\n", + " feature=\"pRB (nuc median)\",\n", + " label_obs_save_str=\"cell_cycle_phase\", # This column will store our labels\n", " n_components=2,\n", - " ordered_labels=['G0', 'G1/S/G2/M'],\n", - " operation_name='initial_pRB_split'\n", + " ordered_labels=[\"G0\", \"G1/S/G2/M\"],\n", + " operation_name=\"initial_pRB_split\",\n", ")\n", "\n", "print(\"Initial thresholding complete!\")\n", - "print(f\"\\nLabel distribution:\")\n", - "print(seq_gmm.adata.obs['cell_cycle_phase'].value_counts())" + "print(\"\\nLabel distribution:\")\n", + "print(seq_gmm.adata.obs[\"cell_cycle_phase\"].value_counts())" ] }, { "cell_type": "markdown", - "id": "dd488677", + "id": "13", "metadata": {}, "source": [ "### Visualize Results" @@ -335,47 +256,29 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "4aa28551", + "execution_count": null, + "id": "14", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Figure saved to: c:\\Users\\dap182\\Documents\\git\\cc_mapping\\notebooks\\results\\sequential\\step1_pRB_split.png\n" - ] - }, - { - "data": { - "image/png": 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DY3b//v2ZtpfvRkIaRHM0zPC+83nGdymZZUSTE40lq/5l9Z5oiiKMxCohQj27pAdyTHmY852n80WEt5g7ZU4vP9eiIdDlwS47bUuEzkcffZT+WjLEyGsZF19rgS8yPysPOGJ1Ee0+N8TbUubj5Bx8w3hk/k+sNiL0CTOe2AbjRyJPfPKjlx+caAC+cwDGk75kfJAUVhKzI5P7coPIyRyUE+LeLj8ceZI2bsJiJhHBJaa9hx9+OMP28gQpmS1ESIhzitzkjx07pibM84M8NYtGINqQ7FOOJbFIshghBGLay835RLQIuanJ/IWYiYyMJ3ITkidtGRvRqMw3PZkvk/kfuVlLzKGMt5iV5HweeeSRPPVf3Lvlb2WRvos2bUa0E7lZde7cWc3J7du3T6XpkocBObecEDO0mD5FOxQTp2GiNDKeiHCQ983I/I8RKynCTOZWxZFEEHOpfLeGqdFXSMnfiRYk+5TxlPOR70BMf/JQIxlj5OFKtAiZszPCP8yIcJPPZG5NHChkDlHOW8xrcj3Jg5jcsA3nHhkP6Zdo9PLQYZh+DaS/ssh8lph68xKTJkIyP9qSL5LaTX6DMkbiUGVGTNjyW5H5N7le5boVgSUPI3L95MZDDz2kxlFCSUTj9bXOiIlSHgRkvMVKI+Mm52y2eMj3KlYLI60ZYQhB0EMIDHfmrNyEzWTlOv+f//zHU61aNZXpwuwqnVUIwSOPPKLc9sWN+7rrrvOsWrVKuTkbGSzyE0IgGUNGjRrlueKKKzylS5f2hIeHe2rWrOm58847Pbt3786wrfSlR48enkWLFnmaNWumwhYaNmyYyTU6LyEEwsqVK1V4RURERIYxyWsIgZnff//dM3z4cNV32Z98F9JHGStxD/flwIEDyu1c3PzF9b9nz55ZZtnICRl76eddd92V6bMZM2aoTBziYi79kX7985//9Bw5ciRP+5aQgbfeesvTtm1b1UfJSiNu8dLPr776KlNoghHCktViXAOPPvqoctHPKrONZCOR60euKzmWZNbo3LmzOg+D9957T10jvu79Zr777jvPzTff7KlYsaLaj7jDt2vXTrnwm8MijGsku8W4Fn744QdPkSJFssy+k91vzkxeQwiMcZA+//e//83wvoxLkyZNPOvXr1ffR1RUlLqWZTzycyz5TUlWHeN6zCqEQM5HjvHtt99m+nsjc0p+r1MnU0T+o7Qn/kLmHkTTknyGxH6I5iyWAN+kynlFHD9E48gtK4w/Ea1FTIiSxzEYiJVCLABixjeQeV8xSVtdAUA0e9HszNMFbocJmgkhCjHfyrxhVlk08orc7I0UVcFCzLtiug8WEuMm5kgJESlIJYJAIaZgebjMy5yem6AmR/wKNTniRkJFkyOZoXclIYQQx0JNjhBCiGOhJkcIIcSxUMgRQghxLCHnXSkpfyTYUVLcMMEoIYS4F4/Ho1KwSaB9dtlubCfkRMBJotm80BajEYkYJCEeqzABwSQK4ViA/1Pt7ngRicic9oqQrBgt5Yck3ZkUweUQEZIrUrQ5r0WPQ97xRErBSB639fMOILpkxrQ5vswZGI2LJ8JQvEIa+szInJMwkKRdTMGx7u+pdvMF7VG0eOCrCxBnED1wIMJOnEBahQpImDHD6u4QErLEnz+PGj16qBypBc3FGXKanGGiFAFXKjpnIVe1FZB4BogqE5brtv4mrWgyjuntmOiSKFo85IaShCpSveHMGYSVKYOYLIq5EkIyUpipK1vfmTtruWUJsRd6UmRCSOChdyUhhBDHQiFHCCHEsdjaXEkIcTapUiPN6k6QgCFV9gLtsmdrIffDvcDFU0DxckDP3OslEhIa3HuvlHYGypUD8lDo042Iy/dRqTwusVHidFAIxwMSoohjv8eDMmlpkHLFgfqGbS3k4vcD5+OAlOBGDxBSOPbvB+LigAReuNmhBFx4OCpVqIASkZFMDOFAPB4PLiQlIe7ECSlpjioBOo6thRwhxJkmStHgRMCVL2BsFLEHxaOi1Dru2DFUSksLiOmSjieEkJBCzcEVKaI0OOJ8Ssj3XKRIwOZeKeQIIaFHkSI0Ubop0LtI4OZcKeQIIYQ4Fs7JEUJsw9Fjx1Qew2AgOXQrx8YG5VgkcFDIEUJsI+B69x2GhDNJQTledJlIfD97Sr4FnfRz/OuvY96iRTh46BBKx8SgXt26GDZ4MO4YOhQlSpRAYmIiHhk7FtNmzkRSUhK63XAD3p8wAbEUqn6HQo4QYgtEgxMB1yX8OVSIrB3QY51I2oOfzoxTx8yPkPt7zx5c16ULypQujZfGjUPTJk0QGRmJzX/+iY8nT0a1qlXRu0cPjH7iCSUEp3/5pcqu/69HHkH/oUPx208/BfS83AiFHCHEVoiAqxzVMPAHKoC73/2jR6NYsWJYv2IFSpYsmf5+ndq10adnTxUbJuXEJn35JaZ+9hk6d+yoPp/8wQdo1LIlVq9di2tat/bnWbgeWwu5q+4CUi4A4SWs7gkh+eCuu4ALF4ASvHCdxMmTJ/HjkiV46dlnMwg4X0/CDb//jpSUFHTp1Cn9/YYNGqBmjRpYRSHnd2wt5Br1t7oHhBSA/rxwnciuv/9WmlqD+vUzvF+hZk0kJmnziKPuuQfNmzZFRESEcmwxE1upkprPI/7F1kKOECdTRIqr5oBn/fqg9YUUnLXLlyMtLQ1DR45UTiYkuFDIEUKIH6hXp44yR27fuTPD+zIfZ05hVblSJSQnJyunFrM2dywujiELAcDWweAXTgAJx7Q1IbZBEtKKWUrWxDGUL18eXTt3xnsffYTz589nu13Lq65CeHg4lixblv7e9h07sP/AAbSl04nfsbUmN3u4VoWgZCXgtvlW94aQPDJ8uFaFoFIlYD4vXCchsW4SQtCqfXs8+9RTaHbFFQgLC8O6jRuxbccOJeAkZGDk8OEYM3YsypUti5iYGDzw6KNo26YNPSsDgK2FHCHEfUgMW6geo26dOvh95Uq89NprGPvssyoYXOLkGjdsiEcfegj333232m7CK68o4Tdg2DBvMPhbb/n5LIhAIUcIsQUyfyVZSCRIOxjlwuVYvh6QeaFK5cp494031JIdUVFRmDhhglpIYKGQI4TYAsk8Imm2mLuS5AcKOUKIrQQdkyYT13hXEkIIITlBTY6QIAd0HwBQnaNOSFCgJkcIIcSxUJMjJICptwgh1kJNjhBCiGOxtSZ38/tAWioQVtTqnhCSd27Qf3h/vv8+h42QAGNrIVfmMqt7QEj+2WE0LuMFTEigsbWQI4S4C6m3xmBwkh8o5AghthFwA/v2ReKZM0E5XlSZMpgxe3a+g8+ln+Nffx3zFi1SuStLx8SgXt26GDZ4MO4YOhQlSpTAx599hqnffouNf/yBc+fO4fTBg1mmELt48SIq1KqFP1atQu3LLsNrb72Fz6dMwb4DB1C8eHHUr1sXd995J+66884Mfzfi3ntRrUoVvDBunHq9dPlyvPHuu1izbh3OJSSgWtWqaHXVVaqI6/Xt2qltlv3yCyZMnIi169cj/tw5te/HHn4YQwcPztSv5156CTt378aUSZNwWePG2Ld/P76ePBm33nJLhu2atGqFv7Ztw+QPP8Sdw4bBCmwt5HYtBC4lAsWigHo3Wd0bQvLGEAAlpLFwIXATL9y8IhqcCLj/hIejdmRkQC+3PUlJ+PeZM+qY+RFyf+/Zo6oQlCldGi+NG4emTZqoBM2b//wTH0+erIRL7x49cOHiRdzUtataxuqCKCsW//wzatWooYTkM//5Dz767DO898YbaNWihRJE6zduxGkfoZ+amoofFizAvJkz1ev3P/4Y/3rkEdw+ZAi++eILlUT6bHy8Enyjn3wSG379VW23cs0aNGvSBE+MHq2qlMs+ht99txLSPbt3z3CMOfPm4ckxY9Jf16heHZOnTMkg5FavXYujcXEoWbIkrMTWQm7tO95SOxRyxC68agSDv/MOhVwBEAHXUC9AGlBS8p8F+v7Ro1GsWDGsX7Eiw81dCqf26dkTHo9HvX541Kh07SknRJiIUBS+nz9fVTG4pX//9M+bN22a6W9Wrl6t6tVd3bKlqlH38BNPqOO9+fLLGbaTMkAP3n9/+uunHnssw+cPjRqFH3/+GbO+/z6DkDtw8CD+3LpVCWgD0fYmvPee+kwEnvDZl19i6KBB+PLrr2ElDCEghBA/cPLkSfy4ZIkyAWanvUjl8LySlpamtKk+upATjfLn5ctx/PjxHP9OhGGv7t3VsWbOmYOUlBQ8Pnp0gfojGp/UvMuw/3nz0LF9e1UHz0A0v25duuCLr75Sry9cuIBvZs3CP6R2osVQyBFiEQfj4lQweXYLsRe7/v5baWoN6tfP8H6FmjURHRurlif+/e8870/MfUKbq69W6zfHj8fxEydQuW5dNGvTBvc++CAW/Phjjtrfjp07lTAym1xnzp6d3h9ZNm/ZkuXxv505E+s2bMAIn7k0tf+bb860/T9uvx2ff/WVGgOZy6xbuzaubNYMVkMhRwghAWTt8uXYtHIlmjRqpAqk5hURJmImlOKqQuNGjbBl3TqsXrpUaUhxx4+j1y234C7d9Cls3bYNh48cwQ0dO2arrXXr0kX1Z96MGTh//jxS09IyHVvm60bcdx8+ee89NGncOP39+Ph4LP/113QhaqbHTTchISEBv/z6qzJVitALBSjkCCHED9SrU0cJlO07d2Z4X+bjxHGkeD7nEb/PQmMSgSdzbTLHNuvrr/H5Rx9h0hdfYM/evdrfzJ+Prp06qaKsQv169XD27Fnl8WkQHR2t+lOrZs0sj7t8xQr0GjQIE15+GcNvuy3DZ6I5SpVzY97NjMxFinPLuBdfxJr167P0yrQCCjlCCPED5cuXR9fOnfHeRx8pDakw7Ny1S4UJyP5yQgSOYBxPtD9xcDEY2LevckJ55c0383RccYTpMXAgXnn+edzzj39k+lztPwstzkC0N9H0ZJuyPnN5VmFr70pCCAkl3p8wQYUQtGrfHs8+9ZTyYBTta93Gjdi2YwdaXnWV2k40K1lkHk+QEINSpUqhZvXqKFeunBImXTp1UjF1BgOHDsV1bdvi2jZt1BybaG9jn30Wl9evj4YNGiAuLk6FFHz/zTfpf1OzRg288dJLeOjxx3Hq9GncOXSoireT9pRp09Q2RXVzqJgoe95yCx66/34M6Ns3XfuLCA9Xfbp06RIWLF6MRx96KNvzb9SwIU7s25eh31ZDIUcIsRUSwxaqx5AYtN9XrsRLr72mBJAEg0ucnGhcIhwkBED48NNP8dz48el/d323bmptBE3P+eEHFTjuO5f29fTpKtBcvB5F0HXu0EEJUzEVzl2wAK1btUKFChUy/N0D992nhM+b776LgbffrubVypcrh7atW2Ph7NloesUVarsvpk5VXpGyf1kMOrRrh2ULFyozZnTJkmhx5ZW5arShRBGPEbgRIsgXULp0aWxbdhalor0uqlkx9WZvnNxt8xFU0i4mY1d77SJtv+ImFC3O5wU3UhAvSKNo6kEJoi3EsT3r18OJJIqQKVYMtWvUQJQp6NsuGU8Ky4kTJ1ClXj0c3L4dsfk4du9Bg9CubdtswwUKy4OPPqq0ufffegv+JDEpCXsOHEDtS5fgO2sZn5CA0h07qnlFc8hCfrD1nbl4+YxrQuzAUZ81yRsibEToOD13pZgSJVwgPwJOEAE3xCetlj+5onFjpf3ZDVsLuX7/tboHhOQfLeqJFAQROlYInmAic2yy5JdAaXAGWTmi2AF6VxJCCHEsFHKEEEIci63NlYQEGqbXIsTe2FrIrXgRSIoHImOA9v9ndW8IyRsfAignDgYA7uWgZY3Hg7TQcvwmAUJ9zwH8rm0t5A785g0hIMQu9DCFEJDMRMg8SloaDsfFoWK5cioYOe+5+4ld8ABITknB8VOn1Pct33sgsLWQI4Q401GgtseDIxcu4LARlJ2PEjXEJng07a1EaipqBtBBhEKOEBJyyFO93PgupaYi1erOkIBRVBdCgXyEoZAjhIQkcuML1xdCCkq+NMRnn31WlZIwLw31LNhCYmIiRo0apXKXSTmHAQMG4JipxAMhhBASTPJtBm3SpAmOHDmSvvz666/pn40ePRpz587F9OnTsXz5chw+fBj9+/f3d58JIYSQwJgrJdt15cqVM70vCTQnTZqEqVOnorNeA2ny5Mlo1KgRVq9ejWuuuSa/hyKEEEKCq8nt3LkTVatWRZ06dTB06FDs379fvb9hwwakpKSgS5cu6duKKbNmzZpYtWpV4XpJCCGEBFqTa9OmDT7//HM0aNBAmSqfe+45tG/fHlu2bMHRo0cRERGhMnebkUza8ll2JCUlqcVcaocQQggJupDr3r17ertZs2ZK6NWqVQvffvstihcvXqAOjB8/XglLXxp2LJ3t3xxar8VX1O3mzXhCiF34GkBZAKet7gghLqBQIQSitV1++eXYtWsXunbtiuTkZFXryazNiXdlVnN4BmPHjsWYMWMyaHI1auStlGSb7KuwExKyPG51BwhxEYUKMk9ISMDu3btRpUoVtGzZEuHh4ViyZEn659u3b1dzdm3bts12H1IaXiq+mhdCCCEk6Jrco48+il69eikTpYQHjBs3DkWLFsWQIUNQunRpjBw5Umll5cqVU8LqgQceUAKOnpWEEEJCXsgdPHhQCbSTJ0+iYsWKaNeunQoPkLYwYcIEhIWFqSBwcSbp1q0b3n///UD1nRBCCPGfkJs2bVqOn0dFRWHixIlqCQbfDgAunABKVAAGzQzKIQkpNFsBVAVwGEAjjichAcXWlcEvXQRSzmtrQuxCNIAYfU0ICSy2FnKEEEJITlDIEUIIcSwUcoQQQhwLhRwhhBDHQiFHCCHEsVDIEUIIcSwUcoQQQhwLhRwhhBDHUqgqBFZz3VggNQkoGml1TwjJnXAA7QB8J3UUAWzkoBEScGwt5Gq1t7oHhOTO8wAe1jOcFDG9L1URvwCwFMCtrC9HSECguZKQANEXgGSc+zeAUj4CDvpr0e5uBHACwGf8JgjxOxRyhASAqQBmSdJy03tpAM5JZXsAJwGk6tqc8UMcoSdtZkVFQvyHrc2Vx7cCaSlAWDhQkencSQgg2tlaAK1M76UAeB3A/+lCrQWACADJAMoB+BpABX3bKgCOAmgIYL9F50CIk7C1kFv8CHA+DihZCbhtvtW9IQRYrwsxgw0A2uqCzmAOgOpSnxFADQBSjXGcvoiQLA5gB4C6utZHCCk4NFcS4icWmQScaGxv6RqdWcBlx3O6MLykv47U687JXB4hpOBQyBHiB17UHUgMASdCa3Q+97EGQDOTUCyla4KEkIJDIUdIIekA4EnT64m6kCsIW/VYOnFSEeoD+JzfECEFhkKOkEIg82ffm35IywE8UMgRFceVkSbPy+EmLZEQkj8o5AgpBLNNLv8SFtDVT6P5uR6GAN0ZxTccgRCSNyjkCCkgPUxCTcyL1+XRySSv3K57YAolAUzx474JcQsUcoQUAMlUMtmUxeRlANv9PJJirrzJND/X3yf+jhCSOxRyhBSAl/T4NuhZSiTQOxD8qTuyQBeo3wboOIQ4FQo5QvKJCLeHTNpWtwCPoIQixOnt2rpTCiHEBRlPBk7X7zK+mW8JCSCTdHOl4XiyJZ9/30i/ZA3vydyQHJd365lShLd1UykhxOFCLkJm4wkJIg11hxPoTiZ3FmAfCQX4GwlTWAfgat0J5dkC7IMQN2JrIUdIsJlosvFL2q74IB5bzJR/6Frg4xJS0KqVKr6aHZ71kkmTEHfDOTlC8khTAJ309gUATwV55DYDWGjKbflCkI9PiB2xtSb3vylAynkgvCTQbJjVvSFO503T9O/zpmTKBXEkidG1wAn5/Nv7AezWn04f0AWtP2PzCHEattbktkwFNn6irQkJJJJD8gbTnNprhdjXGH1OTdb5Za/u7GJoc08Xoh+EuAFbCzlCgsV4kxb3H1OAthU8Yjq+tAkh2UMhR0guVALQR29fLICJ0d+INrdAb5fUwwsIIVlDIUdILjxhmrx+J0TmwMaa4uxkfpAQ4kDHE0ICTQkA9+jtS3px1FBAPC1X69XEY3Wvz6U+2xRplX2mS4YXELdATY6QHBCn3Wi9LXkjz4XQaMncIPS5QpkzJIRkhkKOkOzwePCo6eUzITZSC/RwAkF0thoW94eQUIRCjpDs2LRJhQ4IK00CJZSQOUKhKFN9EZIlFHKEZMcXX6Q3nwvhZNFn9PYQ5ionxFlCrnwDoFJTbU2IXzl9GvjtN9U8BmCJH3e9EcAqfV1YzgOYpreLA/iHH/ZJiJOwtXdlN6sDlohzmTNHzckZJkEpd+MvjJg7f/GW7gEapgeHi3ZHCHGAJkdIQEhLA2bMUE0Rbh+F+DBvN2mFl+spyAghGhRyhPiyZQtw9Khq/gjgpA1G6D2TA8pjFveFkFCCQo4QX77+Or1ZmETMwUTm5U7o7cEW94WQUMLWQm7RaGDOCG1NiF84fx5YqucOKVsWKwIwrHP0kARZ+wspnjpLb0sZn/5+3DchdsbWQu7kdiBus7YmxC+sWAFc0ivF9epV4JpxOdFCT8cla3/yoak9ys/7JsSu2FrIEeJ3pk/3tgcMsNUA/w5gh95uq2t0hLgdCjlCDE6eBP74Q2s3bgxUq2a7sfnUFDN3l8V9ISQUoJAjxBwbZzBwoC3H5Uu95p0w0uK+EBIKUMgRIkjg99y52lgULQpcf70tx0Wys6zV2xIvV9fi/hBiNRRyhAiHDgEHDmhj0bYtUKaMbcfFyHgSDuAOi/tCiNVQyBHi63BiU1OlgZzJKb1NIUfcDoUcIampwMKF2jiULAm08Ldzf3BJBPCz3q4egFAFQuwEhRwh+/cDp3Tdp107oEQJ24+JOKAYP3BmQCFuxtZVCK64DUg5D4SXtLonxPamSr3iAPr1C/jh3tRj2OIDeIwf9DRfFQAMA/BEAI9FiGM1uZdffhlFihTBww8/nP5eYmIiRo0ahfLlyyM6OhoDBgzAsWPi8+V/mg0DWv5TWxNSaFNl+fJA06YBH8gJehHWQFaKEpE9W29X1YPDCXEjBRZy69atw0cffYRmzZpleH/06NGYO3cupk+fjuXLl+Pw4cPo35+Z9EiIsncvkJCgta+9FoiMhFOYaWoHXj8lxEFCLiEhAUOHDsUnn3yCsmXLpr9/9uxZTJo0CW+++SY6d+6Mli1bYvLkyVi5ciVWr17tz34T4h+kbpzUjxN69XLUqC40VSYYYnFfCLGVkBNzZI8ePdClS5cM72/YsAEpKSkZ3m/YsCFq1qyJVatWwd8knweSE7Q1IQUyVf4oFeMAxMYGxVQpRAMopa8DzXSTlyVNlsSN5NvxZNq0adi4caMyV/py9OhRREREoIxPIG1sbKz6LCuSkpLUYhAfn/fp+Bm3AOfjgJKVgNvm5+s0CNGCv8+d00bi6quBcAmfDjxbdaFzEECNAB9Lcrjcp7dvBuD/R01CHKTJHThwAA899BC++uorREVF+aUD48ePR+nSpdOXGjUC/bMnRGfWLMeaKg0WmALDh1rcF0JCXsiJOTIuLg4tWrRAsWLF1CLOJe+8845qi8aWnJyMM2fOZPg78a6sXLlylvscO3asmsszFhGkhAQcCRkwvCorVQqaqdIKZujr2gBaWtwXQkLaXHnDDTdg8+bNGd4bMWKEmnd74oknlBYWHh6OJUuWqNABYfv27di/fz/aSj7ALIiMjFQLIUFFwloM0/hVVwEREY79AkSbu0dvy2z5Bov7Q0jICrlSpUrhiiuuyPBeyZIlVUyc8f7IkSMxZswYlCtXDjExMXjggQeUgLvmmmv823NCCsN333krgPfu7eixlHg5sa2U0XNZvmJ1hwixc8aTCRMmICwsTGly4lDSrVs3vP/++/4+DCGFQ/eqlDQFdUeNgtMddMUBZZhefqeB1Z0hxE5CbtmyZRlei0PKxIkT1UJISCIelUeOqKZ4GzpdwAki0m/Xf/AdARRp1SrH7T3r1wetb4QEEiZoJq42VRq115zO1yLb9TYrhhM3QSFH3MeKFdq6eHG4JQ9PKoCVersRgKx9nQlxHhRyxH1ZTgwP4UaN0tNeuQEjYbNkWrne4r4QEixsXWqn6xtAWgoQFpxEFcQJzJ3r9aqUsjobNwa9C30ASMBCcpCP+xWAV/WUYmKy/DbIxyfECmwt5CqK3YWQ/LBpk7YOCwOaNLFk7IIvVjVkTm4vAAl7l59OlF5FnBAnQ3MlcRdGQuZ69YCaNeE2RJsTJHleB4v7QkgwoJAj7mHtWiBZNxL26AE3IlUJLupt1pgjbsDW5sp9K4DUJKBoJFCrvdW9ISGPuaahT+aeYCLitbgubOYF+dh/62ZLOT6dT4gbsLWQ+228t9QOhRzJle+/19ZipvSpaB9MPgxiqZ2s+BzA4/q8XBsAayzoAyHBguZK4g4kw0liojchc5EicCuLJaMJACky1MLqzhASYCjkiDv46SevkOsoia3cy096+IL8+Idb3RlCAgyFHHGPkBMqVgRasqqapPkSrtJNp4Q4FQo54nwk+HuvRIgBqFYNKFECbsdIvyyVHJ1bLpYQCjniBqQC+Hm91oBezNftfGIKJbjP4r4QEkioyRHns327ti5aVOWrJNqcnIQTCJcDYGY84lQo5IjzmadHo9WoAVx2mdW9CRlm6Gsponq1xX0hJFBQyBFns20bkJCgtW++2erehBTTTCbLrhb3hZBAYWshV6w4EF5SWxOSJRs2AGlpul1ODHPWIyI3Xl9biRhxjWjBGy3uCyGBwtYZTwbNtLoHJOSZM0dbV60KtG2LUCBUZgU9emX0UQCuAVAXwG6rO0WIn7G1JkdIjkjw9+nT3vk4cTwhGdhgaouQI8RpUMgRZweAG0KuVy+rexOSfClhhPqNQDQ6QpwGhRxxLjt2aOvISEsTMocyqQDWmcyoEhxOiJOw9ZzcmreBpHggMgZo85DVvSEhx6JF3lReMicXIrwKoCyA03o1AKuRhGcyW1kfQBMLK5cTEghsrcntXgRsn6OtCckUOmCYKvv3D6nBGQLgLn0dCkjpHT0fDHpa3BdC/I2thRwh2fLXX97QgVq1OFA5cECSweht1h4mToNCjjiTmXp8SeXKQLt2VvcmpEkBMFlvd5Kashb3hxB/QiFHnEdKitdUKVUHGDqQK3+Y2iy9Q5wEhRxxHsuWAXFxWrsnZ5nywhe6p6WYLR8M7LdDSFChkCPOY98+oEgRIDwcaMpqaXlBaqZv0dv1jBp8hDgACjniPL7/HvB4gLJlWXUgHyzW16puulFklhCbQyFHnMXBg8DJk5om17u31b2xFf8FcMF4sWmTtZ0hxE9QyBFnsWULkJSkaXIMHcgXu80ZT1auDMCXQ0jwsXXGkxrXeTOeEKKYP9+b5aRz55AcFCnhWg7AKYQWEhA+BcAd8mLNGuDMGaBMGau7RYh7hVz7/7O6BySkEO1t/36tXaqUlrMyBLkXocsuo5GcDJw6RSFHbA/NlcQ5iPYhc3LCwIFW98aWTNSDw9UDwzffWN0dQtytyRGSgWPHvO36km5Yo0irVhyoPCLVyk9Kohh5cfgwx43YHmpyxDl8/bW2lnmkq66yuje2RLS42caLVauAQ4es7RAhbhZy390OTL1ZWxOXk5CgLUKbNghl1ulJkY06bqGG7roDFCtGIUdsj62F3MWTwPk4bU1czvr1wNGjWrtFC4QylfX8kMokGIKsMhqS9UQC6wmxMbYWcoRkiI8TSpcG2rNgTGE4YdaG//xTS3hNiE2hkCPO4JdftHVYGFCpktW9sT9GIP2BA8AJJfYIsSUUcsQZCZllEW691ereOINBg4CICG9VB0JsCoUcsT8SAJ4qhWIAxMZa3RtnUK6c10xpmIIJsSEUcsT+zJihraXqwE03Wd0bZxATA3TrprWXLgXOnbO6R4QUCAo5Ym8kM4dRIFWcTsTtnfiHmjW1tWjJFHLEplDIEXsjJWF27tTaffta3Rtn0a+fVrJIhJwRaE+IzaCQI/ZGaseJR6XcjBs2tLo3zkKSXIt2LBjaMiE2w9a2ndYPApcSgWJRVveEWMZXXwFpaUDx4oBNclQ+DqCEuUBpqBIVBVx3HTBvHrBkiVaVQBxSCLERthZy9ehj4G4SE4H4eK19zTWwC7Yy/LVrpwk5CSeQPJYUcsRm2FrIEZfzxx/e+Ljmza3ujaMwKjdISPheaSQn44MRI3C//rlH0qgRYgM4J0fsy/bt3rmjTp2s7o0jkRK0kksmDYCIvSJWd4gQNwm5M3uBU7u1NXEhEr8lSNBytWqwC5cDaKyvQx0PgL/1G8XVEiNudYcIcZOQm38/MHOwtiYu4/hxb+jA4MGwE0sk77G+tgPvSsUPva2HhxNiG2wt5IiLkcTB4ngiVKxodW8cjQQPFNfb11ncF0ICKuQ++OADNGvWDDExMWpp27YtFixYkP55YmIiRo0ahfLlyyM6OhoDBgzAsWPH8t0pQnJl7lxv+ikGgQeUg1KgWG+LzqynbSbEeUKuevXqePnll7FhwwasX78enTt3Rp8+ffCn1JwCMHr0aMydOxfTp0/H8uXLcfjwYfTv3z9QfSduT8osFC2qxXORgAs6IVqP8SPEkSEEvXr1yvD6xRdfVNrd6tWrlQCcNGkSpk6dqoSfMHnyZDRq1Eh9fo2N4phIiLN7txY+IAwcaHVvXMHbgAofiAQwyurOEBKMObnU1FRMmzYN58+fV2ZL0e5SUlLQpUuX9G0aNmyImjVrYtWqVQU9DCGZOX1aS+NlLu5JAsppAEl6uzrHmjg5GHzz5s1KqMn8m8y7fffdd2jcuDE2bdqEiIgIlClTJsP2sbGxOHr0aLb7S0pKUotBvJHBgpDsmDJFqz4gWTiMcjAkoJwCILPvAwCMlDcuXtRSqRHiNE2uQYMGSqCtWbMG9913H+644w789ddfBe7A+PHjUbp06fSlRo0aBd4XcQGSp/LsWa1du7ZXoyMBZ6W+vmho04Q4UciJtlavXj20bNlSCajmzZvj7bffRuXKlZGcnIwzZ85k2F68K+Wz7Bg7dizOnj2bvhwQ13BCskMeqDZv1trXX89xCiLz9XWMuVAtIU6Pk0tLS1PmRhF64eHhWCLZynW2b9+O/fv3K/NmdkRGRqaHJBgLIdkiISlSGFWWHK4r4n/26tlPJMUXjhzhEBPnzcmJ1tW9e3flTHLu3DnlSbls2TIsWrRImRpHjhyJMWPGoFy5ckpYPfDAA0rABcqzsu+XQFoqEFY0ILsnoch33wGXLmntJk1gRyQ9llyyqbAXEnq/CUAdebF4MTBuHMM3iLOEXFxcHIYPH44jR44ooSaB4SLgunbtqj6fMGECwsLCVBC4aHfdunXD+++/H6i+o0SFgO2ahCLi7CDxcTIPJw4nEiNnQ7J3wwp9PgfQQywwoklv2wZceaXVXSLEf0JO4uByIioqChMnTlQLIX5n717g8GGtTQcly0yWEiuntOkVKyjkSMjD3JXEPqxdq62jo4E+fazujSvZAmCZ8eK337RQDkJCGFsXTd06C0i5AISXABoxe5jz2bBBW0ti5gr2tVXfrafHSgDwCeyFiLTjxos9ezSNLjzc2k4R4lRN7vdPgTVvaWvicE6eBIxq1JLKS+aEbMozAN7U13bkdflP5kNTUzUHFEJCGFsLOeIiJPg4OVlr21iLc0r2EyXghF27LO4NITlDIUfswfTp2joyEhg0yOreuBol1lq31l7MmaNloSEkRKGQI/bACD6WfImSs5JYS3U9TXNKird4LSEhCIUcCX0OHgRW6pkTb77Z1vNxjqFfP2194QIwc6bVvSEkWyjkSOgj+VANwda4sdW9IULFit5sJ+IUREiIQiFHQp+pU72pvDp0sLo3xHD+adpUG4tp0zgvR0IWCjkS2ohTw4kTWvvyyzXHExIaGAmyZZ40Ls7q3hCSJRRyJLTZtw/YuFFrt2oFhPGSDRmaN9fW584BP/xgdW8IyRJbz+DH1ATCo4Hi5azuCQmokJPAY1m6dHHEQO8AIGVfj8HmiGYtOUTFMehvKcJDSOhhayHX80Ore0ACzqJFWuCxLHVUkRfbcwMcgpgpY2MBKXQsdSQljyUrtZMQg7YfErpI/NUff2htKeckiZlJaHHLLZqWLQLO+K4ICSEo5EjoIg4nhkNDpUpW94Zk52UpWrY4CBlzp4SEEBRyJHT56SdtLR6Vgwdb3RuSFRJGYFRo//FHjhEJOWw9J/fz00DiGSCqDND5Bat7Q/yOVJ4WRFOIiXHMAE8RBUgUVQDDYHPE27VMGa0tBW05L0dCDFtrckc3AodWa2viwCwny5dr7e7dHTUfJ+Hs3fS1I7jtNm+KrwULrO4NIc4RcsTBSOyVkd3eSAZMQhNz6SPR5ggJISjkSOim8hIzpbik92fZ95Cmbl1vTlFJ8UVICEEhR0KT48e1ddmyQMmSVveG5EbDhtpaHkoSEjheJGSgkCOhKeCWLdPa7duzfpwd6NzZW8GdKb5ICEEhR0IPmdeRwqiiFVx7rdW9IXmhfn3NOUi+M6PALSEhAIUcCT2+/x5ITtbc0Y1yLiS0KV9ecxCS72zWLKt7Q4gz4uSIAxFnk7/+0totWmjFOUnIUUQqQvhwP4C3ASRfvIgSkrDZIblGib2hJkdCC5nT2blTa9esyYS/NmK3/tRcQl6sWWN1dwixvybXoC+QnABEOCdOmPz2mzYGMicnyX8dyCcASuvldpzEzwD+BKCSfEkg/5AhVneJEHsLuZb3WN0D4nc2bdLWKSnaPI8DeR7OJMUsuA1tnBCLobmShFZpHalLJnTqlDGTBrEFbxiNs2eBX3+1tjOEUMiRkEvlJV6VQrVqVveGFIBDvlXdCbEYanIkdPjmG+DSJa3t0Pk4p7NGn5dL/z4JsRhbC7mpNwOftNLWxAEYQcQSVFxaXDOcyQEAHn3tRP5nNEQrl8oEhFiIrYUccRDx8cCiRVq7bVvmq7Qx082V3RcvtrYzxPXY2ruSOIgDB7SwAXn6l3yVhQxMJtaxTv4rUQK4eFH7XgmxEGpyJDSQYpuG04lRtoXYkoPyn3jGSoovSdFGiIVQyBHrkeKo69TzP3DFFVqmE2Jv+vbVstWIx+yePVb3hrgYCjliPeKcsFuSQgGoUQMI42Vpe+R7FE1OgvqNAH9CLIBzcsR6fvxRWxctCgwenOljzrnZkHbttFjHQ4eAhQuBfv2s7hFxKXxkJtazbZvXbMksJ84gPBwoVUpri/OJaHWEWACFHLGWpCRvJelrrgEqV+Y34hQMrTwuDvjlF6t7Q1wKhRyxljNnvOV0Gjbkt+EkatfOWO2dEAuw9Zxcx+eB1BSgaLjVPSEFZsYMTZsTbrzRFQM5DECkKLFwOOIpGxsLHDumpfhi6R1iAbYWclUZA2x/jJIsMhdXvTrcwHK4CMleM3s2kJAAnDoFlCtndY+Iy6C5kliHaHBGORYJAC9enN+G07jySq9ZmtXCiQVQyBHrWLtWvwrDgD59+E04EUnRZmhvRtV3QoKIrc2Vh9d75+RourSxkJPQAZeYKoUOpjk5x5supZqEoaEzKJxYgK01uWXPAAsf0NbEZqSmAvPna+3mzYG6deEWpgBYpK9dFUpw9CgFHQk6thVy3wwAzsdpbVkf0JUCYhPEESExMbOrOXEel13mbe/da2VPiAuxpZCb9wAQvy/jewvvBxLPWdUjkm9mzvSGDnA+ztlce613Xm7aNKt7Q1yG7YRcUUTg8KqsP/u6R7B7QwqMkZBZqoBLMl/i/Jg5w8tStHhCgoTthNxIZCPhAFy6AFw4HdTukIIgjiZGUuZGjYAyZTiObkjYbFQLZygBCSK2E3JVcKVXq4vI/Pn8UcHtDykA69drCXslnVevXhxCt4QSGAmbKeRIELGVkGuEAShi6nKkrgAUM8UQn95hQcdI/jBuciLo6HTiDipWBCIlcALeArmEBAFbCbkb8UZ6O7oKoKf1RYT+gGhwUs8URUIQEWxz53oTMjMps3sYONBbemfLFqt7Q1xCvoTc+PHjcfXVV6NUqVKoVKkS+vbti+3bt2fYJjExEaNGjUL58uURHR2NAQMG4JgkaPUDpeF1ULj+We/7Iuyiynpfr3zVL4cjgeDcOeDiRa1dp463AgFxPvXre9t79ljZE+Ii8iXkli9frgTY6tWrsXjxYqSkpODGG2/E+fPn07cZPXo05s6di+nTp6vtDx8+jP79+xe6o+VQH0XSdTegWsuMn181wts+xofE0EWS9RpCjvNx7qJDB82bVvj2W6t7Q1xCvtJ6LZQy9iY+//xzpdFt2LAB119/Pc6ePYtJkyZh6tSp6Ny5s9pm8uTJaNSokRKM10hRzALSCc+nC7kSlbT3btMTZgieNGDVm3o7BUi7BITZOmmZQzHMVHKzMz/Zu4gabk/YLEm5JftJfDwQE2N1j4jDKdScnAg1oZwe6CnCTrS7Ll26pG/TsGFD1KxZE6tWZe36n5SUhPj4+AxLVtSBd5+NddO+mSJhQNEo7+udkjeJhB5GhWhJ48XQAfchpXeE06eZ4ouEtpBLS0vDww8/jOuuuw5X6IGeR48eRUREBMr43LxiY2PVZ9nN85UuXTp9qZFNYHAJaILUAw+uGJJ1n2Kbe9tbZxTwxEjgkCf4S5e0ebh+/TjSbqRrV6/J8qefrO4NcQEFFnIyN7dlyxZMK2SanrFjxyqN0FgOiOeVD2VRJz10IBXJCM+m7Fiz273tU/SwDD02bvR6WIrTCXEfYvWJ0k0uf/2lXQuEhJqQ+9e//oUffvgBS5cuRXVTiZTKlSsjOTkZZyR1jwnxrpTPsiIyMhIxMTEZFl9a4Z/p7VPYld7e8LE2DydroVpr79+kJvL3E5JOJ4apUoqkuhQpmiHBMK4tntG3rzdZ8x9/WN0b4nDyJeQ8Ho8ScN999x1+/vln1PYJ5G3ZsiXCw8OxZMmS9PckxGD//v1oa9jiC0B99Exv/4np3n3PBrZM1dbqZGReTo83FU5kjG4gViLm6pQUb2kdF3M3gDH62pW0aOFt76TJhYSQkBMT5ZQpU5T3pMTKyTybLBd1l3CZUxs5ciTGjBmjtDxxRBkxYoQScIXxrCyDy9Ln4zbi0xy3LVvP297+fYEPSfyN5Ko0Qgc6duT4upnWrYEIPSff9/yRkhASch988IGaN+vYsSOqVKmSvnzzzTfp20yYMAE9e/ZUQeASViBmylmzZhW4gxI2EA5tEs6DNJzDoRy3r9vV2z7EGnOhw88/e9M7iRs5cTfigGJUo8jGKY0Qf1Asv+bK3IiKisLEiRPV4g+qoGV6fNwFnMx1+7o3AWve0trnj/ilC6SwiEfl339rbZnDLVEiw8dFWrXiGLuNJk2AefOA5GRg2zaZ0Le6R8ShhHzuyia4Nb19FJty3b5kBT3PlzifJNH5JCRYsAC4cEFrDxhgdW9IKCCFco2EzYWw9BBieyFXG53S29uhe5jkQoTJQZPJmkOAzZu1ddGiWv04QkTAVa3qzWNpVIknxG1Crhzqpjud/IWZefqb8ibnkz36VBCxkEV6+platbSFEPO83JEjwP/+xzEh7hRykSiV7nRyHnF5+psa13rbh1m6ylp+/x0wEnjffLPFnSEhxU03eb0sly2zujfEoYS0kItB9fQJtos4nee/u8xr4cTZfYHoGckza9dmdDYgxEBS+BUrljEbDiF+JqTz9NfHzemelSeQObK7cgsg8QwQlTFVJmJM6S+Tss73TIKd5aRKFYBelIrlAMQ/6oSDr8KcPGY969frGxUBevbUyu5IULh4WbKILnGTkDNXHtinbg0Z6fxC1n8nv51ixYFLEnucBiQlAJF6TlgSRA4f1sqpCBIbxwKpimG8CL1cd523tpw4KFHIETeZK2PRLL29A/Py9belqnjbR2gJsQaZZzG85jqZbMiEGEi6P+PhR0JNCHGTkItBtXTPysPIX/qSSk297UNr/N0zkid++MGbeb4QuUuJg5GEs0b9SSmoK56WhLhFyIVDy4yRiiSk4VK+/rZaG287Ti9GTYKIeFQaN6yaNYHi2dRHIsRI2JyWps3LEeIGIVcO9dNryCVkEzrww73A9EHa2pcqLb3tcwcD1k2SHVKJ4tw5rd27N8fJPDSitOhrol8fhsnyu+84JMQdQq42vJnqj+OvLLeJ3w+c+Vtb+1KivDe9V7J+ryUWJGSWzBZtTGo1weUqXZ22Jvo10lSfX9i0CTh7lsNCnC/kaqJ9ens/fi3QPsJLamtPGpCsxyOTIJCa6s1gIambYmM57CRnrtUzOEiOU5osiRuEXCy8hTX3oGC5uUppfiuKOD19IgkCS5d6QwckES8huSGJu8UJRZgzh+NFnC/kSqNmumflEWwo0D4qNPC2DxdsF6QgrF6treWmVYhiucRFlC7t1fgl+0liotU9Ig4hLNRzVqYiWS0FoarJ+eR41tN6xN9IzcHleuB+pUpAPVO2bELyEkpw4oSWAYUQpwq5kqiEMBRV7QuFSH4UaypAfTYL5xQSIC3utJ5ntEcPDjHJO/3702RJ3CHkqqF1evsEdhR4P6X0clXCxdyLihN/sMYUec8AcJIfJL9pKc2CA8lvKRXlCXGikLsMHdLbR1HwnFwSelM0SmunJQNp/M0EnoULtXXZslq+SkLyilQkMEyWBw8Cu3Zx7IgzEzRXgZ4BAcBeLC3UvkpWBOIPaO2zB4GylxW2dyRbJMZJ5lOEbt3ynJWekHT69tUCwiX7ybx5TNhMnCnkyujVwIVDyL7q6VV3ASkXgPASOeyrjlfIHf2dQi6grFyZOe6JZOJ5AFIUI4Fjk5m6dbXg8IsXtfldEXZGaAEhThFy0aik1mlIzbIaeLVWeioTMy95m4fWe9LblZoA+3Vnv+NbgEb9AtFjopg/X1vHxFDI5cAnvFyyRyqFd+6saXF79gB//00PXVIoQvIRqRgi1ToJhc/HVfkqb/tkwX1YSG5IBvmjR7W23KQIKYzJ0shlaTw4EeIkIWckZj6HQ4XeV4WG3vY5VvEIHL/95m23axfAAxHH06gREB6utX/9VYu9JMRJQs4gTuVqz55oVFY152SdHeHFgSJayB2S9ExTJACIeUkoWRLo4PWOJZmRq1UyzmV/1bqcqChvkV0xV+7bZ3WPiI0JaSF3OAenE+EerMMYHFTrnIiM0RtpQAqzBfmfv/4CDh/W2h07ek1NJEvkapXqTzlftS6nXz/vdTR3rtW9ITYmpIXcfqzyy36iq3jbp5gtyP/88ou3bTyBE1IYmjTxmiwlTZx4WRLiJCEniZmPYZNf9lXWG5GAY3oFGOJHDOcAqf5NUyXxB+Zrae9emiyJ84RcGlKQggt+2VfsFd728T/9skti9qo0TJWixdFUSfxZfseAJkviNCF3EXqSXz9QqZm3fWav33ZLjNpxBl27ckyIf02WEhguLFmiFeMlxAnB4MIZ+E8alTGl8jqnh3IRPyCu3T/+6DUvtfdWcyckO3JL8eaR5Mxmk6VcY4cOaSbLOnU4sMQZmtxx+K8AXNFwoIguzlOYS8l//P47cEQPPjQS6xLiTwYN8rZnzODYEucIuZxyVhaEqDJ6Iw1IoqDzDwsWeNusHUcCQcOGQLRk+gSweDHL7xBnCDnxrDwAUwYNP1DKHEbA9F6FR2p9STYKoUQJgFUGSKADw6UY7w7+eIkDhJxwAtv8ur/yl3vbR//w667dydq1wPHjWrt7d6t7Q5zMwIHe9rRpVvaE2JCQFHKpSEIqknPd7gvcgIloota5UdEURnDSv/LTncyera0lZKB/f6t7Yyvkam2ir0keaNAAqFBBa//8M5DItEXE5kLuAvTCm7lwEjuUg4qsc6NiY2/7NMMICkdCgqbJCWXKaDchkmfkahW3KhreClAxXAScODwRYmchdxb7/b7P0rW87QuZS9SR/LBihSbohJ49OXYkOOV3DL75hiNO7C3k4uD/tCRFiwFhEVo7+bzfd+8uZs3S1lKx+bbbrO4NcUvF8Nq1tbY4PJ3nj5jYWMgdwOo8bdcUQ9ACI9U6L0SV1htpQOLZQnTQzUhh1P/pCUBr1gQqVrS6R7ZDrtaR+prkEZn7NYepLFrEoSP2FXKHoc/35EJXvIre+FSt80JMDW/7BJ1PCoY5vZKUQyH5Rq7WT/U1yQc33ZTZmkCIHYXcCWwNyH7L1fO243Kux0qy47vvvE/WZtduQgJN5crAlVdq7W3bgGPHOObEnkIuDYFJxFrJFEZATa4AbN2qlT0RWrf2Js8lxIrKBF9/zXEn9hRygcIcK3fmbyt7YnMtTrj1Vit7QtxKu3behyspv8PKBCQXXCXkYqp62xfyFopHDCQ+SebjBLnJXHstx4YEn1KlgOuv19pnzwKbN/NbIDniKiEXVgwoqj8Eply0ujc2jI2Tm4qRxqtoUat7RNzKLbd42198YWVPiA1wlZATosrqjTTgwkmLO2Mnvv3W2x4+3MqeELcjzidVq3pj5pjmi+SA64RcaVMYwfHAOHE6D6kZZ6RSqlFDWwixCklC0Lu3t3DvzJn8Lki2uE7IlTWHEdCcn79kzMKQIVr4ACFWIunkjOtQKhOIsCPEaUIuAUcRj4NqnVcqN/O2T/iv+Liz68bNmZP5CZoUGLlaD+prUoiYuRYtvJaGnTs5lCRLisHGfIyr8/03FZt62/FypyE5s3IlcEJ3Re3QQStiSQpF/q9akiWDBgEbNmjtSZOAV17hQBFnaXIFIbqSZOvQ2uf1mp8kB776ytseMYJDRUIHCSUoX15rL10KJOdeg5K4D9cJuSJh3jCC1ETAk2Z1j0I8GbPxpBwbCzRsaHWPCPESHg7cfLPWTkvL6AFMiBPMlQWlRHng3CGtfT4OiK5sdY9CFHPdrmHDtDm5bCjSqlVw+kSIb8zclCma44lYHeQ6JaQwmtwvv/yCXr16oWrVqihSpAhmmz3vlEevB8888wyqVKmC4sWLo0uXLtgZoEnhnvgQt+Bbtc4PMTW97aN/+L9fjiApyetVKcKtTx+re+QY5GoVnSN/Vy3JEomXM5I2Hz/uLQNFSEGF3Pnz59G8eXNMnDgxy89fffVVvPPOO/jwww+xZs0alCxZEt26dUNiAAI2L0cPNMEtap0fKjb2to/xN5E1Msdx7pzW7twZKFGiEN8UMSNXq+TsyN9VS7Jl6FBv+4MPOFCkcObK7t27qyUrRIt766238PTTT6OP/uT/5ZdfIjY2Vml8t4ZIUt9YUxjBye1W9iREEdOPOV3SXXdZ2RviMnIzfXvWr8/sgFKpEhAXB8hnZ84AZcoEtpPEnY4ne/bswdGjR5WJ0qB06dJo06YNVq1aleXfJCUlIT4+PsMSaMwld+IPBPxw9iypY5iY69XTFkJCFTGn9+/vfUD76COre0ScKuREwAmiuZmR18ZnvowfP14JQmOpEYSUUVGlvWEESXrOYbcjT8/G8p2em1JySNyyaxedSog9HFAiIrT2999r3paEhEIIwdixY3H27Nn05cCB4KhW4SW1ddol4BLDa9KRqCMjp4nIfz3XCSGhTenSQKdOXqep6dOt7hFxYghBZUm1I84cx44p70oDeX2l4QHlQ2RkpFqCjYQNnN7lnZeLNWVCcTNPADCK6LwtJYn0NkMESMgzciSwaJHWnjwZGDzY6h4Rp2lytWvXVoJuiVFcU+a84uOVl2Xbtm0RSpQzTTMd0RPsux1J2HW/3r4E4C2L+0NIvqhTB2jUSGtLKrq1azmAJP9CLiEhAZs2bVKL4Wwi7f3796u4uYcffhgvvPACvv/+e2zevBnDhw9XMXV9+/YNqeGuZNLc4rRTcT13ANCtuJgB4IzrR4TYjrvv9rbf4mMaKYC5cv369ehk2L4BjBkzRq3vuOMOfP7553j88cdVLN0999yDM2fOoF27dli4cCGiQiyxb5WW3vbpPVb2JHSedsaZXj9rYV8IKTDt22sp6I4dA3bsAHbtonewy8m3kOvYsaOKh8sO0eaef/55tQSazfgaxVEWF3E6339btra3zUTNgEQ1GrOoKwAwfDBwfC3XnzxcBfAYrkVqzIl38GuvebW5996zulfEQmydu3IxHi/w34YVBcLCgbQUb6JmSd7sSjwejDe9fMbCrriBgl+1JE8MGAB8+KGWsUfm5SR8SXeKI+7Drbd1RXF5nNY5dwTuZd06NNCbWwAss7g7hBSKYsWAgQO1tsTLcW7O1dhak8uOaq30SO9sOLReM7eWvkyrQqDeWwPE6EkTXMeECelNzsURR6T9ktqHX38NSM5cycN68qS39hxxFa7W5Mw5LA9vhDuRrO16Ci/xv5lldX8I8QeSULxXL62dmgq8LVGfxI3YWsj9C1sxFmfVuiBUa+1tn9wGd2JM0AP4j57KiwSWrXo2mYJdtSTP3HuvVlhV+PFHLXEzcR22FnIRiEYkYtS6IFRs4m2fPwb38eefWjJmMddKxQir++MS5GqN0dckwKm+brpJa1+6lMEsT9yDrYVcYSkW6c1hdemilsDcVbz0UgYtLtXSzhASAB56SHNEESTl16lTHGaX4WohJxSXigQ68aLOuGkubrseDVe+PD61uj+EBAKpK2fW5t54g+PsMlwv5MTD0uCQm1LdPfect33PPdTiiHN5+GGvNidzc1JclbgG1wu52ObewTiYdV1X57FyJbBvn9aWisohlleUEL9rc4anpcxJ/EeM88QtuF7I1bjOOxgnd8D5yI/8WVM03KhRQFGjuA4hDtbmDE/LVauA3but7hEJEq4XchUbewfDCAx3NLNmeSffq1cHbr7Z6h4REnhKlgRuvdX7+t//5qi7BNcLuWIRXg9LTwqQ5mQXw5QU4M03va8fe0xLaEuIG7j/fqB4ca0tFQp++83qHpEg4HohJ5QwZfs5/heci8QJJSVp7QYNgOtMtlpCnI6YK8U8b/DCC1puS+JobJ278gfci2Iojku4WOgq4Rd0U+W+pUCsqaCqYxAT5QwphapjnpcjQeVeCV0BCnnVkgIxeDAwaRJw+jRw/Djw1VfA7bdzMB2MrYXcDszzy35qXAscXKm1D62DMxk7Nv2pdTaAfkOGWN0j1+Kfq5YUCDHPiwZnaHRSkqd/f23OjjgSmisB1PIWOsfZ/XAekpV9wwbVTAQwwur+EGIlbdoATfScfmK+p1XD0dhak/MX0ZW87ZTzcBaivT3xRPrLFwAwTS1xaykeVYZHePVVoGdPLaRGSvFs2QJccUXwOkmChq01uSpogeq4Rq0La8EIN1krzh6Ec3jnHeDs2fQkzOYK4MQa5Gq9Rl8Ti4iN1SqIZ2HOJ87C1kJuCObgLqxS68ISU8Pb/nsRnMHRo9rEus4wUews7RAR5GqV5DqFv2pJoXj0Ua3unHDkiOaQQhyHrYWcP6nR1tve+wtsa6YxLzsMcwyA+QCWWd1BQkIJyWc53mTb+OQT7cGQOArOyenU7Q5smqy1z0iJbJvNNfjyCIDL9bZMM5pyPRBCDCRWtGVLzTFLzJUPPgh88w2TJDgICjmdsrW9g3LpgqYA2TUZSBWfuTdxlj5nYX8ICeUHRSlge1wVYZa5ir+BL78E7rjDiu6RAEBzpY4ItKJ6xh/hlI3zt4q1VU9FCwn7+8Li/hASysTrAfrpTJwIHHSS95m7oZAzUbqWt73tO9gSqfVdT29LTNyNFveHEDsgMxV6PgjNbCl5Lult6Qgo5EzU6extH/gVtqM1AG9EHHA3Y+IIyTPd5T+j7NThw8BL8shI7A6FnInL9bqKQoLNnKyiACwyfaGLAUyxuE+E2M1smUGwzZ6t1Z4jtoZCzkTJihJHptXaSUv1oFar4qjWqkiGJVRZKgWQ9bZkNDHJa0JIXrnhBm0xGDNGS+ZMbAuFnA8XcEKti6AImmM47MBLegYN6MHe8hPVC+oQQvL9g3oJKFPGW4PxzjvT402J/bB1CMF7aKSEkQf+uwD34Gc0hZahvyXuxgZ8jFCMgzPoBuBJ0+uxADb6tVfE3zRSD1Hw41VL/IrMy0kYQZ8+mnA7dEjL/yr5LontsLWQS0ZCgf4uJ7NjNbROF3IVcUW2fxuFcCzA/6l2dPv2SERKxgSwQeAyAHP1Gyb0jCb8GYY+BbtqSVCpWhUYN85boeDnn4EvvmD8nA2hudKHQ1gLj57hsRgiERaizwGScW+TKR7uGICuFveJEEchafF69/a+fvdd4Fcbul27HAq5LEjS84OIKbQF7kIofml/Slyf/lrm35pJphaL+0WI43jmGeByI0EegNGjgd02zhThQkJTTckjbTEakYhBEuKxChP8tt89WIpG6KvarfEQ1uNDhBJrdFOlIDpnRwBxFveJ5J3ReiopcVn331VLAjUvXkwvU6XKTsoc3dChwNy5QMWKHHQbYGtNri3GoCOeVWt/shzPpbfLp+cPCQ1+BGD8DMVx4XYAqy3uE8kfcrXKTI9/r1oSKMRCIrPzF9PfuAT065dep5GENrbW5ALFUWxS83JFEIYwFEVJxOK8mvUqvIdkYRxT5prm3UTAidvL1ALvjRCSVySB85UAtkuSW9HmEhOBXr2A+fOB6GgOZAhja00ukJw3xcvdjHes7g7myTy46fV7rPJNSFDZYdScM7hwAejRA4hXuVJIiEJNLhs24hNcr4cI1M8gXoIfCydVBdqbXn8O4EG/9YgQkmeuvBJ4+23goYe01+fPA927aynAOEcXklCTy4ZleC49yDwcxVFUqzZliRelWcB9CWBE0HtCCMlQaHWCyWUoKUkzXe7axUEKQSjksiENKbikJ8cSk+WNeDOY3wtKATgCoLH+WsTtRwBYypGQEKB9e+D9972vxRnl1lsZRxeCUMjlwA7l6qFxFf4RjO9DP5Y20V3JJOBe9C3sSAixltatgf/+11ueR3j44YzCj1gO5+RyYA5GojEGKk0uHFGIQHSBU4nllUf01Fxhpji4EbqZkhBiLVnNqVeVIsu69UXx2WfAunXAp59mFIDEEqjJ5UAyzuESklVbBN2t+D5gX0RRvTLxa6YvRY58HQUcISHNYd3qssf85ubNQIcOwJ4M7xILsLWQO4KNOIBVah0oNuLT9PZl6BCQY0iZHEkk1taUbFmi8ioz0NuRyNUqpThZLcI5JAKoI43rrze9mQjccovmjUksw9ZC7mv0wSRcq9aBYiEeSPeylNDwKzHSr4M/T9fgiuvvefT3RMCxVKMzkav1Wn1NHMabbwL/p4UepSPzdl27Avv3W9UrV8M5uVwQAZeAoyiFKup1T0zEJkwq9MCLl6RUqovwSR8kRX5mFHrvhBDLkJRfEmYgOS6NquKnT8PTvz9mARikz7X7EswyXW7C1ppcsJiMTunanMTLXYbOBd7XlboN/3OTgJM9/6VXFaCAI8QBVKoELF6smSt1ZCpigG7aZN7S4EEhlwdOYXuGmLlhWJDvgb5cTwsk8zCaTuh1LhkOoIlkCcr3XgkhIY1UFJ8zB6hQIf0tqQH5hl48905LO+cObC3khign/5VqHWi+Rm+TNheOq/FAnp1K9uouxvVNjiWyp+/04qdTAthvEnrM0edhA3/VkpCgWjVg4UJVmVLz1dYoqaxEkicXeELekMTPxO/Yek6uClogBtURj4MBP9bfWKy0OYmXE22uC14C8HKW24ogewrA43rsjCHYBLmMN+vVBFgDzp20AFAdCMJVS4JNbrlpPwNUdUpxXzMi6EoYd5Krr9bm8p57DihTJhjddQW2FnLB5n1cgQexUwk5+eeLOA9P1FNx+arIHl2bk1TPfwetx4SQUELuA/8EMEqPfxUnlAzh4r/9BnTpAsTEAMOHA8OGAcWKBbyMl5Oxtbky2JzGbsSplMkZ+Q1Aiiq2qhVXNA9qmm6aqqYLPwo4Qoh4Ut+mO59JIS9txt+ElO957z3gmmu08IMPP7QgRbwzoJDLJzMwGGlKdHnt5819VGKPbmeXyeUIPWuJJFsmhBAzcieRoj1R8uLVV4Hy5TMPkIQhfPqp8spM0B+me/tMg5DsobkyB6JQBk0xFE0wCJXRHBEopQLCNTJeYh796Uy0trsB7Mxpx4QQ4kORx2UWXxN4rwO43WdOv4jurHK9vsg9J1F3bJMEEg1atdIKuxYAj4NNnQETchMnTsRrr72Go0ePonnz5nj33XfRWrJ2hxgyt1YeDVEP3VEHnVAJTVESlVAMkfqnuT8vXUIxRCBF2dbfooAjhBQCEVz/0pdovQKJmDbL+ZjeiuiZkhrpiyR3hz51cka/D/2qsjZpaeQymURdQkCE3DfffIMxY8bgww8/RJs2bfDWW2+hW7du2L59OypJkGSAEcEUhXIoizqogAaoiMYoh/oog8sQjVhEoTSKoTjClFjShFhehJmEEKQiWc3N/Q+fob2ed7wYLqm/lkUyGgiSwKeZmNYDeqaEECeToJsz9Trk6A7gUXHE1LU6X6EHfYqkkr5cp4cneEzm0SQ9V+4J/T6lSr1OnQrUrQtUr65VOI/IOANoZ6eXIh6P/4MzRLBdffXVeE8mTmVg09JQo0YNPPDAA3jyySdz/Nv4+HjcW3q5EkLFEKVX5Y5UmpUs0pasI2FKe4pWwsmj/zPElfZ/4SzWsr80XMIFnMRhrMfv+Azb8b0Sc4L0bAG0HHXX4kWEIyXTET0+beMCk/m6syrIXFvO6hfzBf3zS/oi28vRZL/aUTX+APBDoc6OWMkBUwhBDX4VpBA01+tM3qBfU5r9qXDzdR6ftke//xjLJT3eL1m/X8nSuF49TTBGRmqLtM2vw8O9a/EWlc+MtpQjkrasjdeyhIUhISUFpZ58EmfPnkWMeJyGgiaXnJyMDRs2YOzYsenvhYWFoUuXLli1SpTmjCQlJanFQE6mAXrl65jZufTnhBHYLUJL4t8u4AROYAf2Yhm2YgZO5sO6Lem4JGru4fT+ZFwb7TB9wOUJrLD6LG+QhBB54L3PZxjK6Y4pN+oFmCvr9xzDbpXbndL3viXkWhVvl9IH/Y6Ya4VC6WIeP3Po0CEl/FeuXJnh/ccee8zTunXrTNuPGzfOeFjgwjHgNcBrgNcArwGP7xgcOHCgwDLJcu9K0fhk/s5ATJunTp1C+fLlUaSIc5xkxQwrJtsDBw4UWO22OxwDjgGvA/4W8nM/EA3u3LlzqFpV6q8XDL8LuQoVKqBo0aI4dkzKfnqR15Uri+KckcjISLWYKePglDbyZbpVyBlwDDgGvA74W8jr/aB0aZkQCqFg8IiICLRs2RJLlizJoJ3J67ZtpfY1IYQQEhwCYq4U8+Mdd9yBVq1aqdg4CSE4f/48RowYEYjDEUIIIcETcoMHD8bx48fxzDPPqGDwK6+8EgsXLkRsbCzciphkx40bl8k06yY4BhwDXgf8LQT7fhCQODlCCCEkFGCCZkIIIY6FQo4QQohjoZAjhBDiWCjkCCGEOBYKuUKWE7rssssQFRWlklKvXbs2x+2nT5+Ohg0bqu2bNm2K+fPnZ/h81qxZuPHGG9OzvWzatAluGoOUlBQ88cQT6v2SJUuqLAfDhw/H4cOH4abr4Nlnn1WfyxiULVtW5X1ds2YN3DQGZu699171e5BQJLec/5133qnO2bzcdNNNcNs1sHXrVvTu3VsFhMvvQRL/798vtRPyQYETgrmcadOmeSIiIjyfffaZ588///TcfffdnjJlyniOHTuW5fa//fabp2jRop5XX33V89dff3mefvppT3h4uGfz5s3p23z55Zee5557zvPJJ5+ofG2///67x01jcObMGU+XLl0833zzjWfbtm2eVatWqXynLVu29LjpOvjqq688ixcv9uzevduzZcsWz8iRIz0xMTGeuLg4j1vGwGDWrFme5s2be6pWreqZMGGCxy3nf8cdd3huuukmz5EjR9KXU6dOeUKVaQEYg127dnnKlSun8h5v3LhRvZ4zZ062+8wOCrkCIjffUaNGpb9OTU1VP8Tx48dnuf2gQYM8PXr0yPBemzZtPP/85z8zbbtnzx5bCLlAjoHB2rVr1Vjs27fP49YxOHv2rBqDn376yeOmMTh48KCnWrVqStDXqlUrZIVcIM5fhFyfPn08dqF1AMZg8ODBnmHDhhW6bzRXFqKckJiR8lJOSJD3zdsLUkg2u+1DnWCNgZReElNNKOYzDcYYyDE+/vhjZa5p3lyqh7ljDCQV4O23347HHnsMTZo0QagSyGtg2bJlqsh0gwYNcN999+HkyZNwyxikpaVh3rx5uPzyy9X7Mg5iAp09e3a++0chVwBOnDiB1NTUTBlc5LVkeMkKeT8/24c6wRiDxMRENUc3ZMiQkExqHcgx+OGHHxAdHa3mKyZMmIDFixer5OduGYNXXnkFxYoVw4MPPohQJlDnL/NvX375pcr5K2OxfPlydO/eXR3LDWMQFxeHhIQEvPzyy2osfvzxR/Tr1w/9+/dXY5EfLC+1Q0hWiBPKoEGDVKmNDz74wHWD1KlTJ+V4JDeQTz75RI2FOJ/IE63TEa3g7bffxsaNGx1Vbis/3Hrrreltccpo1qwZ6tatq7S7G26QOuDOJi0tTa379OmD0aNHq7akh1y5ciU+/PBDdOjQIc/7oiYXhHJCgryfn+3dPAaGgNu3b5/SYEJRiwv0GIgnWb169XDNNddg0qRJSquRtRvGYMWKFepJvmbNmuq8ZZFr4ZFHHlHee268F9SpU0cda1eAKnCH2hjIPuV7b9y4cYZtGjVqlG/vSgq5IJUTkvfN2wtyA7dr+aFAjYEh4Hbu3ImffvpJhVOEKsG8DmS/SUlJcMMYyFzc//73P6XJGouEk8j83KJFi+DGa+DgwYNqTq5KlSoINSICMAayTwkX2L59e4ZtduzYgVq1auWvg4V2XXEp4jIbGRnp+fzzz5UL7D333KNcZo8ePao+v/322z1PPvlkBpfZYsWKeV5//XXP1q1bPePGjcvkMnvy5EnlUTlv3jzlTSfHkNfiPuyGMUhOTvb07t3bU716dc+mTZsyuE8nJSV53DAGCQkJnrFjx6rwib1793rWr1/vGTFihDqGeBm65bfgSyh7V/r7/M+dO+d59NFH1TUgntbiVduiRQtP/fr1PYmJiR63XAOzZs1S73388ceenTt3et59910VdrBixYp89Y1CrhDIoNesWVPFh4gL7erVq9M/69Chg3IDNvPtt996Lr/8crV9kyZNlDAzM3nyZCXcfBe5ANwwBkboRFbL0qVLPW4Yg4sXL3r69eun3K/l8ypVqijBL6EUbvot2EnI+fv8L1y44Lnxxhs9FStWVDd5OXeJOzMEhpuugUmTJnnq1avniYqKUvGSs2fPzne/WGqHEEKIY+GcHCGEEMdCIUcIIcSxUMgRQghxLBRyhBBCHAuFHCGEEMdCIUcIIcSxUMgRQghxLBRyhBBCHAuFHCGEEMdCIUcIIcSxUMgRQghxLBRyhBBC4FT+H7AdU/Qk1i8KAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "hist_kwargs = {\n", - " 'bins': 50, 'color': \"black\"}\n", + "hist_kwargs = {\"bins\": 50, \"color\": \"black\"}\n", "\n", "# Plot histogram with decision boundaries\n", "seq_gmm.plot_hist_distribution_with_boundaries(\n", - " operation_name='initial_pRB_split',\n", + " operation_name=\"initial_pRB_split\",\n", " resolution=1000,\n", " num_std=5,\n", " hist_kwargs=hist_kwargs,\n", - " save_path=sequential_results_dir / 'step1_pRB_split.png' # Optional: save the figure\n", + " save_path=sequential_results_dir\n", + " / \"step1_pRB_split.png\", # Optional: save the figure\n", ")\n", - "plt.title('Initial Split: G0 vs G1/S/G2/M (pRB)')\n", + "plt.title(\"Initial Split: G0 vs G1/S/G2/M (pRB)\")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "2ce61f09", + "id": "15", "metadata": {}, "source": [ "**Note:** The histogram shows the pRB distribution at the time of this operation. After subsequent refinements (M, G1, S, G2), only cells that still have the original labels (G0 or G1/S/G2/M) are shown in the histogram." @@ -383,36 +286,23 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "0bd07d73", + "execution_count": null, + "id": "16", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Strip plot with labels\n", - "hist_kwargs = {\"bins\": 100, 'color': 'black'}\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", "seq_gmm.plot_strip_plot_histogram_with_decision_boundaries(\n", - " operation_name='initial_pRB_split',\n", - " hist_kwargs=hist_kwargs,\n", - " scatter_density=True\n", + " operation_name=\"initial_pRB_split\", hist_kwargs=hist_kwargs, scatter_density=True\n", ")\n", - "plt.suptitle('Initial Split: G0 vs G1/S/G2/M (pRB)')\n", + "plt.suptitle(\"Initial Split: G0 vs G1/S/G2/M (pRB)\")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "4803c308", + "id": "17", "metadata": {}, "source": [ "---\n", @@ -424,7 +314,7 @@ }, { "cell_type": "markdown", - "id": "5c10f2fd", + "id": "18", "metadata": {}, "source": [ "### Exploratory Visualization" @@ -432,31 +322,20 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "929fb8e9", + "execution_count": null, + "id": "19", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Explore p21 distribution in G1/S/G2/M cells only (not G0)\n", "fig, (ax_strip, ax_hist) = seq_gmm.plot_feature_strip_plot_exploratory(\n", - " feature='pp21 (nuc median)',\n", - " obs_label='cell_cycle_phase',\n", - " value_to_subset='G1/S/G2/M', # Only look at cycling cells\n", - " hist_kwargs={'bins': 50, 'color': 'black'},\n", - " scatter_density=True\n", + " feature=\"pp21 (nuc median)\",\n", + " obs_label=\"cell_cycle_phase\",\n", + " value_to_subset=\"G1/S/G2/M\", # Only look at cycling cells\n", + " hist_kwargs={\"bins\": 50, \"color\": \"black\"},\n", + " scatter_density=True,\n", ")\n", - "plt.suptitle('Exploratory: p21 in G1/S/G2/M Cells - Looking for M phase (low p21)')\n", + "plt.suptitle(\"Exploratory: p21 in G1/S/G2/M Cells - Looking for M phase (low p21)\")\n", "plt.show()\n", "\n", "# This helps us see if 2 components are appropriate and where the boundary might be" @@ -464,46 +343,30 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "8a8baa1c", + "execution_count": null, + "id": "20", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First refinement complete!\n", - "\n", - "Updated label distribution:\n", - "cell_cycle_phase\n", - "G1/S/G2 6067\n", - "G0 695\n", - "M 34\n", - "G1/S/G2/M 0\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "# Refine G1/S/G2/M population to separate M phase\n", "seq_gmm.refine_labels_with_gmm(\n", - " feature='pp21 (nuc median)',\n", - " obs_label='cell_cycle_phase',\n", - " value_to_refine='G1/S/G2/M',\n", + " feature=\"pp21 (nuc median)\",\n", + " obs_label=\"cell_cycle_phase\",\n", + " value_to_refine=\"G1/S/G2/M\",\n", " n_components=2,\n", - " ordered_labels=['G1/S/G2','M'],\n", - " operation_name='separate_M_phase',\n", - " duplicate_labels=False\n", + " ordered_labels=[\"G1/S/G2\", \"M\"],\n", + " operation_name=\"separate_M_phase\",\n", + " duplicate_labels=False,\n", ")\n", "\n", "print(\"First refinement complete!\")\n", - "print(f\"\\nUpdated label distribution:\")\n", - "print(seq_gmm.adata.obs['cell_cycle_phase'].value_counts())" + "print(\"\\nUpdated label distribution:\")\n", + "print(seq_gmm.adata.obs[\"cell_cycle_phase\"].value_counts())" ] }, { "cell_type": "markdown", - "id": "76724e7d", + "id": "21", "metadata": {}, "source": [ "### Visualize Results" @@ -511,62 +374,40 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "82b6a836", + "execution_count": null, + "id": "22", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the M phase separation\n", "seq_gmm.plot_hist_distribution_with_boundaries(\n", - " operation_name='separate_M_phase',\n", + " operation_name=\"separate_M_phase\",\n", ")\n", - "plt.title('First Refinement: Separate M Phase (p21)')\n", + "plt.title(\"First Refinement: Separate M Phase (p21)\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "e0621660", + "execution_count": null, + "id": "23", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Strip plot\n", - "hist_kwargs = {\"bins\": 100, 'color': 'black'}\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", "seq_gmm.plot_strip_plot_histogram_with_decision_boundaries(\n", - " operation_name='separate_M_phase',\n", + " operation_name=\"separate_M_phase\",\n", " hist_kwargs=hist_kwargs,\n", " scatter_density=False,\n", - " title='First Refinement: Separate M Phase (p21)'\n", + " title=\"First Refinement: Separate M Phase (p21)\",\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "670c3786", + "id": "24", "metadata": {}, "source": [ "---\n", @@ -578,7 +419,7 @@ }, { "cell_type": "markdown", - "id": "15d3de39", + "id": "25", "metadata": {}, "source": [ "### Exploratory Visualization\n", @@ -588,32 +429,21 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "300264c7", + "execution_count": null, + "id": "26", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Explore DNA content distribution in G1/S/G2 cells only\n", "fig, (ax_strip, ax_hist) = seq_gmm.plot_feature_strip_plot_exploratory(\n", - " feature='Int_Intg_DNA_nuc',\n", - " obs_label='cell_cycle_phase',\n", - " value_to_subset='G1/S/G2', # Only look at G1/S/G2 cells (not M or G0)\n", - " hist_kwargs={'bins': 50, 'color': 'mediumseagreen'},\n", + " feature=\"Int_Intg_DNA_nuc\",\n", + " obs_label=\"cell_cycle_phase\",\n", + " value_to_subset=\"G1/S/G2\", # Only look at G1/S/G2 cells (not M or G0)\n", + " hist_kwargs={\"bins\": 50, \"color\": \"mediumseagreen\"},\n", " scatter_density=True,\n", - " x_axis_limits=(3, 15)\n", + " x_axis_limits=(3, 15),\n", ")\n", - "plt.suptitle('Exploratory: DNA Content in G1/S/G2 Cells - Looking for 3 phases')\n", + "plt.suptitle(\"Exploratory: DNA Content in G1/S/G2 Cells - Looking for 3 phases\")\n", "plt.show()\n", "\n", "# From this plot, we can:\n", @@ -624,48 +454,29 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "be34b921", + "execution_count": null, + "id": "27", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Second refinement complete!\n", - "\n", - "Final label distribution:\n", - "cell_cycle_phase\n", - "G1 3968\n", - "S 1151\n", - "G2 948\n", - "G0 695\n", - "M 34\n", - "G1/S/G2 0\n", - "G1/S/G2/M 0\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "# Refine G1/S/G2 population to separate individual phases\n", "seq_gmm.refine_labels_with_manual_thresholds(\n", - " feature='Int_Intg_DNA_nuc',\n", - " obs_label='cell_cycle_phase',\n", - " value_to_refine='G1/S/G2',\n", + " feature=\"Int_Intg_DNA_nuc\",\n", + " obs_label=\"cell_cycle_phase\",\n", + " value_to_refine=\"G1/S/G2\",\n", " manual_thresholds=[6, 9],\n", - " ordered_labels=['G1', 'S', 'G2'],\n", - " operation_name='separate_G1_S_G2',\n", + " ordered_labels=[\"G1\", \"S\", \"G2\"],\n", + " operation_name=\"separate_G1_S_G2\",\n", ")\n", "\n", "print(\"Second refinement complete!\")\n", - "print(f\"\\nFinal label distribution:\")\n", - "print(seq_gmm.adata.obs['cell_cycle_phase'].value_counts())" + "print(\"\\nFinal label distribution:\")\n", + "print(seq_gmm.adata.obs[\"cell_cycle_phase\"].value_counts())" ] }, { "cell_type": "markdown", - "id": "1d7005a3", + "id": "28", "metadata": {}, "source": [ "### Visualize Results" @@ -673,65 +484,39 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "135b4f3a", + "execution_count": null, + "id": "29", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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uvPNOmzRpkt111102c+bM/N+oM9+ifl9Z0rTVwVkHR1VxlZVI9ZACpjR/Z2mclerE5D//+Y+rqVAVbdho2SmkIvtOvL9N1ZY66EcHYHHceuutriP4uHHj7LLLLiv25yIBoksX6uReXNoHtA4UzCo9FeWkk05yARgpGRem36rSrYKyMIXmtGnT9hqAulmFgmPq1Kn7vNGEThBr1qxZ5LFHtUiFa0f2Z7tuUQbHin3tQ1qPsrd1Uloq/TVAXTOKdXYRuaYWCZMLLrjAbWTaEAuPr9eR6widOnVyIamdUFUbhccTVQ3pTO3vf/97gXEUmqqf//3vfx/379BOIKqyjKb52B86Y/3jH//oqrZUHSw66Oq6qd4rTL9n165dVlY0bVVz6RpTYZpu4WVeUiplRs7aYx3IVZVUEjqxKWoedfISqULfG91VR9ffbr75Zlc9VtYlzqLoTiiqqi9s/vz5bn+It/pdFHoqMTz55JP2r3/9K+Y4sX7fgw8+aGPGjLE77rjD3ZkmHrp+rNqfeO6W8vPPP7uw0bVinSTu66AcuRao3xVN1Y26DqbtWpdcCg+qhVK1aFF3nREFlkq+OnbEunuVToxVsvzuu+/cMez00093yzb6evP69etd0OraaqRKtTS260vK4FgRKRwUtR8tXLjQVZ9HV+OWlUpfAtStj1Qnfv7557szQV0HnDt3rs2YMSP/NkKRM5l77rnHhgwZ4jYcXURWvb/ONnQWqGtnqi7TGZSuiZxzzjku5PR5BZ7OrpYtW5a/IWiHVWipgUb//v3dDqWNVyuuJPft1LR01qtrMbpGdtxxx9ns2bMLXFMoKR1QFKQ6M58+fbo701YDIZ2tqyGPDiA6EKoK5qWXXnLLp6yqH3Q2rUAePXq0C2TtzCql6VqDqpIeeeSRAg129sf48ePdAUGlAh1gVLLRgUI7tA4mqoaMl5aVtg9tS6o2U9BGqupOPfVU9+++GsKo4ZS2OW1jHTp0cNe8VG2v5aADqpZDrOspjz32mDtofP/99+71a6+95n5HZD+IXHPTLa203ao0pfVbFN3aTqUX7TuREFGjqcmTJ7vruQqjiDlz5rhqdJVy9rV9qySjW7ZpH9M+otKDSpQKEJX+FRiREz7RslAVm6pdddavz0dToOqEoSiaV21H+m5dRy1MjZ0i36nSjK4vahlrfnQ9UttjcbZbDe+9916B9xU6CnQ1wotFJ8NVq1Z1yznSUCsWBZxOntQITu0ZtG9qma1evdrNq44/2k5E297bb7/ttu3rr7/efb9O9FQNq2uVJdG5iO26LI4V+g7Rb9XlCYV65LeJfpv2jfK4Bljpu0HoTifqYqBm3OofFrktmm53FOtOMOo8rc7ratqrQZ9Ts9yVK1fu0bdI/VUiHU3VF0b9aKK988477jZhapKvzu3quF5UR/jCd7qINFGO7jOopuLqb6imxJpmaXSEj1DHezVbjnTUVidg9dnSstIyU38ddQ/QbasK98UrbjcIzXNhkd8fq++Tukdo2WkZq7uCurCoOffepl24mfq+fr+aiqtbipqy6+4c6kd49tlnu2brEUU1bY/cDCC6a4W6DmieNM+FOwwXtxtEhPqI3Xrrra6fopaD+nGpA7HmV33QCovVYTn6BgUR2k71nrqW7I1uj6fp6yYC6hCu7kDqW3bxxRfvcVMBNWPXd6ofWHFoW9aNI9Q1SfuGvlvrQMteXV2i+/ZFtpGihujlXxT161TftMI3tYheZvq75kVN/tUHVbejiyXW9lX45hCRbUXbrfqj7o1u1KEuU7m5uXsdT8tEXTq6d+/u+q9qe9X89+vXb48uElo/uuOOjnnqK6nuGtHdhUpzu95WjGPF3o4/hY9f+p06PqsLjtZJ9PFBN67Qax1by0PC/80ggAOEqq10Zq6qzNKiEppuYq0aCd2kOWxUra7W3vrtsarXUTncdNNNroZA1aDlUQKs9NcAAfxG57OqrlRVVmlfa1d3kjCGn6gaTdWfqvbmcUiV0+bNm+2pp55y2265VH+qQQ4lQACAjygBAgC8RAACALxEAAIAvEQAAgC8VCk6wutOCOr8q47r5dU6CAAQvlbOumm4bpKum5Z4EYAKv5Lc3w4HhmrVatidd/x6G7d7R3W03NyfK3qWUM6SqyXazDt6u/+fMWqG/ZIb+2kS8MOaNWvivvF5pQ3AyKNKFvzvGkupFf997lC57dqdZy+9/+tjVxZ/v9yqVqMWwDd5uTttw2O/3mz6+5kzLTHxt6dowB9Z2dnW+KyzivXsxAMmACPVngq/2ikEoG9yd+nRRr8GYErtOlatOgHom7ydOf+3BZjVqZViiVUJQJ8llNKlMBrBAAC8RAACALxEAAIAvFQprgECwIFMV7l3VvRMhICu7CaW4/QIQACoQAq+VQkJlqd+bT73cw4CN6Tm5VmaGrqUwyQJQACoIHoY6w8q9dSoYY0POcSqeByAQRDYjpwc27Bpk1lurjUqh2kSgABQQXaZ2Y7ERDu0fn2rmZzs/Xqo8X/LYMP69XZIXl6ZV4eWqBGMHjrZrFkzS05Otq5du+71ydNTpkxxfTaiB30OAHwXuZ9N9Wr0a4yoqYcuJyRYrpW9uANwxowZNnjwYMvMzLRFixZZhw4drGfPnrZhQ6Sb6p7q1KljP/zwQ/7w7bff7u98A8CBQQWDip6HsHVyL6eq4LgDcOzYsTZgwADr16+ftWnTxiZMmGA1a9a0yZMn7/UHpaWl5Q8NGzbc3/kGAKD8rgHu3LnTFi5caEOGDMl/T3fk7tGjh82bN6/Iz23fvt2aNm3qnurQqVMnGzVqlLVt23b/5hwADlDr1q+3LVu2lNv0UlNTLc3DgklcAbhp0ybbvXv3HiU4vV6xYkXMzxx55JGudNi+fXvbunWrjRkzxo477jhbtmxZkXfzzsnJcUNEVlZWPLMJAJU6/M7tdYVt3/LbMbCspaQm2av/nBp3CGpeR48ZY/87a5Z9t3at1a1Tx1q2aGFX9O5tfS+/3NUOPjl5sk174QVb9PHH7lFGP333nQvcMCjzVqDdunVzQ4TC76ijjrKJEyfayJEjY35m9OjRNmLEiLKeNQAIHZX8FH49qo2wBkmHl/n0NuWssne2ZLrpxhOAX69aZcf36GGpdevaqMxMO7ptW0tKSrKly5bZk888Y+mHHmrnnnWW7fj5ZzvjtNPcMCTz1yd6hEVcAdigQQNLTEy09evXF3hfr3VtrziqVatmxxxzjH355ZdFjqMqVjW0iS4B8jxAAD5R+KUlty6fieXG/5Hrb77Zqlatags++MBq1aqV/37zww+3884+2/Xrk5tuuMH9O+f99y1s4moEU716devcubPNnj07/z1d19Pr6FLe3qgKdenSpdaoUdHdHHUWoZaj0QMAIBw2b95sb82ebTdce22B8CuLRxaFqhWoSmaTJk2yv//977Z8+XK77rrrLDs727UKlT59+hRoJHP33XfbW2+9ZV9//bXrNnHFFVe4bhDXXHNN6f4SAEC5+PLrr10J78hWrQq836BJE0tp2NANtw0dGvq1Efc1wN69e9vGjRtt2LBhtm7dOuvYsaPNnDkzv2HM6tWrXcvQiJ9++sl1m9C49erVcyXIuXPnui4UKFvpGcU7A1u74NeqCgDYH/Pfe8/VCl7ev3+BhoxhVaJGMAMHDnRDLHPmzCnw+uGHH3YDAODA0LJ5c1fFufKLLwq8r+t/0bc0CzueBwgAiMtBBx1kp51yij02caK7BFZZEYAAgLg9/vDDtmvXLsvo3t1mvPSSLV+xwlZ+/rlNnT7dVnz+uesxEOkruOSTT9x1Q1E3Cb3+8ccfraLxNAgACCH1zwvzdFo0b26L5861UQ8+aEOGD3cd4dWCv03r1nbLoEF2/YABbrwJTz1lI0aPzv/ciT17un+fmTDBrrriCqtIBCAAhIjukqI7s6hzerk8EsF+vRNMSe7O0igtzR596CE3FGX4nXe6IYwIQAAIEd2NRbcl416gZY8ABIAQhqCPN6cubzSCAQB4iQAEAHiJAAQAeIkABAB4iQAEAHiJAAQAeIkABAB4iX6AABAyun8mHeHLHgEIACELv3OvuNS25/xcbtNMSaphr06dHlfne/dc2Hvusf+dNcvWb9hg9VJTrcPRR9uw22+347t1s8qAAASAEFHJT+HXY8SV1uDwtDKf3qZV6+ydzGfddOMJwAsvv9x25uba3ydOdM8BVAjOnjPHNofgKQ/FRQACQAgp/Bq1bmphtGXLFvtg7lyb8+abdlL37u69pk2aWJeMDKtMaAQDAIhLSkqKG/75+uuWk5NjlRUBCACIS9WqVW3KhAn292nTLDU93Y7v0cPuGD7cPvn0U6tMCEAAQNwu7NXLvv/iC3v1hRfsjB49bM4HH1in44+3KVOnWmVBAAIASiQ5OdlOO+UUG3r77TZ39mz3hPfMe++1yoIABACUijatW1t2drZVFrQCBQDEZfPmzXbxlVfa1X36WPt27ax2SootWLTIHnj4YTvv7LOtsiAAASCE1D8vrNNJSUmxrsceaw8/9ph9tWqV5ebmWuP0dBtw1VV2x623WmVBAMLSMxKKtRTWLghYWkAZS01NdXdmUef08pKSVMNNt7iSkpJs9IgRbqjMCEAACBHdjUW3JeNeoGWPAASAEIZgPLclQ8nQChQA4CUCEADgJQIQAOAlAhAA4CUCEADgJQIQAOAlAhAA4CX6AQJAyKxbv56O8OWAAASAkIXfRb162S9btpTbNJNTU+2lf/4z7s73mtfRY8bY/86aZd+tXWt169Sxli1a2BW9e1vfyy+3X375xT0e6a1//9tWr1ljBzdoYL3OPttGDh1qdevWtYpGAAJAiOgWaAq/kdWq2eFJSWU+vVU5OTZ0yxY33XgC8OtVq9yT4FPr1rVRmZl2dNu27h6hS5ctsyefecbSDz3UmjdrZt//8IONufde96ikb1evtj/ddJN776XnnrOKRgACQAgp/FonJ5fPxHJz4/7I9TffbFWrVrUFH3xgtWrVyn+/+eGHu0ciBUFgCQkJ9o9p0/L/1qJ5c7t32DC74pprbNeuXe7zFYlGMACAuJ8H+Nbs2XbDtdcWCL9oCr9YtmZlWZ3atSs8/IQABADE5cuvv3YlvCNbtSrwfoMmTSylYUM33DZ06B6f27Rpk428/367tl8/CwMCEABQKua/954tmTvX2h51lOXk5BT4W1ZWlp110UXuWuDwO++0MKj4MigAoFJp2by5q+Jc+cUXBd7X9T+pUeja5bZt2+yM88+32ikp9srzz1u1atUsDCgBAgDictBBB9lpp5xij02caNnZ2XsdVyW/0887z6pXq2avvvCCJZdXw55iIAABAHF7/OGHXUvOjO7dbcZLL9nyFSts5eef29Tp023F559bYmJifvgpJJ9+/HHL2rbN9R3UsHv3bqtoVIECQAipf16Yp9OieXNbPHeujXrwQRsyfLjrCK9+gLrGd8ugQXb9gAE2f8EC++i//3Xjt2zfvuB0ly2zZk2bWkUiAAEgRFJTU92dWdQ5vST980oiOTXVTTdejdLS7NGHHnJDLCefeKIF27dbWBGAABAiuhuLbkumO7OUl9TU1Lhvg3YgIAABIGQURj4GUnmjEQwAwEsEIADASwQgAFSkILCANZBPt1jTMikPBCAAVJDE//t3Zzm19qwMdqhbRhBYedwrhkYwAFBBdACuuXu3bfzxR6tWtapVKeIJCr6U/Hbk5NiGTZssNS8v/+SgLBGAAFBBFHeN1Cn855/t2zVr9Awhf9dF8GvVp8IvrZwmSQACQAWqbmatgsB2huDWYBVN1Z7lUfKLIAABoIKpMUZ4bhHtDxrBAAC8RAACALxEAAIAvEQAAgC8RAACALxEK9BKKj3D4/5CAFAKKAECALxUogAcP368NWvWzJKTk61r1642f/78Yn1u+vTplpCQYL169SrJZAEAqLgAnDFjhg0ePNgyMzNt0aJF1qFDB+vZs6dt2LBhr5/75ptv7JZbbrHu3bvvz/wCAFAxATh27FgbMGCA9evXz9q0aWMTJkywmjVr2uTJk4v8zO7du+3yyy+3ESNGWPPmzfd3ngEAKN8A3Llzpy1cuNB69Ojx2xdUqeJez5s3r8jP3X333XbIIYdY//79929uAQCoiFagmzZtcqW5hg0bFnhfr1esWBHzMx9++KE9/fTTtmTJkmJPJycnxw0RWVlZ8cwmAAAV2wp027ZtduWVV9qkSZOsQYMGxf7c6NGjrW7duvlD48aNy3I2AQAeiqsEqBBLTEy09evXF3hfr9PS9nyC01dffeUav5xzzjn57+Xl5f064apVbeXKldaiRYs9PjdkyBDX0Ca6BEgIAgAqLACrV69unTt3ttmzZ+d3ZVCg6fXAgQP3GL9169a2dOnSAu/dddddrmT4yCOPFBlqSUlJbgAAIDR3glHJrG/fvpaRkWFdunSxcePGWXZ2tmsVKn369LH09HRXjal+gu3atSvw+dTUVPdv4fcBAAh1APbu3ds2btxow4YNs3Xr1lnHjh1t5syZ+Q1jVq9e7VqGAgBwwN0LVNWdsao8Zc6cOXv97JQpU0oySQAAShVFNQCAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXqlb0DKCg9IwEFgkAlANKgAAAL1ECRKmXTtcuCFiqAEKPEiAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEslCsDx48dbs2bNLDk52bp27Wrz588vctyXX37ZMjIyLDU11WrVqmUdO3a0Z599dn/mGQCA8g/AGTNm2ODBgy0zM9MWLVpkHTp0sJ49e9qGDRtijl+/fn278847bd68efbJJ59Yv3793DBr1qz9n3sAAMorAMeOHWsDBgxwIdamTRubMGGC1axZ0yZPnhxz/JNPPtnOP/98O+qoo6xFixY2aNAga9++vX344YclnWcAAMo3AHfu3GkLFy60Hj16/PYFVaq41yrh7UsQBDZ79mxbuXKlnXjiiSWbYwAAyvuJ8Js2bbLdu3dbw4YNC7yv1ytWrCjyc1u3brX09HTLycmxxMREe/zxx+20004rcnyNpyEiKysrntkEAKB0A7CkateubUuWLLHt27e7EqCuITZv3txVj8YyevRoGzFiRHnMGgDAU3EFYIMGDVwJbv369QXe1+u0tLQiP6dq0pYtW7r/qxXo8uXLXcgVFYBDhgxxIRldAmzcuHE8swoAQOldA6xevbp17tzZleIi8vLy3Otu3boV+3v0megqzsKSkpKsTp06BQYAACq0ClQls759+7q+fV26dLFx48ZZdna2axUqffr0cdf7VMIT/atx1QJUoffGG2+4foBPPPFEqf4QAADKNAB79+5tGzdutGHDhtm6detclebMmTPzG8asXr3aVXlGKByvv/56++6776xGjRrWunVrmzp1qvseAAAqVSOYgQMHuiGWOXPmFHh9zz33uAEAgDDhXqAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAAL1Wt6BnwRXpGQkXPAgAgCiVAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJe4Fygq5L6naxcELHkAFYoSIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASyUKwPHjx1uzZs0sOTnZunbtavPnzy9y3EmTJln37t2tXr16bujRo8dexwcAIJQBOGPGDBs8eLBlZmbaokWLrEOHDtazZ0/bsGFDzPHnzJljl112mb377rs2b948a9y4sZ1++um2du3a0ph/AADKJwDHjh1rAwYMsH79+lmbNm1swoQJVrNmTZs8eXLM8Z977jm7/vrrrWPHjta6dWt76qmnLC8vz2bPnl2yOQYAoLwDcOfOnbZw4UJXjZn/BVWquNcq3RXHjh07LDc31+rXr1/kODk5OZaVlVVgAACgwgJw06ZNtnv3bmvYsGGB9/V63bp1xfqO2267zQ499NACIVrY6NGjrW7duvmDqk0BAKi0rUDvu+8+mz59ur3yyiuuAU1RhgwZYlu3bs0f1qxZU56zCQDwQFxPg2jQoIElJiba+vXrC7yv12lpaXv97JgxY1wAvvPOO9a+ffu9jpuUlOQGAABCUQKsXr26de7cuUADlkiDlm7duhX5uQceeMBGjhxpM2fOtIyMjP2bYwAAKuJ5gOoC0bdvXxdkXbp0sXHjxll2drZrFSp9+vSx9PR0dx1P7r//fhs2bJhNmzbN9R2MXCtMSUlxAwAAFSHuAOzdu7dt3LjRhZrCTN0bVLKLNIxZvXq1axka8cQTT7jWoxdddFGB71E/wuHDh5fGbwAAoHyeCD9w4EA3FNXxPdo333xTkkkAAFCmuBcoAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADAS1Uregbgp/SMhGKNt3ZBUObzAsBPBGA5HswBAOFBFSgAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEsEIADASwQgAMBLBCAAwEslCsDx48dbs2bNLDk52bp27Wrz588vctxly5bZhRde6MZPSEiwcePG7c/8AgBQMQE4Y8YMGzx4sGVmZtqiRYusQ4cO1rNnT9uwYUPM8Xfs2GHNmze3++67z9LS0kpjngEAKP8AHDt2rA0YMMD69etnbdq0sQkTJljNmjVt8uTJMcc/9thj7cEHH7RLL73UkpKS9n+O4ZX0jARr1T0l/3Wr2umWXqXeHgMAlGkA7ty50xYuXGg9evT47QuqVHGv582bZ6UlJyfHsrKyCgwAAFRYAG7atMl2795tDRs2LPC+Xq9bt67UZmr06NFWt27d/KFx48al9t0AAIS2FeiQIUNs69at+cOaNWsqepYAAAeYqvGM3KBBA0tMTLT169cXeF+vS7OBi64Vcr0QABCaEmD16tWtc+fONnv27Pz38vLy3Otu3bqVxfwBAFDxJUBRF4i+fftaRkaGdenSxfXry87Odq1CpU+fPpaenu6u40Uaznz22Wf5/1+7dq0tWbLEUlJSrGXLlqX9ewAAKJsA7N27t23cuNGGDRvmGr507NjRZs6cmd8wZvXq1a5laMT3339vxxxzTP7rMWPGuOGkk06yOXPmxDt5AAAqJgBl4MCBboilcKjpDjBBEJRs7gAA8KkVKAAAZY0ABAB4iQAEAHiJAAQAeIkABAB4qUStQAEA+y8hI6NY4wULFrC4ywAlQACAlwhAAICXCEAAgJcIQACAlwhAAICXCEAAgJfoBgEAFdjFARWHAASAkKO/YNkgAAEgDpTsDhwEIAAQbF4iAAFPpVepV6zx1ub9VObzAlQEWoECALxECRDelGYoyZTdsi3t5Vt4msnVEm3mHb3d/1O6n2C/5O52/+cm0dgfBCBQiVBtCZQeAhA4AEudxQ3K8p4mpXCECdcAAQBeogQI4IAumQJFIQD3Ij0jYW9/BoBK10mfhkO/oQoUAOAlAhAA4CWqQOGNMPRnK49pAigeAhAohIYagB8IQADwCI9W+g0BCKDS4tFE2B80ggEAeIkABAB4iQAEAHiJAAQAeIkABAB4iQAEAHiJAAQAeIkABAB4iQAEAHiJO8EAIcD9R4Hy52UA8qBbAABVoAAALxGAAAAvEYAAAC8RgAAALxGAAAAvEYAAAC8RgAAAL3nZDxAAsHcJGRlWHMGCBVZZUQIEAHiJAAQAeIkABAB4iQAEAHiJRjAAgDJtLBPWhjIHXADypAcAQHFQBQoA8BIBCADwEgEIAPASAQgA8BIBCADwEgEIAPDSAdcNAgAQLgkhvbF2pQrA1ifXrehZAABUcFBWaBXo+PHjrVmzZpacnGxdu3a1+fPn73X8F1980Vq3bu3GP/roo+2NN94o6fwCAFAxAThjxgwbPHiwZWZm2qJFi6xDhw7Ws2dP27BhQ8zx586da5dddpn179/fFi9ebL169XLDp59+WhrzDwBA+QTg2LFjbcCAAdavXz9r06aNTZgwwWrWrGmTJ0+OOf4jjzxiZ5xxht1666121FFH2ciRI61Tp0722GOPlWyOAQAo7wDcuXOnLVy40Hr06PHbF1Sp4l7Pmzcv5mf0fvT4ohJjUeNLTk6OZWVl5Q9bt26NZzYBAAewIAjKvxHMpk2bbPfu3dawYcMC7+v1ihUrYn5m3bp1McfX+0UZPXq0jRgxIp5ZwwEsN/dnGz7iyIqeDVSgX3J328kjprEO4GzevNnq1q17YLYCHTJkiLvOGLFlyxZr2rSprV69ulR+dFlSibVx48a2Zs0aq1OnjoUZ88pyZRtgG8iqRMcs1QY2adLE6tevXyrfF1cANmjQwBITE239+vUF3tfrtLS0mJ/R+/GML0lJSW4oTOEX9hUUoflkXlmubANsA2wDpU+X3krle+IZuXr16ta5c2ebPXt2/nt5eXnudbdu3WJ+Ru9Hjy9vv/12keMDAFAe4q4CVdVk3759LSMjw7p06WLjxo2z7Oxs1ypU+vTpY+np6e46ngwaNMhOOukke+ihh+yss86y6dOn24IFC+zJJ58s/V8DAEBZBWDv3r1t48aNNmzYMNeQpWPHjjZz5sz8hi66ThddPD3uuONs2rRpdtddd9kdd9xhrVq1sn/+85/Wrl27Yk9T1aHqdxirWjRsmFeWK9sA2wDbQOVYrglBabUnBQCgEuFpEAAALxGAAAAvEYAAAC8RgAAAL4U2AJ944glr3759fody9Rt88803rTK47777LCEhwW666SYLo+HDh7v5ix70uKowWrt2rV1xxRV20EEHWY0aNdzjtNSNJoz0iLDCy1XDDTfcYGGjWxoOHTrUDj/8cLdcW7Ro4W5UH8Y2cdu2bXP7ku4GpXlVy/L//ve/Fgbvv/++nXPOOXbooYe6da0W7tG0PNVivlGjRm7edV/kL774IpTz+vLLL9vpp5/u9jX9fcmSJRUyn/ua19zcXLvtttvcsaBWrVpuHHW/+/777w+cADzssMNckOjm2zrgnXLKKXbeeefZsmXLLMy0Y06cONGFd5i1bdvWfvjhh/zhww8/tLD56aef7Pjjj7dq1aq5k5/PPvvM9SetV6+ehXXdRy9T3fBBLr74Ygub+++/351k6qksy5cvd68feOABe/TRRy1srrnmGrcsn332WVu6dKk7SCtIdHJU0dQHWo+E0zNSY9Ey/dvf/uaemvPRRx+5A7YeBvDLL7+Ebl6zs7PthBNOcNtCRdvbvO7YscM9ik8ncPpXwb1y5Uo799xz459QUInUq1cveOqpp4Kw2rZtW9CqVavg7bffDk466aRg0KBBQRhlZmYGHTp0CMLutttuC0444YSgstL6b9GiRZCXlxeEzVlnnRVcffXVBd674IILgssvvzwIkx07dgSJiYnB66+/XuD9Tp06BXfeeWcQJjqcvvLKK/mvtd7T0tKCBx98MP+9LVu2BElJScHzzz8fhGleo61atcr9ffHixUEY7G1eI+bPn+/G+/bbb+P67tCWAAtX1+gOMjorCPMt1FTVpbvdFH78UxipGkZVB82bN7fLL7/c3cAgbF599VV3xyGVoA455BA75phjbNKkSVYZ6NFhU6dOtauvvtpV4YSNqhF1i8LPP//cvf74449dLcCZZ55pYbJr1y63/ycnJxd4X9WJYay1iLZq1Sp3s5Do44HuZ9y1a9e9Pg4OJbtJtvaz1NTUyv80iAhVdyjwVF2QkpJir7zyinsIbxgpoFUcD8u1ib3RDjhlyhQ78sgjXVWdHj3VvXt3+/TTT6127doWFl9//bWrptPt93QXIS3bG2+80d2TVrfjCzNds9BTTK666ioLo9tvv909BUDXfnWDe4XMvffe606GwkTbo44Buj6pB2rrjlPPP/+8C5CWLVtamEUe+Rbv4+AQH+WDrgledtllcd94PNQBqAO0LsQq3V966SV30HvvvfdCF4J6jIjuearrFIXPVMMo+ixf1yoViGpg8MILL1j//v0tLHSjdZUAR40a5V6rBKiQ1vWUsAfg008/7ZazStlhpHX93HPPudsU6nqw9jM1NNH8hm3Z6tqfStK6x7DCulOnTu5gp/YB8Ftubq5dcsklrrGRTpbjFeoqUJ3p6yxPT6DQzbV1UfSRRx6xsNGOuGHDBrdjVq1a1Q0Kal381v91dh1mqjY44ogj7Msvv7QwUcu5wic7KgWEsbo22rfffmvvvPOOa7wRVrfeeqsrBV566aWuNd2VV15pN998c/5N7MNELVS1P23fvt2dbM6fP98d+FR9H2aRR77F+zg4xBd+2t9U+CjJY6dCHYCxSgQ5OTkWNqeeeqqrrtVZdGRQyUXVSfq/zlrDTAeWr776ygVOmKgFqFp3RdM1K5VWw+yZZ55x1yx1PTis1JKu8DPVtJ1qHwsrtaDUNqrWwbNmzXKtwsNMXUwUdNGPg1O1s1qDhrktQ2UKP7Vl0Mmmum6URGirQPVUeFUh6em/6gekqpo5c+a4DT9sdJ2i8NMttLNqpcTz1Ivycsstt7g+NgoS9Z3R3dV18FO1UpioRKLGGqoC1cauM389RivMj9JSgCgAVY2o0n9Yaf3rmp/2L1WBLl682MaOHeuqGsNG+7yquHRJRLUUKr3q2mXkEWwVffIYXXOihi866dUTy7VsVa18zz33uKfgKBDVdF/VzL169QrdvP7444+udiXSny5y8qkQL+8S697mVSdBF110kWtz8frrr7satsg1Vf1dNYfFFoSUmmg3bdo0qF69enDwwQcHp556avDWW28FlUWYu0H07t07aNSokVu26enp7vWXX34ZhNFrr70WtGvXzjUdb926dfDkk08GYTZr1izXHHvlypVBmGVlZbnts0mTJkFycnLQvHlz160gJycnCJsZM2a4+dP2qm4FN9xwg+tOEAbvvvuuW9+Fh759++Z3hRg6dGjQsGFDtw3rOFZR28a+5vWZZ56J+Xd1mwrTvEa6acQa9Ll48DgkAICXKtU1QAAASgsBCADwEgEIAPASAQgA8BIBCADwEgEIAPASAQgA8BIBCADwEgEIAPASAQgA8BIBCADwEgEIADAf/X936kPEzS8RmAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "hist_kwargs = {\"bins\": 100, 'color': 'black'}\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", "# Plot the G1/S/G2 separation\n", "seq_gmm.plot_hist_distribution_with_boundaries(\n", - " operation_name='separate_G1_S_G2',\n", - " hist_kwargs=hist_kwargs,\n", - " x_axis_limits=(3, 12)\n", + " operation_name=\"separate_G1_S_G2\", hist_kwargs=hist_kwargs, x_axis_limits=(3, 12)\n", ")\n", - "plt.title('Second Refinement: G1, S, G2 (DNA Content)')\n", + "plt.title(\"Second Refinement: G1, S, G2 (DNA Content)\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "3000e542", + "execution_count": null, + "id": "30", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Strip plot\n", - "hist_kwargs = {\"bins\": 100, 'color': 'black'}\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", "seq_gmm.plot_strip_plot_histogram_with_decision_boundaries(\n", - " operation_name='separate_G1_S_G2',\n", - " hist_kwargs=hist_kwargs,\n", - " scatter_density=False\n", + " operation_name=\"separate_G1_S_G2\", hist_kwargs=hist_kwargs, scatter_density=False\n", ")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "cfb977ce", + "execution_count": null, + "id": "31", "metadata": {}, "outputs": [], "source": [ @@ -740,7 +525,7 @@ }, { "cell_type": "markdown", - "id": "f3f0ef7a", + "id": "32", "metadata": {}, "source": [ "---\n", @@ -750,7 +535,7 @@ }, { "cell_type": "markdown", - "id": "9e72ac6d", + "id": "33", "metadata": {}, "source": [ "### Generate Thresholding Report" @@ -758,69 +543,19 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "95acbb4c", + "execution_count": null, + "id": "34", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Thresholding Report\n", - "==================================================\n", - "\n", - "1. initial_pRB_split (Standard Thresholding)\n", - "--------------------------------------------\n", - " Feature: pRB (nuc median)\n", - " Layer: None\n", - " Obs column: cell_cycle_phase\n", - " Components: 2\n", - " Thresholds: [0.0083]\n", - " Labels: ['G0', 'G1/S/G2/M']\n", - " Cell counts: G1/S/G2/M=6101, G0=695\n", - "\n", - "2. separate_M_phase (Refinement) of cell_cycle_phase\n", - "----------------------------------------------------\n", - " Feature: pp21 (nuc median)\n", - " Layer: None\n", - " Obs column: cell_cycle_phase\n", - " Components: 2\n", - " Thresholds: [0.0067]\n", - " Labels: ['G1/S/G2', 'M']\n", - " Refined from: ['G1/S/G2/M']\n", - " Cell counts: G1/S/G2=6067, M=34\n", - "\n", - "3. separate_G1_S_G2 (Refinement) of cell_cycle_phase\n", - "----------------------------------------------------\n", - " Feature: Int_Intg_DNA_nuc\n", - " Layer: None\n", - " Obs column: cell_cycle_phase\n", - " Components: N/A (manual thresholds)\n", - " Thresholds: [6.0000, 9.0000]\n", - " Labels: ['G1', 'S', 'G2']\n", - " Refined from: ['G1/S/G2']\n", - " Cell counts: G1=3968, S=1151, G2=948\n", - "\n", - "==================================================\n", - "Total operations: 3\n", - "Operation types:\n", - " - standard: 1\n", - " - refinement: 1\n", - " - refinement_manual: 1\n" - ] - } - ], + "outputs": [], "source": [ "# Generate a text report using the method from the base class\n", - "report = seq_gmm.generate_thresholding_report(\n", - " output_format='text'\n", - ")\n", + "report = seq_gmm.generate_thresholding_report(output_format=\"text\")\n", "print(report)" ] }, { "cell_type": "markdown", - "id": "3d29eb18", + "id": "35", "metadata": {}, "source": [ "Or as a DataFrame for further analysis:" @@ -828,123 +563,19 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "45566d5d", + "execution_count": null, + "id": "36", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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OperationTypeFeatureLayerObs LabelComponentsThresholdsLabelsParentRefined FromTotal Cells
01. initial_pRB_splitstandardpRB (nuc median)Nonecell_cycle_phase20.0083G0, G1/S/G2/MNoneN/A6796
12. separate_M_phaserefinementpp21 (nuc median)Nonecell_cycle_phase20.0067G1/S/G2, Mcell_cycle_phaseG1/S/G2/M6101
23. separate_G1_S_G2refinement_manualInt_Intg_DNA_nucNonecell_cycle_phaseN/A (manual thresholds)6.0000, 9.0000G1, S, G2cell_cycle_phaseG1/S/G26067
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" - ], - "text/plain": [ - " Operation Type Feature Layer \\\n", - "0 1. initial_pRB_split standard pRB (nuc median) None \n", - "1 2. separate_M_phase refinement pp21 (nuc median) None \n", - "2 3. separate_G1_S_G2 refinement_manual Int_Intg_DNA_nuc None \n", - "\n", - " Obs Label Components Thresholds Labels \\\n", - "0 cell_cycle_phase 2 0.0083 G0, G1/S/G2/M \n", - "1 cell_cycle_phase 2 0.0067 G1/S/G2, M \n", - "2 cell_cycle_phase N/A (manual thresholds) 6.0000, 9.0000 G1, S, G2 \n", - "\n", - " Parent Refined From Total Cells \n", - "0 None N/A 6796 \n", - "1 cell_cycle_phase G1/S/G2/M 6101 \n", - "2 cell_cycle_phase G1/S/G2 6067 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Generate a DataFrame report\n", - "report_df = seq_gmm.generate_thresholding_report(\n", - " output_format='dataframe'\n", - ")\n", + "report_df = seq_gmm.generate_thresholding_report(output_format=\"dataframe\")\n", "display(report_df)" ] }, { "cell_type": "markdown", - "id": "295a119d", + "id": "37", "metadata": {}, "source": [ "### Boolean Label Combination\n", @@ -954,55 +585,43 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "7eeb71b4", + "execution_count": null, + "id": "38", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Combined label distribution:\n", - "control_and_G2M\n", - "other 6499\n", - "control_G2M 297\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "from cc_mapping.utils import create_boolean_label_combination\n", "\n", "# Create synthetic treatment labels for demonstration\n", "np.random.seed(42)\n", "n_cells = len(adata_final)\n", - "adata_final.obs['treatment'] = pd.Categorical(\n", - " np.random.choice(['Drug_A', 'Drug_B', 'Control'], size=n_cells)\n", + "adata_final.obs[\"treatment\"] = pd.Categorical(\n", + " np.random.choice([\"Drug_A\", \"Drug_B\", \"Control\"], size=n_cells)\n", ")\n", "\n", "# Combine two categorical labels using boolean operators\n", "# Note: Parameter names are obs_key_1, match_values_1, output_obs_key, true_label, false_label\n", "adata_final = create_boolean_label_combination(\n", " adata_final,\n", - " obs_key_1='treatment',\n", - " match_values_1=['Control'],\n", - " obs_key_2='cell_cycle_phase',\n", - " match_values_2=['G2', 'M'], # Combine G2 and M phases\n", - " operator='AND', # Find cells that are BOTH Control AND in G2 or M phase\n", - " output_obs_key='control_and_G2M',\n", - " true_label='control_G2M',\n", - " false_label='other',\n", - " overwrite=False # Default: raises error if output_obs_key already exists\n", + " obs_key_1=\"treatment\",\n", + " match_values_1=[\"Control\"],\n", + " obs_key_2=\"cell_cycle_phase\",\n", + " match_values_2=[\"G2\", \"M\"], # Combine G2 and M phases\n", + " operator=\"AND\", # Find cells that are BOTH Control AND in G2 or M phase\n", + " output_obs_key=\"control_and_G2M\",\n", + " true_label=\"control_G2M\",\n", + " false_label=\"other\",\n", + " overwrite=False, # Default: raises error if output_obs_key already exists\n", ")\n", "\n", "print(\"Combined label distribution:\")\n", - "print(adata_final.obs['control_and_G2M'].value_counts())" + "print(adata_final.obs[\"control_and_G2M\"].value_counts())" ] }, { "cell_type": "code", "execution_count": null, - "id": "9b1e47a8", + "id": "39", "metadata": {}, "outputs": [], "source": [] diff --git a/docs/source/tutorials/Single_Thresholding_Workflow.ipynb b/docs/source/tutorials/Single_Thresholding_Workflow.ipynb index 755ab7d..3bafb30 100644 --- a/docs/source/tutorials/Single_Thresholding_Workflow.ipynb +++ b/docs/source/tutorials/Single_Thresholding_Workflow.ipynb @@ -28,10 +28,9 @@ "from urllib.request import urlretrieve\n", "\n", "import anndata as ad\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", "from cc_mapping.thresholding import GMMThresholding\n", "from cc_mapping.utils import create_boolean_label_combination\n", @@ -50,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -67,18 +66,9 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results directory created at: c:\\Users\\dap182\\Documents\\git\\cc_mapping\\notebooks\\results\n", - "Single threshold results directory: c:\\Users\\dap182\\Documents\\git\\cc_mapping\\notebooks\\results\\single\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Create results directory structure for saving figures\n", "results_dir = cwd / \"results\"\n", @@ -94,260 +84,9 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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E2F1 (nuc median)cycA (nuc median)cycD1 (nuc median)p21 (nuc median)Int_Intg_DNA_nucSkp2 (nuc median)Cdt1 (nuc median)Nuc areaCdh1 (nuc median)cycE (nuc median)...STAT3 (phospho/total nuc)agephasePHATE_1PHATE_2PCNA fociDNA contentLocal cell densityannotated ageannotated phase
Unnamed: 0.1.1
00.0068510.0058750.0194710.0260325.7468990.0064930.005158553.00.0143360.009262...1.77018610.263173G1-0.0206540.0079070.0051512.2987597.0NaNNaN
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50.0067440.0060270.0090330.0052955.2491190.0104220.007797296.00.0088960.007767...1.2663041.828240G1-0.0190390.0005840.0032772.0996483.0NaNNaN
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5 rows × 299 columns

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" - ], - "text/plain": [ - " E2F1 (nuc median) cycA (nuc median) cycD1 (nuc median) \\\n", - "Unnamed: 0.1.1 \n", - "0 0.006851 0.005875 0.019471 \n", - "1 0.012360 0.028153 0.007462 \n", - "2 0.007279 0.005707 0.006592 \n", - "3 0.006531 0.016602 0.009369 \n", - "5 0.006744 0.006027 0.009033 \n", - "\n", - " p21 (nuc median) Int_Intg_DNA_nuc Skp2 (nuc median) \\\n", - "Unnamed: 0.1.1 \n", - "0 0.026032 5.746899 0.006493 \n", - "1 0.004318 8.852262 0.022797 \n", - "2 0.003632 4.951003 0.015366 \n", - "3 0.006264 10.466743 0.019196 \n", - "5 0.005295 5.249119 0.010422 \n", - "\n", - " Cdt1 (nuc median) Nuc area Cdh1 (nuc median) \\\n", - "Unnamed: 0.1.1 \n", - "0 0.005158 553.0 0.014336 \n", - "1 0.005951 490.0 0.015229 \n", - "2 0.004929 363.0 0.005730 \n", - "3 0.005234 579.0 0.009361 \n", - "5 0.007797 296.0 0.008896 \n", - "\n", - " cycE (nuc median) ... STAT3 (phospho/total nuc) age \\\n", - "Unnamed: 0.1.1 ... \n", - "0 0.009262 ... 1.770186 10.263173 \n", - "1 0.008392 ... 1.455814 12.109644 \n", - "2 0.009117 ... 1.290323 4.954907 \n", - "3 0.007118 ... 1.435065 13.424587 \n", - "5 0.007767 ... 1.266304 1.828240 \n", - "\n", - " phase PHATE_1 PHATE_2 PCNA foci DNA content \\\n", - "Unnamed: 0.1.1 \n", - "0 G1 -0.020654 0.007907 0.005151 2.298759 \n", - "1 S 0.025945 -0.000735 0.006861 3.540905 \n", - "2 G1 0.002961 -0.004575 0.004148 1.980401 \n", - "3 G2 0.034884 0.002944 0.002457 4.186697 \n", - "5 G1 -0.019039 0.000584 0.003277 2.099648 \n", - "\n", - " Local cell density annotated age annotated phase \n", - "Unnamed: 0.1.1 \n", - "0 7.0 NaN NaN \n", - "1 1.0 NaN NaN \n", - "2 6.0 NaN NaN \n", - "3 9.0 NaN NaN \n", - "5 3.0 NaN NaN \n", - "\n", - "[5 rows x 299 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "csv = pd.read_csv(download_path, index_col=0, low_memory=False)\n", "csv.index = csv.index.astype(str)\n", @@ -356,34 +95,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# convert the dataframe into an anndata object\n", "# ensure the data is not of object type (e.g. contains strings)\n", "adata = ad.AnnData(\n", - " X=csv.values[:,:-10].astype(np.float32),\n", + " X=csv.values[:, :-10].astype(np.float32),\n", ")\n", "adata.var_names = csv.columns.values[:-10]" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['E2F1 (nuc median)', 'cycA (nuc median)', 'cycD1 (nuc median)']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "list(adata.var_names)[:3]" ] @@ -412,16 +140,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Initialize the GMM thresholding object for exploration\n", "# Note: We don't need to fit() or categorize_samples() yet for exploratory plots\n", "gmm_explore = GMMThresholding(\n", - " adata=adata, \n", - " feature='cycD1 (nuc median)',\n", - " label_obs_save_str='cell_cycle_explore'\n", + " adata=adata, feature=\"cycD1 (nuc median)\", label_obs_save_str=\"cell_cycle_explore\"\n", ")" ] }, @@ -434,33 +160,22 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Plot histogram without any thresholding\n", "fig, ax = plt.subplots(figsize=(8, 5))\n", "\n", "gmm_explore.plot_feature_distribution_exploratory(\n", - " hist_kwargs={'bins': 100, 'color': 'steelblue', 'alpha': 0.7},\n", + " hist_kwargs={\"bins\": 100, \"color\": \"steelblue\", \"alpha\": 0.7},\n", " x_axis_limits=(0, 0.08),\n", - " ax=ax\n", + " ax=ax,\n", ")\n", "\n", - "ax.set_title('CycD1 Distribution - Exploratory View')\n", - "ax.set_xlabel('CycD1 (nuc median)')\n", - "ax.set_ylabel('Density')\n", + "ax.set_title(\"CycD1 Distribution - Exploratory View\")\n", + "ax.set_xlabel(\"CycD1 (nuc median)\")\n", + "ax.set_ylabel(\"Density\")\n", "plt.tight_layout()\n", "plt.show()" ] @@ -474,72 +189,37 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\dap182\\AppData\\Local\\Temp\\ipykernel_49784\\869146395.py:9: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", - " plt.tight_layout()\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Plot strip plot with density coloring (no thresholding yet)\n", "fig, (ax_strip, ax_hist) = gmm_explore.plot_feature_strip_plot_exploratory(\n", " scatter_density=True,\n", " x_axis_limits=(0, 0.08),\n", - " hist_kwargs={'bins': 100, 'color': 'black'},\n", + " hist_kwargs={\"bins\": 100, \"color\": \"black\"},\n", ")\n", "\n", - "plt.suptitle('CycD1 Distribution - Strip Plot with Density', y=1.02)\n", + "plt.suptitle(\"CycD1 Distribution - Strip Plot with Density\", y=1.02)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The optimal number of components is 2\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# initialize the gmm thresholding object with the adata object and the feature of interest\n", - "gmm_kwargs = {'init_params': 'k-means++', 'n_init':10, 'max_iter':1000}\n", + "gmm_kwargs = {\"init_params\": \"k-means++\", \"n_init\": 10, \"max_iter\": 1000}\n", "\n", - "gmm_thresholding = GMMThresholding(adata= adata, \n", - " feature = 'cycD1 (nuc median)',\n", - " label_obs_save_str= 'Low/High DNA Content',\n", - " gmm_kwargs = gmm_kwargs)\n", + "gmm_thresholding = GMMThresholding(\n", + " adata=adata,\n", + " feature=\"cycD1 (nuc median)\",\n", + " label_obs_save_str=\"Low/High DNA Content\",\n", + " gmm_kwargs=gmm_kwargs,\n", + ")\n", "\n", "# plot the bayesian information criterion curve to determine the optimal number of components\n", "component_range = 5\n", @@ -548,10 +228,12 @@ ")\n", "\n", "# the optimal number of components can be calculated without plotting as well\n", - "optimal_component_number = gmm_thresholding.determine_optimal_components(component_range=component_range)\n", - "print(f'The optimal number of components is {optimal_component_number}')\n", + "optimal_component_number = gmm_thresholding.determine_optimal_components(\n", + " component_range=component_range\n", + ")\n", + "print(f\"The optimal number of components is {optimal_component_number}\")\n", "\n", - "#fit the GMM with the optimal number of components\n", + "# fit the GMM with the optimal number of components\n", "gmm_thresholding.fit(n_components=optimal_component_number)" ] }, @@ -571,52 +253,24 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Categorize samples using automatically calculated decision boundaries from the fitted GMM\n", "# The ordered_labels parameter specifies the labels from low to high feature values\n", - "gmm_thresholding.categorize_samples(ordered_labels=['Low', 'High'])" + "gmm_thresholding.categorize_samples(ordered_labels=[\"Low\", \"High\"])" ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Figure saved to: c:\\Users\\dap182\\Documents\\git\\cc_mapping\\notebooks\\results\\single\\basic_threshold_histogram.png\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "\n", "gmm_thresholding.plot_hist_distribution_with_boundaries(\n", - " save_path=single_results_dir / 'basic_threshold_histogram.png' # Optional: save the figure\n", + " save_path=single_results_dir\n", + " / \"basic_threshold_histogram.png\" # Optional: save the figure\n", ")" ] }, @@ -629,25 +283,16 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plots a strip plot with the labeled histogram and the decision boundaries, coloring by sample density\n", - "hist_kwargs = {\"bins\": 100, 'color': 'black'}\n", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plots a strip plot with the labeled histogram and the decision boundaries, coloring by sample density\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", "# Note: vmax is set to match the exploratory plot's auto-calculated range for consistent colorbars\n", - "gmm_thresholding.plot_strip_plot_histogram_with_decision_boundaries(hist_kwargs=hist_kwargs, vmax=4, y_axis_limits=(0,0.06));" + "gmm_thresholding.plot_strip_plot_histogram_with_decision_boundaries(\n", + " hist_kwargs=hist_kwargs, vmax=4, y_axis_limits=(0, 0.06)\n", + ");" ] }, { @@ -659,27 +304,17 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#plots a strip plot with the labeled histogram and the decision boundaries, coloring by sample labels\n", - "hist_kwargs = {\"bins\": 100, 'color': \"black\"}\n", - "gmm_thresholding.plot_strip_plot_histogram_with_decision_boundaries(y_axis_limits=(0,0.06),\n", - " hist_kwargs=hist_kwargs,\n", - " scatter_density=False,\n", - " );" + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# plots a strip plot with the labeled histogram and the decision boundaries, coloring by sample labels\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", + "gmm_thresholding.plot_strip_plot_histogram_with_decision_boundaries(\n", + " y_axis_limits=(0, 0.06),\n", + " hist_kwargs=hist_kwargs,\n", + " scatter_density=False,\n", + ");" ] }, { @@ -702,37 +337,16 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Reinitialize to demonstrate manual thresholds\n", "gmm_thresholding_manual = GMMThresholding(\n", - " adata=adata, \n", - " feature='cycD1 (nuc median)',\n", - " label_obs_save_str='Manual Low/High DNA Content',\n", - " gmm_kwargs=gmm_kwargs\n", + " adata=adata,\n", + " feature=\"cycD1 (nuc median)\",\n", + " label_obs_save_str=\"Manual Low/High DNA Content\",\n", + " gmm_kwargs=gmm_kwargs,\n", ")\n", "\n", "# Fit the GMM (still useful for visualization even when using manual thresholds)\n", @@ -741,8 +355,7 @@ "# Categorize with manual threshold at 0.05\n", "# The manual_thresholds parameter overrides automatic boundary calculation\n", "gmm_thresholding_manual.categorize_samples(\n", - " ordered_labels=['Low', 'High'], \n", - " manual_thresholds=[0.05]\n", + " ordered_labels=[\"Low\", \"High\"], manual_thresholds=[0.05]\n", ")\n", "\n", "# Visualize to see the manually set threshold\n", @@ -760,22 +373,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 6797 × 289\n", - " obs: 'Low/High DNA Content'\n", - " uns: 'gmm_thresholding_events'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "adata = gmm_thresholding.return_adata()\n", "adata" @@ -812,38 +412,12 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Thresholding Report\n", - "==================================================\n", - "\n", - "1. cycD1 (nuc median) (Standard Thresholding)\n", - "---------------------------------------------\n", - " Feature: cycD1 (nuc median)\n", - " Layer: None\n", - " Obs column: Low/High DNA Content\n", - " Components: 2\n", - " Thresholds: [0.0191]\n", - " Labels: ['Low', 'High']\n", - " Cell counts: Low=5744, High=1053\n", - "\n", - "==================================================\n", - "Total operations: 1\n", - "Operation types:\n", - " - standard: 1\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Generate a text report using the method from the base class\n", - "report = gmm_thresholding.generate_thresholding_report(\n", - " output_format='text'\n", - ")\n", + "report = gmm_thresholding.generate_thresholding_report(output_format=\"text\")\n", "print(report)" ] }, @@ -856,82 +430,12 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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OperationTypeFeatureLayerObs LabelComponentsThresholdsLabelsParentRefined FromTotal Cells
01. cycD1 (nuc median)standardcycD1 (nuc median)NoneLow/High DNA Content20.0191Low, HighNoneN/A6797
\n", - "
" - ], - "text/plain": [ - " Operation Type Feature Layer \\\n", - "0 1. cycD1 (nuc median) standard cycD1 (nuc median) None \n", - "\n", - " Obs Label Components Thresholds Labels Parent Refined From \\\n", - "0 Low/High DNA Content 2 0.0191 Low, High None N/A \n", - "\n", - " Total Cells \n", - "0 6797 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Generate a DataFrame report\n", - "report_df = gmm_thresholding.generate_thresholding_report(\n", - " output_format='dataframe'\n", - ")\n", + "report_df = gmm_thresholding.generate_thresholding_report(output_format=\"dataframe\")\n", "display(report_df)" ] }, @@ -953,27 +457,15 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Combined label distribution:\n", - "control_and_high_DNA\n", - "other 6455\n", - "control_with_high_DNA 342\n", - "Name: count, dtype: int64\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# Create a synthetic treatment label for demonstration\n", "np.random.seed(42)\n", "n_cells = len(adata)\n", - "adata.obs['treatment'] = pd.Categorical(\n", - " np.random.choice(['control', 'drug_A', 'drug_B'], size=n_cells)\n", + "adata.obs[\"treatment\"] = pd.Categorical(\n", + " np.random.choice([\"control\", \"drug_A\", \"drug_B\"], size=n_cells)\n", ")\n", "\n", "# Combine two categorical labels using boolean operators\n", @@ -981,19 +473,19 @@ "# Note: Parameter names have changed - use obs_key_1, match_values_1, etc.\n", "adata = create_boolean_label_combination(\n", " adata,\n", - " obs_key_1='treatment',\n", - " match_values_1=['control'],\n", - " obs_key_2='Low/High DNA Content',\n", - " match_values_2=['High'],\n", - " operator='AND', \n", - " output_obs_key='control_and_high_DNA',\n", - " true_label='control_with_high_DNA',\n", - " false_label='other',\n", - " overwrite=False # Default: raises error if output_obs_key already exists\n", + " obs_key_1=\"treatment\",\n", + " match_values_1=[\"control\"],\n", + " obs_key_2=\"Low/High DNA Content\",\n", + " match_values_2=[\"High\"],\n", + " operator=\"AND\",\n", + " output_obs_key=\"control_and_high_DNA\",\n", + " true_label=\"control_with_high_DNA\",\n", + " false_label=\"other\",\n", + " overwrite=False, # Default: raises error if output_obs_key already exists\n", ")\n", "\n", "print(\"Combined label distribution:\")\n", - "print(adata.obs['control_and_high_DNA'].value_counts())" + "print(adata.obs[\"control_and_high_DNA\"].value_counts())" ] } ], diff --git a/docs/source/tutorials/index.rst b/docs/source/tutorials/index.rst index 1621428..ecbcacd 100644 --- a/docs/source/tutorials/index.rst +++ b/docs/source/tutorials/index.rst @@ -8,6 +8,6 @@ Step-by-step tutorials using real data and workflows. .. toctree:: :maxdepth: 2 - + Single_Thresholding_Workflow Sequential_Thresholding_Workflow diff --git a/docs_requirements.txt b/docs_requirements.txt index e28cc47..0080857 100644 --- a/docs_requirements.txt +++ b/docs_requirements.txt @@ -1,8 +1,8 @@ -sphinx>=8.0.0 +ipykernel>=6.29.0 myst-parser>=4.0.0 -sphinx-autodoc-typehints>=2.0.0 -sphinx-copybutton>=0.5.0 nbsphinx>=0.9.0 -pydata-sphinx-theme>=0.16.1 numpydoc>=1.9.0 -ipykernel>=6.29.0 \ No newline at end of file +pydata-sphinx-theme>=0.16.1 +sphinx>=8.0.0 +sphinx-autodoc-typehints>=2.0.0 +sphinx-copybutton>=0.5.0 diff --git a/environments/cc_mapping.yml b/environments/cc_mapping.yml index 746b7c2..d182604 100644 Binary files a/environments/cc_mapping.yml and b/environments/cc_mapping.yml differ diff --git a/environments/manifold.yml b/environments/manifold.yml index 7377836..14c9f7d 100644 Binary files a/environments/manifold.yml and b/environments/manifold.yml differ diff --git a/environments/palantir.yml b/environments/palantir.yml index 7563d3f..99d9d70 100644 Binary files a/environments/palantir.yml and b/environments/palantir.yml differ diff --git a/notebooks/CSV_to_Anndata.ipynb b/notebooks/CSV_to_Anndata.ipynb index fcec1b3..ad4de52 100644 --- a/notebooks/CSV_to_Anndata.ipynb +++ b/notebooks/CSV_to_Anndata.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -15,15 +15,12 @@ "import anndata as ad\n", "import numpy as np\n", "import pandas as pd\n", - "import scipy.stats as st\n", "\n", "%matplotlib inline\n", "\n", "import sys\n", "\n", - "sys.path.append(\"R:\\Dante\\git\\cc_mapping\\src\")\n", - "\n", - "from cc_mapping import core, manifold, plot, preprocess" + "sys.path.append(r\"R:\\Dante\\git\\cc_mapping\\src\")" ] }, { @@ -35,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -59,260 +56,9 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " E2F1 (nuc median) cycA (nuc median) cycD1 (nuc median) \\\n", - "Unnamed: 0.1.1 \n", - "0 0.006851 0.005875 0.019471 \n", - "1 0.012360 0.028153 0.007462 \n", - "2 0.007279 0.005707 0.006592 \n", - "3 0.006531 0.016602 0.009369 \n", - "5 0.006744 0.006027 0.009033 \n", - "\n", - " p21 (nuc median) Int_Intg_DNA_nuc Skp2 (nuc median) \\\n", - "Unnamed: 0.1.1 \n", - "0 0.026032 5.746899 0.006493 \n", - "1 0.004318 8.852262 0.022797 \n", - "2 0.003632 4.951003 0.015366 \n", - "3 0.006264 10.466743 0.019196 \n", - "5 0.005295 5.249119 0.010422 \n", - "\n", - " Cdt1 (nuc median) Nuc area Cdh1 (nuc median) \\\n", - "Unnamed: 0.1.1 \n", - "0 0.005158 553.0 0.014336 \n", - "1 0.005951 490.0 0.015229 \n", - "2 0.004929 363.0 0.005730 \n", - "3 0.005234 579.0 0.009361 \n", - "5 0.007797 296.0 0.008896 \n", - "\n", - " cycE (nuc median) ... cyto_over_DNA CDK2_cyto_over_nuc \\\n", - "Unnamed: 0.1.1 ... \n", - "0 0.009262 ... 909.012025 3.974065 \n", - "1 0.008392 ... 654.860857 4.824163 \n", - "2 0.009117 ... 537.062859 4.273837 \n", - "3 0.007118 ... 361.621566 3.177175 \n", - "5 0.007767 ... 532.660833 6.257769 \n", - "\n", - " STAT3 (phospho/total nuc) age phase PHATE_1 \\\n", - "Unnamed: 0.1.1 \n", - "0 1.770186 10.263173 G1 -0.020654 \n", - "1 1.455814 12.109644 S 0.025945 \n", - "2 1.290323 4.954907 G1 0.002961 \n", - "3 1.435065 13.424587 G2 0.034884 \n", - "5 1.266304 1.828240 G1 -0.019039 \n", - "\n", - " PHATE_2 PCNA foci DNA content Local cell density \n", - "Unnamed: 0.1.1 \n", - "0 0.007907 0.005151 2.298759 7.0 \n", - "1 -0.000735 0.006861 3.540905 1.0 \n", - "2 -0.004575 0.004148 1.980401 6.0 \n", - "3 0.002944 0.002457 4.186697 9.0 \n", - "5 0.000584 0.003277 2.099648 3.0 \n", - "\n", - "[5 rows x 297 columns]" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "csv = pd.read_csv(download_path, index_col=0, low_memory=False)\n", "csv.index = csv.index.astype(str)\n", @@ -331,28 +77,9 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['E2F1 (nuc median)', 'cycA (nuc median)', 'cycD1 (nuc median)',\n", - " 'p21 (nuc median)', 'Int_Intg_DNA_nuc', 'Skp2 (nuc median)',\n", - " 'Cdt1 (nuc median)', 'Nuc area', 'Cdh1 (nuc median)',\n", - " 'cycE (nuc median)',\n", - " ...\n", - " 'cyto_over_DNA', 'CDK2_cyto_over_nuc', 'STAT3 (phospho/total nuc)',\n", - " 'age', 'phase', 'PHATE_1', 'PHATE_2', 'PCNA foci', 'DNA content',\n", - " 'Local cell density'],\n", - " dtype='object', length=297)" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# initial columns\n", "csv.columns" @@ -360,33 +87,38 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import Levenshtein\n", - "import numpy as np\n", - "from scipy.cluster.hierarchy import linkage, fcluster\n", - "from collections import defaultdict\n", "import math\n", "import statistics\n", + "from collections import defaultdict\n", + "\n", + "import Levenshtein\n", + "from scipy.cluster.hierarchy import fcluster, linkage\n", + "\n", "\n", "# --- Helper function for distance (unchanged) ---\n", "def calculate_normalized_distance(s1, s2):\n", " \"\"\"Helper function to calculate normalized Levenshtein distance.\"\"\"\n", - " if not s1 and not s2: return 0.0\n", - " if not s1 or not s2: return 1.0\n", + " if not s1 and not s2:\n", + " return 0.0\n", + " if not s1 or not s2:\n", + " return 1.0\n", "\n", " dist = Levenshtein.distance(s1, s2)\n", " max_len = max(len(s1), len(s2))\n", "\n", " if max_len == 0:\n", - " norm_dist = 0.0\n", + " norm_dist = 0.0\n", " else:\n", " norm_dist = dist / max_len\n", - " if not math.isfinite(norm_dist): norm_dist = 1.0\n", + " if not math.isfinite(norm_dist):\n", + " norm_dist = 1.0\n", " return norm_dist\n", "\n", + "\n", "# --- Helper function for internal sorting (unchanged, outliers first) ---\n", "def sort_items_within_cluster(cluster: list[str]) -> list[str]:\n", " \"\"\"\n", @@ -406,20 +138,20 @@ " distances_to_others.append(calculate_normalized_distance(item, other_item))\n", "\n", " avg_dist = statistics.mean(distances_to_others) if distances_to_others else 0.0\n", - " item_distances.append({'item': item, 'avg_dist': avg_dist})\n", + " item_distances.append({\"item\": item, \"avg_dist\": avg_dist})\n", "\n", " # Sort items based on their average distance (DESCENDING) -> outliers first\n", - " sorted_items_with_dist = sorted(item_distances, key=lambda x: -x['avg_dist'])\n", + " sorted_items_with_dist = sorted(item_distances, key=lambda x: -x[\"avg_dist\"])\n", "\n", - " return [d['item'] for d in sorted_items_with_dist]\n", + " return [d[\"item\"] for d in sorted_items_with_dist]\n", "\n", "\n", "# --- Main function using SciPy Hierarchical Clustering ---\n", "def group_columns_hierarchical_internal_sort(\n", " column_names: list[str],\n", - " normalized_threshold: float = 0.4, # Threshold for fcluster distance criterion\n", - " linkage_method: str = 'average', # Method for scipy.cluster.hierarchy.linkage\n", - " sort_clusters_by: str = 'size' # Options: 'size', 'none'\n", + " normalized_threshold: float = 0.4, # Threshold for fcluster distance criterion\n", + " linkage_method: str = \"average\", # Method for scipy.cluster.hierarchy.linkage\n", + " sort_clusters_by: str = \"size\", # Options: 'size', 'none'\n", ") -> list[list[str]]:\n", " \"\"\"\n", " Groups similar column names using hierarchical clustering (SciPy),\n", @@ -443,13 +175,17 @@ " \"\"\"\n", " if not column_names:\n", " return []\n", - " \n", + "\n", " # Ensure unique names first, remember original order/duplicates if needed (not done here)\n", " # Using unique names for clustering is generally better\n", - " unique_column_names = sorted(list(set(column_names))) # Use unique names for clustering\n", + " unique_column_names = sorted(\n", + " list(set(column_names))\n", + " ) # Use unique names for clustering\n", " if len(unique_column_names) <= 1:\n", - " # If only one unique name, just apply internal sort (which does nothing)\n", - " return [sort_items_within_cluster(list(column_names))] # Return original list structure\n", + " # If only one unique name, just apply internal sort (which does nothing)\n", + " return [\n", + " sort_items_within_cluster(list(column_names))\n", + " ] # Return original list structure\n", "\n", " num_unique_cols = len(unique_column_names)\n", "\n", @@ -459,21 +195,23 @@ " k = 0\n", " for i in range(num_unique_cols):\n", " for j in range(i + 1, num_unique_cols):\n", - " dist = calculate_normalized_distance(unique_column_names[i], unique_column_names[j])\n", + " dist = calculate_normalized_distance(\n", + " unique_column_names[i], unique_column_names[j]\n", + " )\n", " condensed_dist_matrix[k] = dist\n", " k += 1\n", "\n", " # --- 2. Perform Hierarchical Clustering ---\n", " if condensed_dist_matrix.size == 0:\n", - " # Handle edge case where all columns might be identical after unique()\n", - " cluster_labels = np.ones(num_unique_cols, dtype=int)\n", + " # Handle edge case where all columns might be identical after unique()\n", + " cluster_labels = np.ones(num_unique_cols, dtype=int)\n", " else:\n", " linked = linkage(condensed_dist_matrix, method=linkage_method)\n", "\n", " # --- 3. Form Flat Clusters ---\n", " # 'fcluster' creates flat clusters from the hierarchical clustering.\n", " # 'criterion='distance'' cuts the dendrogram at the specified distance threshold.\n", - " cluster_labels = fcluster(linked, normalized_threshold, criterion='distance')\n", + " cluster_labels = fcluster(linked, normalized_threshold, criterion=\"distance\")\n", "\n", " # --- 4. Group Unique Names by Cluster Label ---\n", " temp_grouped_clusters = defaultdict(list)\n", @@ -484,11 +222,13 @@ " initial_clusters = list(temp_grouped_clusters.values())\n", "\n", " # --- 5. Sort the Clusters Themselves (Optional) ---\n", - " if sort_clusters_by == 'size':\n", + " if sort_clusters_by == \"size\":\n", " # Sort primarily by size (descending)\n", " # Add secondary sort key (e.g., by first element) for stable sorting if sizes are equal\n", - " sorted_initial_clusters = sorted(initial_clusters, key=lambda x: (-len(x), x[0] if x else ''))\n", - " else: # 'none' or any other value\n", + " sorted_initial_clusters = sorted(\n", + " initial_clusters, key=lambda x: (-len(x), x[0] if x else \"\")\n", + " )\n", + " else: # 'none' or any other value\n", " sorted_initial_clusters = initial_clusters\n", "\n", " # --- 6. Sort Items WITHIN Each Cluster (Outliers First) ---\n", @@ -500,314 +240,51 @@ "\n", " return final_clusters\n", "\n", + "\n", "# Group columns using hierarchical clustering, sort clusters by size, sort items internally (outliers first)\n", "columns = list(csv.columns.values)\n", "final_grouped_clusters = group_columns_hierarchical_internal_sort(\n", " columns,\n", - " normalized_threshold=0.80, # Adjust threshold - might need higher for longer names\n", - " linkage_method='average',\n", - " sort_clusters_by='size'\n", + " normalized_threshold=0.80, # Adjust threshold - might need higher for longer names\n", + " linkage_method=\"average\",\n", + " sort_clusters_by=\"size\",\n", ")" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "================================================================================\n" - ] - }, - { - "data": { - "text/html": [ - "
Cluster 1\n",
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                                         240 Columns Matching - \"median)\"                                          \n",
-       "p14ARF (nuc median)     p14ARF (PM median)      p14ARF (PN median)      pSTAT3 (nuc median)    pSTAT3 (PM median)  \n",
-       "pSTAT3 (PN median)      STAT3 (nuc median)      p14ARF (cyto median)    p14ARF (cell median)   pGSK3b (nuc median) \n",
-       "pGSK3b (PM median)      pGSK3b (PN median)      GSK3b (nuc median)      STAT3 (PM median)      STAT3 (PN median)   \n",
-       "pSTAT3 (cyto median)    pSTAT3 (cell median)    STAT3 (cyto median)     STAT3 (cell median)    cycD1 (nuc median)  \n",
-       "cycB1 (nuc median)      pGSK3b (cyto median)    pGSK3b (cell median)    RB (cyto median)       RB (cell median)    \n",
-       "GSK3b (PM median)       GSK3b (PN median)       GSK3b (cyto median)     pH2AX (nuc median)     GSK3b (cell median) \n",
-       "bCat (nuc median)       Bcl2 (nuc median)       cycD1 (PM median)       cycD1 (PN median)      cycD1 (cyto median) \n",
-       "cycD1 (cell median)     cFos (nuc median)       cJun (nuc median)       bCat (cyto median)     S6 (cyto median)    \n",
-       "bCat (cell median)      S6 (cell median)        YAP (cyto median)       Bcl2 (cyto median)     YAP (cell median)   \n",
-       "cycB1 (cyto median)     Bcl2 (cell median)      cycB1 (PM median)       cycB1 (PN median)      cycB1 (cell median) \n",
-       "cFos (cyto median)      cJun (cyto median)      cFos (cell median)      cJun (cell median)     PCNA (nuc median)   \n",
-       "cMyc (nuc median)       PCNA (cyto median)      PCNA (cell median)      YAP (nuc median)       Skp2 (nuc median)   \n",
-       "pH2AX (cyto median)     Fra1 (nuc median)       pH2AX (cell median)     pH2AX (PM median)      pH2AX (PN median)   \n",
-       "cMyc (cyto median)      cMyc (cell median)      DNA (cyto median)       Skp2 (cyto median)     DNA (cell median)   \n",
-       "Fra1 (cyto median)      RB (nuc median)         Skp2 (cell median)      BP1 (cyto median)      cycE (nuc median)   \n",
-       "Fra1 (cell median)      BP1 (cell median)       E2F1 (nuc median)       AKT (cyto median)      cycE (cyto median)  \n",
-       "AKT (cell median)       cycE (cell median)      E2F1 (cyto median)      E2F1 (cell median)     Cdh1 (nuc median)   \n",
-       "DNA (nuc median)        S6 (nuc median)         Cdt1 (nuc median)       CDK4 (nuc median)      BP1 (nuc median)    \n",
-       "Cdh1 (cyto median)      Cdh1 (cell median)      cycA (nuc median)       bCat (PM median)       bCat (PN median)    \n",
-       "Cdt1 (cyto median)      CDK4 (cyto median)      Cdt1 (cell median)      pCHK1 (nuc median)     AKT (nuc median)    \n",
-       "ERK (cyto median)       CDK4 (cell median)      CDK2 (nuc median)       Bcl2 (PM median)       Bcl2 (PN median)    \n",
-       "ERK (cell median)       cycA (cyto median)      cFos (PM median)        cFos (PN median)       cJun (PM median)    \n",
-       "cJun (PN median)        cycA (cell median)      CDK6 (nuc median)       CDK2 (cyto median)     CDK2 (cell median)  \n",
-       "CDK6 (cyto median)      CDK6 (cell median)      PCNA (PM median)        PCNA (PN median)       ERK (nuc median)    \n",
-       "cMyc (PM median)        cMyc (PN median)        p38 (cyto median)       Skp2 (PM median)       Skp2 (PN median)    \n",
-       "p38 (cell median)       Fra1 (PM median)        Fra1 (PN median)        pCHK1 (cyto median)    pCHK1 (cell median) \n",
-       "p16 (cyto median)       pp65 (nuc median)       p16 (cell median)       RSK1 (nuc median)      pp65 (cyto median)  \n",
-       "cycE (PM median)        cycE (PN median)        pCHK1 (PM median)       pCHK1 (PN median)      pp65 (cell median)  \n",
-       "p53 (cyto median)       pERK (nuc median)       pp38 (nuc median)       pRB (cyto median)      p27 (cyto median)   \n",
-       "RSK1 (cyto median)      p53 (cell median)       E2F1 (PM median)        E2F1 (PN median)       pRB (cell median)   \n",
-       "p27 (cell median)       RSK1 (cell median)      pp53 (nuc median)       pERK (cyto median)     pp38 (cyto median)  \n",
-       "pp27 (nuc median)       pERK (cell median)      p38 (nuc median)        pp38 (cell median)     pp53 (cyto median)  \n",
-       "pp27 (cyto median)      Cdh1 (PM median)        Cdh1 (PN median)        pp53 (cell median)     pp27 (cell median)  \n",
-       "p16 (nuc median)        pS6 (cyto median)       pS6 (cell median)       Cdt1 (PM median)       Cdt1 (PN median)    \n",
-       "YAP (PM median)         YAP (PN median)         CDK4 (PM median)        CDK4 (PN median)       pRSK (nuc median)   \n",
-       "p53 (nuc median)        cycA (PM median)        cycA (PN median)        p27 (nuc median)       pRB (nuc median)    \n",
-       "pRSK (cyto median)      pRSK (cell median)      CDK2 (PM median)        CDK2 (PN median)       pAKT (nuc median)   \n",
-       "pAKT (cyto median)      CDK6 (PM median)        CDK6 (PN median)        pAKT (cell median)     pS6 (nuc median)    \n",
-       "RB (PM median)          RB (PN median)          p21 (cyto median)       DNA (PM median)        DNA (PN median)     \n",
-       "p21 (cell median)       BP1 (PM median)         BP1 (PN median)         pp21 (nuc median)      pp21 (cyto median)  \n",
-       "pp21 (cell median)      AKT (PM median)         AKT (PN median)         S6 (PM median)         S6 (PN median)      \n",
-       "pp65 (PM median)        pp65 (PN median)        RSK1 (PM median)        RSK1 (PN median)       p21 (nuc median)    \n",
-       "pERK (PM median)        pERK (PN median)        pp38 (PM median)        pp38 (PN median)       pp53 (PM median)    \n",
-       "pp53 (PN median)        pp27 (PM median)        pp27 (PN median)        ERK (PM median)        ERK (PN median)     \n",
-       "pRSK (PM median)        pRSK (PN median)        pAKT (PM median)        pAKT (PN median)       p38 (PM median)     \n",
-       "p38 (PN median)         p16 (PM median)         p16 (PN median)         pp21 (PM median)       pp21 (PN median)    \n",
-       "p53 (PM median)         p53 (PN median)         p27 (PM median)         p27 (PN median)        pRB (PM median)     \n",
-       "pRB (PN median)         pS6 (PM median)         pS6 (PN median)         p21 (PM median)        p21 (PN median)     \n",
-       "
\n" - ], - "text/plain": [ - "\u001b[3m 240 Columns Matching - \"median)\" \u001b[0m\n", - "p14ARF \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m p14ARF \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m p14ARF \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pSTAT3 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pSTAT3 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m \n", - "pSTAT3 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m STAT3 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m p14ARF \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m p14ARF \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m pGSK3b \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "pGSK3b \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pGSK3b \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m GSK3b \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m STAT3 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m STAT3 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "pSTAT3 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m pSTAT3 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m STAT3 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m STAT3 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m cycD1 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "cycB1 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pGSK3b \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m pGSK3b \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m RB \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m RB \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "GSK3b \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m GSK3b \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m GSK3b \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m pH2AX \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m GSK3b \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "bCat \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m Bcl2 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m cycD1 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m cycD1 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m cycD1 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m \n", - "cycD1 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m cFos \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m cJun \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m bCat \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m S6 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m \n", - "bCat \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m S6 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m YAP \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m Bcl2 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m YAP \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "cycB1 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m Bcl2 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m cycB1 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m cycB1 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m cycB1 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "cFos \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m cJun \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m cFos \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m cJun \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m PCNA \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "cMyc \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m PCNA \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m PCNA \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m YAP \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m Skp2 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "pH2AX \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m Fra1 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pH2AX \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m pH2AX \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pH2AX \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "cMyc \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m cMyc \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m DNA \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m Skp2 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m DNA \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "Fra1 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m RB \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m Skp2 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m BP1 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m cycE \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "Fra1 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m BP1 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m E2F1 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m AKT \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m cycE \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m \n", - "AKT \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m cycE \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m E2F1 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m E2F1 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m Cdh1 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "DNA \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m S6 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m Cdt1 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m CDK4 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m BP1 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "Cdh1 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m Cdh1 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m cycA \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m bCat \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m bCat \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "Cdt1 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m CDK4 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m Cdt1 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m pCHK1 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m AKT \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "ERK \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m CDK4 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m CDK2 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m Bcl2 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m Bcl2 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "ERK \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m cycA \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m cFos \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m cFos \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m cJun \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m \n", - "cJun \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m cycA \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m CDK6 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m CDK2 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m CDK2 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "CDK6 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m CDK6 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m PCNA \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m PCNA \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m ERK \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "cMyc \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m cMyc \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m p38 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m Skp2 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m Skp2 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "p38 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m Fra1 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m Fra1 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pCHK1 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m pCHK1 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "p16 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m pp65 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m p16 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m RSK1 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pp65 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m \n", - "cycE \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m cycE \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pCHK1 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pCHK1 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pp65 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "p53 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m pERK \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pp38 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pRB \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m p27 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m \n", - "RSK1 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m p53 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m E2F1 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m E2F1 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pRB \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "p27 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m RSK1 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m pp53 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pERK \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m pp38 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m \n", - "pp27 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pERK \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m p38 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pp38 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m pp53 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m \n", - "pp27 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m Cdh1 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m Cdh1 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pp53 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m pp27 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m \n", - "p16 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pS6 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m pS6 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m Cdt1 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m Cdt1 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "YAP \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m YAP \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m CDK4 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m CDK4 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pRSK \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "p53 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m cycA \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m cycA \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m p27 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pRB \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "pRSK \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m pRSK \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m CDK2 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m CDK2 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pAKT \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "pAKT \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m CDK6 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m CDK6 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pAKT \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m pS6 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "RB \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m RB \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m p21 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m DNA \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m DNA \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "p21 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m BP1 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m BP1 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pp21 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m pp21 \u001b[1m(\u001b[0mcyto median\u001b[1m)\u001b[0m \n", - "pp21 \u001b[1m(\u001b[0mcell median\u001b[1m)\u001b[0m AKT \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m AKT \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m S6 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m S6 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "pp65 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pp65 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m RSK1 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m RSK1 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m p21 \u001b[1m(\u001b[0mnuc median\u001b[1m)\u001b[0m \n", - "pERK \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pERK \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pp38 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pp38 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pp53 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m \n", - "pp53 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pp27 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pp27 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m ERK \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m ERK \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "pRSK \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pRSK \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pAKT \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pAKT \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m p38 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m \n", - "p38 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m p16 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m p16 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pp21 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pp21 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n", - "p53 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m p53 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m p27 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m p27 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pRB \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m \n", - "pRB \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m pS6 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m pS6 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m p21 \u001b[1m(\u001b[0mPM median\u001b[1m)\u001b[0m p21 \u001b[1m(\u001b[0mPN median\u001b[1m)\u001b[0m \n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", - "2 Columns Not Matching - \"median)\": ['Local cell density', 'DNA content']\n", - "\n", - "================================================================================\n" - ] - }, - { - "data": { - "text/html": [ - "
Cluster 2\n",
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Cluster 3\n",
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Cluster 4\n",
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Cluster 5\n",
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Cluster 6\n",
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\n" - ], - "text/plain": [ - "Cluster \u001b[1;36m6\u001b[0m\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--------------------------------------------------------------------------------\n", - "All columns in the cluster contain the string \"AreaShape_FormFactor_cell\".\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "from rich.console import Console\n", + "from Levenshtein import median, median_improve\n", "from rich.columns import Columns\n", - "\n", - "from Levenshtein import median_improve, median\n", - "import re\n", + "from rich.console import Console\n", "\n", "console = Console()\n", "for idx, cluster in enumerate(final_grouped_clusters):\n", - " print('='*80)\n", - " console.print(f'Cluster {idx+1}', )\n", + " print(\"=\" * 80)\n", + " console.print(\n", + " f\"Cluster {idx + 1}\",\n", + " )\n", " median_string = median(cluster)\n", - " improved_median_string = median_improve(median_string,cluster)\n", + " improved_median_string = median_improve(median_string, cluster)\n", " median_sections = median_string.split(\" \")\n", " median_sections = list(filter(None, median_sections))\n", "\n", - " for idx,section in enumerate(median_sections):\n", + " for idx, section in enumerate(median_sections):\n", " string = re.escape(section)\n", - " matches = [re.search(rf'{string}', x) for x in cluster]\n", + " matches = [re.search(rf\"{string}\", x) for x in cluster]\n", " num_matches = len([x for x in matches if x is not None])\n", " if num_matches == 0:\n", - " if idx == len(median_sections)-1:\n", + " if idx == len(median_sections) - 1:\n", " print()\n", " continue\n", " else:\n", - " print('-'*80)\n", + " print(\"-\" * 80)\n", "\n", - " matching = [x for x in cluster if re.search(rf'{string}', x) is not None]\n", - " not_matching = [x for x in cluster if re.search(rf'{string}', x) is None]\n", + " matching = [x for x in cluster if re.search(rf\"{string}\", x) is not None]\n", + " not_matching = [x for x in cluster if re.search(rf\"{string}\", x) is None]\n", "\n", " if len(matching) == len(cluster):\n", " print(f'All columns in the cluster contain the string \"{section}\".')\n", @@ -815,46 +292,40 @@ " if len(matching) < 5:\n", " print(f'{len(matching)} Columns Matching - \"{section}\": {matching}')\n", " else:\n", - " columns_display = Columns(matching, expand=True, equal=True, title= f'{len(matching)} Columns Matching - \"{section}\"') # Adjust options as needed\n", - " console.print(columns_display, justify='center')\n", - " print('~'*80)\n", + " columns_display = Columns(\n", + " matching,\n", + " expand=True,\n", + " equal=True,\n", + " title=f'{len(matching)} Columns Matching - \"{section}\"',\n", + " ) # Adjust options as needed\n", + " console.print(columns_display, justify=\"center\")\n", + " print(\"~\" * 80)\n", "\n", " if len(not_matching) != 0:\n", " if len(not_matching) < 5:\n", - " print(f'{len(not_matching)} Columns Not Matching - \"{section}\": {not_matching}')\n", + " print(\n", + " f'{len(not_matching)} Columns Not Matching - \"{section}\": {not_matching}'\n", + " )\n", " else:\n", - " columns_display_nm = Columns(not_matching, expand=True, equal=True, title= f'{len(not_matching)} Columns Not Matching - \"{section}\"') # Adjust options as needed\n", - " console.print(columns_display_nm, justify='center')\n", - "\n", - " if idx != len(median_sections)-1:\n", - " print('-'*80)\n", + " columns_display_nm = Columns(\n", + " not_matching,\n", + " expand=True,\n", + " equal=True,\n", + " title=f'{len(not_matching)} Columns Not Matching - \"{section}\"',\n", + " ) # Adjust options as needed\n", + " console.print(columns_display_nm, justify=\"center\")\n", + "\n", + " if idx != len(median_sections) - 1:\n", + " print(\"-\" * 80)\n", " else:\n", - " print()\n" + " print()" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['E2F1_nuc_median', 'cycA_nuc_median', 'cycD1_nuc_median',\n", - " 'p21_nuc_median', 'Int_Intg_DNA_nuc', 'Skp2_nuc_median',\n", - " 'Cdt1_nuc_median', 'Nuc_area', 'Cdh1_nuc_median', 'cycE_nuc_median',\n", - " ...\n", - " 'cyto_over_DNA', 'CDK2_cyto_over_nuc', 'STAT3_phospho/total_nuc', 'age',\n", - " 'phase', 'PHATE_1', 'PHATE_2', 'PCNA_foci', 'DNA_content',\n", - " 'Local_cell_density'],\n", - " dtype='object', length=297)" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# removes the parentheses and replace spaces with underscores from the column names - optional\n", "csv.columns = [re.sub(r\"[\\(\\)]\", \"\", x) for x in csv.columns]\n", @@ -864,84 +335,9 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PHATE_1PHATE_2
Unnamed: 0.1.1
0-0.0206540.007907
10.025945-0.000735
20.002961-0.004575
30.0348840.002944
5-0.0190390.000584
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" - ], - "text/plain": [ - " PHATE_1 PHATE_2\n", - "Unnamed: 0.1.1 \n", - "0 -0.020654 0.007907\n", - "1 0.025945 -0.000735\n", - "2 0.002961 -0.004575\n", - "3 0.034884 0.002944\n", - "5 -0.019039 0.000584" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# isolate phate dimenionality reduction coordinates columns\n", "phate_cols = csv.filter(regex=\"PHATE\").columns\n", @@ -952,20 +348,9 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['phase'], dtype='object')" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# isolate all columns that are strings that will be converted into the obs for the anndata object\n", "# also make sure to remove all cols not relevant for analysis. i.e. WellID, etc.\n", @@ -975,27 +360,9 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['E2F1_nuc_median', 'cycA_nuc_median', 'cycD1_nuc_median',\n", - " 'p21_nuc_median', 'Int_Intg_DNA_nuc', 'Skp2_nuc_median',\n", - " 'Cdt1_nuc_median', 'Nuc_area', 'Cdh1_nuc_median', 'cycE_nuc_median',\n", - " ...\n", - " 'p38_phospho/total_PN', 'RSK1_phospho/total_PN', 'cycD1_over_p21',\n", - " 'cyto_over_DNA', 'CDK2_cyto_over_nuc', 'STAT3_phospho/total_nuc', 'age',\n", - " 'PCNA_foci', 'DNA_content', 'Local_cell_density'],\n", - " dtype='object', length=294)" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# isolate the feature columns - ensure that all columns included in this dataframe are numeric and relevant to the analysis\n", "sc_feat_df = csv.select_dtypes(exclude=[\"object\"])\n", @@ -1011,21 +378,9 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "AnnData object with n_obs × n_vars = 6797 × 294\n", - " obs: 'phase'" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# convert the dataframe into an anndata object\n", "adata = ad.AnnData(\n", @@ -1045,20 +400,20 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Let's inspect some example clusters\n", - "#print(f\"Total clusters: {len(final_grouped_clusters)}\\n\")\n", + "# print(f\"Total clusters: {len(final_grouped_clusters)}\\n\")\n", "\n", "## Show first few clusters\n", - "#for idx in [0, 1, 2, 9]: # Including cluster 9 which has the median features\n", - " #cluster = final_grouped_clusters[idx]\n", - " #print(f\"{'='*80}\")\n", - " #print(f\"Cluster {idx+1} ({len(cluster)} items):\")\n", - " #print(cluster[:10] if len(cluster) > 10 else cluster) # Show first 10 or all\n", - " #print()" + "# for idx in [0, 1, 2, 9]: # Including cluster 9 which has the median features\n", + "# cluster = final_grouped_clusters[idx]\n", + "# print(f\"{'='*80}\")\n", + "# print(f\"Cluster {idx+1} ({len(cluster)} items):\")\n", + "# print(cluster[:10] if len(cluster) > 10 else cluster) # Show first 10 or all\n", + "# print()" ] }, { @@ -1072,104 +427,74 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "====================================================================================================\n", - "PATTERN EXTRACTION RESULTS\n", - "====================================================================================================\n", - "\n", - "Found 6 clusters with the following patterns:\n", - "\n", - "Cluster 1 (242 columns)\n", - " Examples: ['Local cell density', 'DNA content', 'p14ARF (nuc median)']\n", - " Common suffixes: ['median)']\n", - " Common substrings: ['median)']\n", - "\n", - "Cluster 2 (41 columns)\n", - " Examples: ['STAT3 (phospho/total nuc)', 'GSK3b (phospho/total PN)', 'GSK3b (phospho/total nuc)']\n", - " Common substrings: ['(phospho/total']\n", - "\n", - "Cluster 3 (6 columns)\n", - " Examples: ['age', 'Nuc shape', 'phase']\n", - "\n", - "Cluster 4 (4 columns)\n", - " Examples: ['Int_Intg_DNA_nuc', 'cycD1_over_p21', 'CDK2_cyto_over_nuc']\n", - " Common substrings: ['over']\n", - "\n", - "Cluster 5 (3 columns)\n", - " Examples: ['PCNA foci', 'PHATE_1', 'PHATE_2']\n", - "\n", - "Cluster 6 (1 columns)\n", - " Examples: ['AreaShape_FormFactor_cell']\n", - " Common suffixes: ['FormFactor_cell', 'cell']\n", - " Common prefixes: ['AreaShape']\n", - " Common substrings: ['FormFactor', 'AreaShape', 'cell']\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "from collections import Counter\n", "import re\n", + "from collections import Counter\n", + "\n", "\n", "def extract_common_patterns(cluster, min_frequency=0.7):\n", " \"\"\"\n", " Extract common patterns (suffixes, prefixes, infixes) from a cluster of column names.\n", - " \n", + "\n", " Args:\n", " cluster: List of column names\n", " min_frequency: Minimum fraction of cluster members that must contain pattern\n", - " \n", + "\n", " Returns:\n", " dict with 'suffixes', 'prefixes', and 'common_substrings'\n", " \"\"\"\n", " if not cluster:\n", " return {}\n", - " \n", + "\n", " # Extract potential suffixes (last part after underscore or space)\n", " suffixes = []\n", " prefixes = []\n", - " \n", + "\n", " for col in cluster:\n", " # Get suffix patterns (everything after last underscore, or last 2 parts)\n", - " parts = re.split(r'[_\\s]+', col)\n", + " parts = re.split(r\"[_\\s]+\", col)\n", " if len(parts) >= 2:\n", - " suffixes.append('_'.join(parts[-2:])) # Last 2 parts\n", + " suffixes.append(\"_\".join(parts[-2:])) # Last 2 parts\n", " suffixes.append(parts[-1]) # Just last part\n", " prefixes.append(parts[0]) # First part\n", - " \n", + "\n", " # Count occurrences\n", " suffix_counts = Counter(suffixes)\n", " prefix_counts = Counter(prefixes)\n", - " \n", + "\n", " min_count = len(cluster) * min_frequency\n", - " \n", + "\n", " # Filter patterns that appear frequently enough\n", - " common_suffixes = [pattern for pattern, count in suffix_counts.items() \n", - " if count >= min_count and len(pattern) > 2]\n", - " common_prefixes = [pattern for pattern, count in prefix_counts.items() \n", - " if count >= min_count and len(pattern) > 2]\n", - " \n", + " common_suffixes = [\n", + " pattern\n", + " for pattern, count in suffix_counts.items()\n", + " if count >= min_count and len(pattern) > 2\n", + " ]\n", + " common_prefixes = [\n", + " pattern\n", + " for pattern, count in prefix_counts.items()\n", + " if count >= min_count and len(pattern) > 2\n", + " ]\n", + "\n", " # Find common substrings using the median string approach\n", " common_substrings = []\n", " for col in cluster:\n", " # Extract meaningful parts (longer than 3 chars)\n", - " parts = [p for p in re.split(r'[_\\s]+', col) if len(p) > 3]\n", + " parts = [p for p in re.split(r\"[_\\s]+\", col) if len(p) > 3]\n", " common_substrings.extend(parts)\n", - " \n", + "\n", " substring_counts = Counter(common_substrings)\n", - " common_substrings = [pattern for pattern, count in substring_counts.items() \n", - " if count >= min_count]\n", - " \n", + " common_substrings = [\n", + " pattern for pattern, count in substring_counts.items() if count >= min_count\n", + " ]\n", + "\n", " return {\n", - " 'suffixes': sorted(set(common_suffixes), key=len, reverse=True),\n", - " 'prefixes': sorted(set(common_prefixes), key=len, reverse=True),\n", - " 'substrings': sorted(set(common_substrings), key=len, reverse=True)\n", + " \"suffixes\": sorted(set(common_suffixes), key=len, reverse=True),\n", + " \"prefixes\": sorted(set(common_prefixes), key=len, reverse=True),\n", + " \"substrings\": sorted(set(common_substrings), key=len, reverse=True),\n", " }\n", "\n", "\n", @@ -1177,12 +502,14 @@ "cluster_patterns = []\n", "for idx, cluster in enumerate(final_grouped_clusters):\n", " patterns = extract_common_patterns(cluster, min_frequency=0.7)\n", - " cluster_patterns.append({\n", - " 'cluster_idx': idx,\n", - " 'size': len(cluster),\n", - " 'patterns': patterns,\n", - " 'sample_columns': cluster[:3] # Show first 3 as examples\n", - " })\n", + " cluster_patterns.append(\n", + " {\n", + " \"cluster_idx\": idx,\n", + " \"size\": len(cluster),\n", + " \"patterns\": patterns,\n", + " \"sample_columns\": cluster[:3], # Show first 3 as examples\n", + " }\n", + " )\n", "\n", "# Display pattern summary\n", "print(\"=\" * 100)\n", @@ -1191,14 +518,14 @@ "print(f\"\\nFound {len(cluster_patterns)} clusters with the following patterns:\\n\")\n", "\n", "for cp in cluster_patterns[:15]: # Show first 15 clusters\n", - " print(f\"Cluster {cp['cluster_idx']+1} ({cp['size']} columns)\")\n", + " print(f\"Cluster {cp['cluster_idx'] + 1} ({cp['size']} columns)\")\n", " print(f\" Examples: {cp['sample_columns']}\")\n", - " \n", - " if cp['patterns']['suffixes']:\n", + "\n", + " if cp[\"patterns\"][\"suffixes\"]:\n", " print(f\" Common suffixes: {cp['patterns']['suffixes'][:3]}\")\n", - " if cp['patterns']['prefixes']:\n", + " if cp[\"patterns\"][\"prefixes\"]:\n", " print(f\" Common prefixes: {cp['patterns']['prefixes'][:3]}\")\n", - " if cp['patterns']['substrings']:\n", + " if cp[\"patterns\"][\"substrings\"]:\n", " print(f\" Common substrings: {cp['patterns']['substrings'][:5]}\")\n", " print()" ] @@ -1228,48 +555,24 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CATEGORIZATION RESULTS:\n", - "================================================================================\n", - "\n", - "Features (X matrix): 96 columns\n", - " Examples: ['E2F1_nuc_median', 'cycA_nuc_median', 'cycD1_nuc_median', 'p21_nuc_median', 'Skp2_nuc_median']\n", - "\n", - "Layers: 144 columns\n", - " Examples: ['cycB1_cyto_median', 'cycB1_PN_median', 'cycB1_PM_median', 'Fra1_PM_median', 'cycD1_cyto_median']\n", - "\n", - "Obs (metadata): 2 columns\n", - " Examples: ['age', 'phase']\n", - "\n", - "Excluded: 4 columns\n", - " Examples: ['Nuc_area', 'Cell_area', 'Cyto_area', 'Nuc_shape']\n", - "\n", - "Uncategorized: 49 columns\n", - " Examples: ['Int_Intg_DNA_nuc', 'AreaShape_FormFactor_cell', 'AKT_phospho/total_cell', 'ERK_phospho/total_cell', 'GSK3b_phospho/total_cell', 'RB_phospho/total_cell', 'S6_phospho/total_cell', 'p21_phospho/total_cell', 'p27_phospho/total_cell', 'p53_phospho/total_cell']\n" - ] - } - ], + "outputs": [], "source": [ "def filter_columns_by_patterns(\n", - " columns, \n", + " columns,\n", " clusters,\n", - " feature_patterns=None, \n", - " obs_patterns=None, \n", + " feature_patterns=None,\n", + " obs_patterns=None,\n", " layer_patterns=None,\n", - " exclude_patterns=None\n", + " exclude_patterns=None,\n", "):\n", " \"\"\"\n", " Categorize columns based on pattern matching.\n", - " \n", + "\n", " The clustering helps group similar columns, then you specify which patterns\n", " you want in each category.\n", - " \n", + "\n", " Args:\n", " columns: List of all column names\n", " clusters: The clustered groups from hierarchical clustering\n", @@ -1277,7 +580,7 @@ " obs_patterns: List of patterns for observation metadata\n", " layer_patterns: List of patterns for AnnData layers\n", " exclude_patterns: List of patterns to exclude entirely\n", - " \n", + "\n", " Returns:\n", " dict with keys 'features', 'obs', 'layers', 'excluded', 'uncategorized'\n", " \"\"\"\n", @@ -1289,46 +592,46 @@ " layer_patterns = []\n", " if exclude_patterns is None:\n", " exclude_patterns = []\n", - " \n", + "\n", " result = {\n", - " 'features': [],\n", - " 'obs': [],\n", - " 'layers': [],\n", - " 'excluded': [],\n", - " 'uncategorized': []\n", + " \"features\": [],\n", + " \"obs\": [],\n", + " \"layers\": [],\n", + " \"excluded\": [],\n", + " \"uncategorized\": [],\n", " }\n", - " \n", + "\n", " for col in columns:\n", " categorized = False\n", - " \n", + "\n", " # Check exclusions first\n", " if any(pattern in col for pattern in exclude_patterns):\n", - " result['excluded'].append(col)\n", + " result[\"excluded\"].append(col)\n", " categorized = True\n", " continue\n", - " \n", + "\n", " # Check feature patterns\n", " if any(pattern in col for pattern in feature_patterns):\n", - " result['features'].append(col)\n", + " result[\"features\"].append(col)\n", " categorized = True\n", " continue\n", - " \n", + "\n", " # Check obs patterns\n", " if any(pattern in col for pattern in obs_patterns):\n", - " result['obs'].append(col)\n", + " result[\"obs\"].append(col)\n", " categorized = True\n", " continue\n", - " \n", + "\n", " # Check layer patterns\n", " if any(pattern in col for pattern in layer_patterns):\n", - " result['layers'].append(col)\n", + " result[\"layers\"].append(col)\n", " categorized = True\n", " continue\n", - " \n", + "\n", " # If not categorized, add to uncategorized\n", " if not categorized:\n", - " result['uncategorized'].append(col)\n", - " \n", + " result[\"uncategorized\"].append(col)\n", + "\n", " return result\n", "\n", "\n", @@ -1336,10 +639,14 @@ "example_categorization = filter_columns_by_patterns(\n", " columns=list(csv.columns),\n", " clusters=final_grouped_clusters,\n", - " feature_patterns=['cell_median', 'nuc_median'], # These become your X matrix\n", - " layer_patterns=['PM_median', 'PN_median', 'cyto_median'], # These could go to layers\n", - " obs_patterns=['phase', 'age'], # Metadata\n", - " exclude_patterns=['PHATE', 'area', 'shape'] # Don't want these\n", + " feature_patterns=[\"cell_median\", \"nuc_median\"], # These become your X matrix\n", + " layer_patterns=[\n", + " \"PM_median\",\n", + " \"PN_median\",\n", + " \"cyto_median\",\n", + " ], # These could go to layers\n", + " obs_patterns=[\"phase\", \"age\"], # Metadata\n", + " exclude_patterns=[\"PHATE\", \"area\", \"shape\"], # Don't want these\n", ")\n", "\n", "print(\"CATEGORIZATION RESULTS:\")\n", @@ -1398,34 +705,9 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✓ Features DataFrame: (6797, 96)\n", - "✓ Obs DataFrame: (6797, 2)\n", - "✓ Layers:\n", - " - cyto_median: (6797, 48)\n", - " - PM_median: (6797, 48)\n", - " - PN_median: (6797, 48)\n", - "\n", - "✓ Uncategorized: 48 columns\n", - " These need decisions: ['Int_Intg_DNA_nuc', 'AreaShape_FormFactor_cell', 'AKT_phospho/total_cell', 'ERK_phospho/total_cell', 'GSK3b_phospho/total_cell']...\n", - " (6797, 96)\n", - "✓ Obs DataFrame: (6797, 2)\n", - "✓ Layers:\n", - " - cyto_median: (6797, 48)\n", - " - PM_median: (6797, 48)\n", - " - PN_median: (6797, 48)\n", - "\n", - "✓ Uncategorized: 48 columns\n", - " These need decisions: ['Int_Intg_DNA_nuc', 'AreaShape_FormFactor_cell', 'AKT_phospho/total_cell', 'ERK_phospho/total_cell', 'GSK3b_phospho/total_cell']...\n" - ] - } - ], + "outputs": [], "source": [ "# YOUR DESIRED WORKFLOW: Select the patterns you want\n", "\n", @@ -1434,56 +716,41 @@ "categorized = filter_columns_by_patterns(\n", " columns=list(csv.columns),\n", " clusters=final_grouped_clusters,\n", - " \n", " # YOU DECIDE: Which patterns go where\n", - " feature_patterns=['cell_median', 'nuc_median'], # Main features for analysis\n", - " layer_patterns=['cyto_median', 'PM_median', 'PN_median'], # Alternative measurements\n", - " obs_patterns=['phase', 'age'], # Metadata/annotations\n", - " exclude_patterns=['PHATE', 'area', 'shape', 'density'] # Not needed\n", + " feature_patterns=[\"cell_median\", \"nuc_median\"], # Main features for analysis\n", + " layer_patterns=[\n", + " \"cyto_median\",\n", + " \"PM_median\",\n", + " \"PN_median\",\n", + " ], # Alternative measurements\n", + " obs_patterns=[\"phase\", \"age\"], # Metadata/annotations\n", + " exclude_patterns=[\"PHATE\", \"area\", \"shape\", \"density\"], # Not needed\n", ")\n", "\n", "# 3. Use the categorization to split your dataframe\n", - "features_df = csv[categorized['features']].copy()\n", + "features_df = csv[categorized[\"features\"]].copy()\n", "layers_dict = {\n", - " 'cyto_median': csv[[c for c in categorized['layers'] if 'cyto_median' in c]],\n", - " 'PM_median': csv[[c for c in categorized['layers'] if 'PM_median' in c]],\n", - " 'PN_median': csv[[c for c in categorized['layers'] if 'PN_median' in c]],\n", + " \"cyto_median\": csv[[c for c in categorized[\"layers\"] if \"cyto_median\" in c]],\n", + " \"PM_median\": csv[[c for c in categorized[\"layers\"] if \"PM_median\" in c]],\n", + " \"PN_median\": csv[[c for c in categorized[\"layers\"] if \"PN_median\" in c]],\n", "}\n", - "obs_df = csv[categorized['obs']].copy()\n", + "obs_df = csv[categorized[\"obs\"]].copy()\n", "\n", "print(\"✓ Features DataFrame:\", features_df.shape)\n", "print(\"✓ Obs DataFrame:\", obs_df.shape)\n", "print(\"✓ Layers:\")\n", "for layer_name, layer_df in layers_dict.items():\n", " print(f\" - {layer_name}: {layer_df.shape}\")\n", - " \n", + "\n", "print(f\"\\n✓ Uncategorized: {len(categorized['uncategorized'])} columns\")\n", "print(f\" These need decisions: {categorized['uncategorized'][:5]}...\")" ] }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "UNCATEGORIZED COLUMNS BY CLUSTER:\n", - "================================================================================\n", - "\n", - "Cluster 4 (4 uncategorized / 4 total)\n", - " Patterns: nuc\n", - " Columns: ['Int_Intg_DNA_nuc', 'cycD1_over_p21', 'CDK2_cyto_over_nuc', 'cyto_over_DNA']\n", - "\n", - "Cluster 6 (1 uncategorized / 1 total)\n", - " Patterns: FormFactor_cell, cell\n", - " Columns: ['AreaShape_FormFactor_cell']\n" - ] - } - ], + "outputs": [], "source": [ "# Show uncategorized columns organized by their clusters\n", "# This helps you decide what to do with them\n", @@ -1491,21 +758,23 @@ "print(\"\\nUNCATEGORIZED COLUMNS BY CLUSTER:\")\n", "print(\"=\" * 80)\n", "\n", - "uncategorized_set = set(categorized['uncategorized'])\n", + "uncategorized_set = set(categorized[\"uncategorized\"])\n", "\n", "for idx, cluster in enumerate(final_grouped_clusters):\n", " # Check if this cluster has any uncategorized columns\n", " uncategorized_in_cluster = [col for col in cluster if col in uncategorized_set]\n", - " \n", + "\n", " if uncategorized_in_cluster:\n", " patterns = extract_common_patterns(cluster, min_frequency=0.5)\n", - " print(f\"\\nCluster {idx+1} ({len(uncategorized_in_cluster)} uncategorized / {len(cluster)} total)\")\n", - " \n", - " if patterns['suffixes']:\n", + " print(\n", + " f\"\\nCluster {idx + 1} ({len(uncategorized_in_cluster)} uncategorized / {len(cluster)} total)\"\n", + " )\n", + "\n", + " if patterns[\"suffixes\"]:\n", " print(f\" Patterns: {', '.join(patterns['suffixes'][:3])}\")\n", - " elif patterns['substrings']:\n", + " elif patterns[\"substrings\"]:\n", " print(f\" Common words: {', '.join(patterns['substrings'][:3])}\")\n", - " \n", + "\n", " print(f\" Columns: {uncategorized_in_cluster[:5]}\")\n", " if len(uncategorized_in_cluster) > 5:\n", " print(f\" ... and {len(uncategorized_in_cluster) - 5} more\")" @@ -1542,100 +811,78 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUTOMATIC HEURISTIC CATEGORIZATION:\n", - "================================================================================\n", - "\n", - "✓ Features (X matrix): 133 columns\n", - " Examples: ['E2F1_nuc_median', 'cycA_nuc_median', 'cycD1_nuc_median', 'p21_nuc_median', 'Int_Intg_DNA_nuc', 'Skp2_nuc_median', 'Cdt1_nuc_median', 'Cdh1_nuc_median']\n", - "\n", - "✓ Layers: 154 columns\n", - " Examples: ['cycB1_cyto_median', 'cycB1_PN_median', 'cycB1_PM_median', 'Fra1_PM_median', 'cycD1_cyto_median']\n", - "\n", - "✓ Obs (metadata): 2 columns\n", - " All obs: ['age', 'phase']\n", - "\n", - "✓ Excluded: 6 columns\n", - " Examples: ['Nuc_area', 'Cell_area', 'Cyto_area', 'Nuc_shape', 'AreaShape_FormFactor_cell']\n", - "\n", - "⚠ Uncategorized: 0 columns (need rules or manual review)\n", - " Examples: []\n" - ] - } - ], + "outputs": [], "source": [ "def auto_categorize_columns_heuristic(columns, clusters):\n", " \"\"\"\n", " Automatic categorization using simple heuristics - NO LLM needed!\n", - " \n", + "\n", " This uses rules based on common naming patterns in biology datasets.\n", " You can customize these rules for your specific use case.\n", " \"\"\"\n", - " \n", + "\n", " result = {\n", - " 'features': [],\n", - " 'obs': [],\n", - " 'layers': [],\n", - " 'excluded': [],\n", - " 'uncategorized': []\n", + " \"features\": [],\n", + " \"obs\": [],\n", + " \"layers\": [],\n", + " \"excluded\": [],\n", + " \"uncategorized\": [],\n", " }\n", - " \n", + "\n", " for col in columns:\n", " col_lower = col.lower()\n", - " \n", + "\n", " # Rule 1: PHATE/UMAP/tSNE are dimensionality reductions -> exclude from features\n", - " if any(x in col_lower for x in ['phate', 'umap', 'tsne', 'pca']):\n", - " result['excluded'].append(col)\n", - " \n", - " # Rule 2: Shape and area measurements -> often excluded or separate layer\n", - " elif any(x in col_lower for x in ['area', 'shape', 'formfactor', 'density']):\n", - " result['excluded'].append(col)\n", - " \n", + " if any(x in col_lower for x in [\"phate\", \"umap\", \"tsne\", \"pca\"]) or any(\n", + " x in col_lower for x in [\"area\", \"shape\", \"formfactor\", \"density\"]\n", + " ):\n", + " result[\"excluded\"].append(col)\n", + "\n", " # Rule 3: Phase, age, condition, treatment -> metadata (obs)\n", - " elif any(x in col_lower for x in ['phase', 'age', 'condition', 'treatment', 'timepoint', 'replicate']):\n", - " result['obs'].append(col)\n", - " \n", + " elif any(\n", + " x in col_lower\n", + " for x in [\n", + " \"phase\",\n", + " \"age\",\n", + " \"condition\",\n", + " \"treatment\",\n", + " \"timepoint\",\n", + " \"replicate\",\n", + " ]\n", + " ):\n", + " result[\"obs\"].append(col)\n", + "\n", " # Rule 4: Main feature patterns - nucleus and cell medians are typically primary features\n", - " elif any(pattern in col for pattern in ['nuc_median', 'cell_median']):\n", - " result['features'].append(col)\n", - " \n", + " elif any(pattern in col for pattern in [\"nuc_median\", \"cell_median\"]):\n", + " result[\"features\"].append(col)\n", + "\n", " # Rule 5: Cytoplasm medians could be layers (alternative measurements)\n", - " elif 'cyto_median' in col:\n", - " result['layers'].append(col)\n", - " \n", - " # Rule 6: PM/PN ratios or perinuclear measurements -> layers\n", - " elif any(x in col for x in ['PM_median', 'PN_median', '_PM', '_PN']):\n", - " result['layers'].append(col)\n", - " \n", + " elif \"cyto_median\" in col or any(\n", + " x in col for x in [\"PM_median\", \"PN_median\", \"_PM\", \"_PN\"]\n", + " ):\n", + " result[\"layers\"].append(col)\n", + "\n", " # Rule 7: Phospho/total ratios -> often features\n", - " elif 'phospho/total' in col:\n", - " result['features'].append(col)\n", - " \n", - " # Rule 8: Integrated intensity, DNA content -> features\n", - " elif any(x in col for x in ['Int_Intg', 'DNA_content', 'foci']):\n", - " result['features'].append(col)\n", - " \n", - " # Rule 9: Ratios (with \"_over_\" or \"/\") -> features\n", - " elif '_over_' in col or ('/' in col and 'phospho' not in col):\n", - " result['features'].append(col)\n", - " \n", + " elif (\n", + " \"phospho/total\" in col\n", + " or any(x in col for x in [\"Int_Intg\", \"DNA_content\", \"foci\"])\n", + " or \"_over_\" in col\n", + " or (\"/\" in col and \"phospho\" not in col)\n", + " ):\n", + " result[\"features\"].append(col)\n", + "\n", " # Everything else -> uncategorized (needs manual review)\n", " else:\n", - " result['uncategorized'].append(col)\n", - " \n", + " result[\"uncategorized\"].append(col)\n", + "\n", " return result\n", "\n", "\n", "# Test automatic categorization\n", "auto_categorized = auto_categorize_columns_heuristic(\n", - " columns=list(csv.columns),\n", - " clusters=final_grouped_clusters\n", + " columns=list(csv.columns), clusters=final_grouped_clusters\n", ")\n", "\n", "print(\"AUTOMATIC HEURISTIC CATEGORIZATION:\")\n", @@ -1648,7 +895,9 @@ "print(f\" All obs: {auto_categorized['obs']}\")\n", "print(f\"\\n✓ Excluded: {len(auto_categorized['excluded'])} columns\")\n", "print(f\" Examples: {auto_categorized['excluded'][:5]}\")\n", - "print(f\"\\n⚠ Uncategorized: {len(auto_categorized['uncategorized'])} columns (need rules or manual review)\")\n", + "print(\n", + " f\"\\n⚠ Uncategorized: {len(auto_categorized['uncategorized'])} columns (need rules or manual review)\"\n", + ")\n", "print(f\" Examples: {auto_categorized['uncategorized'][:10]}\")" ] }, @@ -1684,24 +933,9 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Value passed for key 'cyto_median' is of incorrect shape. Values of layers must match dimensions ('obs', 'var') of parent. Value had shape (6797, 48) while it should have had (6797, 143).", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[68], line 129\u001b[0m\n\u001b[0;32m 125\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m adata\n\u001b[0;32m 128\u001b[0m \u001b[38;5;66;03m# Test the automated pipeline\u001b[39;00m\n\u001b[1;32m--> 129\u001b[0m adata_auto, diagnostics \u001b[38;5;241m=\u001b[39m \u001b[43mcsv_to_anndata_auto\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 130\u001b[0m \u001b[43m \u001b[49m\u001b[43mcsv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 131\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_diagnostics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[0;32m 132\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAUTOMATED PIPELINE RESULTS:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 135\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m=\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m80\u001b[39m)\n", - "Cell \u001b[1;32mIn[68], line 110\u001b[0m, in \u001b[0;36mcsv_to_anndata_auto\u001b[1;34m(df, feature_rules, layer_rules, obs_rules, exclude_rules, clustering_threshold, return_diagnostics)\u001b[0m\n\u001b[0;32m 108\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m layer_name, layer_cols \u001b[38;5;129;01min\u001b[39;00m layer_patterns\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 109\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m layer_cols:\n\u001b[1;32m--> 110\u001b[0m \u001b[43madata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlayer_name\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m df[layer_cols]\u001b[38;5;241m.\u001b[39mvalues\n\u001b[0;32m 112\u001b[0m \u001b[38;5;66;03m# Return with diagnostics if requested\u001b[39;00m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m return_diagnostics:\n", - "File \u001b[1;32mc:\\Users\\dap182\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\cc-mapping-UTHkMOyl-py3.10\\lib\\site-packages\\anndata\\_core\\aligned_mapping.py:216\u001b[0m, in \u001b[0;36mAlignedActual.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__setitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key: \u001b[38;5;28mstr\u001b[39m, value: Value):\n\u001b[1;32m--> 216\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_value\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 217\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data[key] \u001b[38;5;241m=\u001b[39m value\n", - "File \u001b[1;32mc:\\Users\\dap182\\AppData\\Local\\pypoetry\\Cache\\virtualenvs\\cc-mapping-UTHkMOyl-py3.10\\lib\\site-packages\\anndata\\_core\\aligned_mapping.py:97\u001b[0m, in \u001b[0;36mAlignedMappingBase._validate_value\u001b[1;34m(self, val, key)\u001b[0m\n\u001b[0;32m 91\u001b[0m dims \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobs\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvar\u001b[39m\u001b[38;5;124m\"\u001b[39m)[ax] \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m 92\u001b[0m msg \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 93\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValue passed for key \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m is of incorrect shape. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 94\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValues of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must match dimensions \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mdims\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m of parent. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 95\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mValue had shape \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mactual_shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m while it should have had \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mright_shape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 96\u001b[0m )\n\u001b[1;32m---> 97\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[0;32m 99\u001b[0m name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mattrname\u001b[38;5;241m.\u001b[39mtitle()\u001b[38;5;241m.\u001b[39mrstrip(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124ms\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 100\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m coerce_array(val, name\u001b[38;5;241m=\u001b[39mname, allow_df\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_allow_df)\n", - "\u001b[1;31mValueError\u001b[0m: Value passed for key 'cyto_median' is of incorrect shape. Values of layers must match dimensions ('obs', 'var') of parent. Value had shape (6797, 48) while it should have had (6797, 143)." - ] - } - ], + "outputs": [], "source": [ "def csv_to_anndata_auto(\n", " df,\n", @@ -1710,16 +944,16 @@ " obs_rules=None,\n", " exclude_rules=None,\n", " clustering_threshold=0.80,\n", - " return_diagnostics=False\n", + " return_diagnostics=False,\n", "):\n", " \"\"\"\n", " Automated pipeline: CSV → AnnData with minimal user input.\n", - " \n", + "\n", " Steps:\n", " 1. Cluster similar column names\n", " 2. Auto-categorize columns using heuristic rules\n", " 3. Build AnnData object with proper structure\n", - " \n", + "\n", " Args:\n", " df: Input DataFrame\n", " feature_rules: Custom rules for features (optional, uses defaults)\n", @@ -1728,118 +962,131 @@ " exclude_rules: Custom rules for exclusions (optional, uses defaults)\n", " clustering_threshold: Levenshtein distance threshold for clustering\n", " return_diagnostics: If True, returns (adata, diagnostics_dict)\n", - " \n", + "\n", " Returns:\n", " AnnData object (or tuple with diagnostics if requested)\n", " \"\"\"\n", - " \n", + "\n", " # Default rules (can be overridden)\n", " if feature_rules is None:\n", " feature_rules = [\n", - " lambda col: 'nuc_median' in col,\n", - " lambda col: 'cell_median' in col,\n", - " lambda col: 'phospho/total' in col,\n", - " lambda col: 'Int_Intg' in col,\n", - " lambda col: '_over_' in col and 'phospho' not in col,\n", - " lambda col: 'DNA_content' in col,\n", - " lambda col: 'foci' in col,\n", + " lambda col: \"nuc_median\" in col,\n", + " lambda col: \"cell_median\" in col,\n", + " lambda col: \"phospho/total\" in col,\n", + " lambda col: \"Int_Intg\" in col,\n", + " lambda col: \"_over_\" in col and \"phospho\" not in col,\n", + " lambda col: \"DNA_content\" in col,\n", + " lambda col: \"foci\" in col,\n", " ]\n", - " \n", + "\n", " if layer_rules is None:\n", " layer_rules = [\n", - " lambda col: 'cyto_median' in col,\n", - " lambda col: 'PM_median' in col or '_PM' in col,\n", - " lambda col: 'PN_median' in col or '_PN' in col,\n", + " lambda col: \"cyto_median\" in col,\n", + " lambda col: \"PM_median\" in col or \"_PM\" in col,\n", + " lambda col: \"PN_median\" in col or \"_PN\" in col,\n", " ]\n", - " \n", + "\n", " if obs_rules is None:\n", " obs_rules = [\n", - " lambda col: col.lower() in ['phase', 'age', 'condition', 'treatment', 'timepoint'],\n", + " lambda col: (\n", + " col.lower() in [\"phase\", \"age\", \"condition\", \"treatment\", \"timepoint\"]\n", + " ),\n", " ]\n", - " \n", + "\n", " if exclude_rules is None:\n", " exclude_rules = [\n", - " lambda col: any(x in col.lower() for x in ['phate', 'umap', 'tsne', 'pca']),\n", - " lambda col: any(x in col.lower() for x in ['area', 'shape', 'formfactor', 'density']),\n", + " lambda col: any(x in col.lower() for x in [\"phate\", \"umap\", \"tsne\", \"pca\"]),\n", + " lambda col: any(\n", + " x in col.lower() for x in [\"area\", \"shape\", \"formfactor\", \"density\"]\n", + " ),\n", " ]\n", - " \n", + "\n", " # Step 1: Cluster columns\n", " columns = list(df.columns)\n", " clusters = group_columns_hierarchical_internal_sort(\n", " columns,\n", " normalized_threshold=clustering_threshold,\n", - " linkage_method='average',\n", - " sort_clusters_by='size'\n", + " linkage_method=\"average\",\n", + " sort_clusters_by=\"size\",\n", " )\n", - " \n", + "\n", " # Step 2: Categorize columns\n", - " categorized = {'features': [], 'layers': [], 'obs': [], 'excluded': [], 'uncategorized': []}\n", - " \n", + " categorized = {\n", + " \"features\": [],\n", + " \"layers\": [],\n", + " \"obs\": [],\n", + " \"excluded\": [],\n", + " \"uncategorized\": [],\n", + " }\n", + "\n", " for col in columns:\n", " # Check each rule category in order\n", " if any(rule(col) for rule in exclude_rules):\n", - " categorized['excluded'].append(col)\n", + " categorized[\"excluded\"].append(col)\n", " elif any(rule(col) for rule in obs_rules):\n", - " categorized['obs'].append(col)\n", + " categorized[\"obs\"].append(col)\n", " elif any(rule(col) for rule in feature_rules):\n", - " categorized['features'].append(col)\n", + " categorized[\"features\"].append(col)\n", " elif any(rule(col) for rule in layer_rules):\n", - " categorized['layers'].append(col)\n", + " categorized[\"layers\"].append(col)\n", " else:\n", - " categorized['uncategorized'].append(col)\n", - " \n", + " categorized[\"uncategorized\"].append(col)\n", + "\n", " # Step 3: Build AnnData\n", " # Features go in X\n", - " X = df[categorized['features']].values if categorized['features'] else None\n", - " \n", + " X = df[categorized[\"features\"]].values if categorized[\"features\"] else None\n", + "\n", " # Obs goes in obs\n", - " obs_df = df[categorized['obs']].copy() if categorized['obs'] else None\n", - " \n", + " obs_df = df[categorized[\"obs\"]].copy() if categorized[\"obs\"] else None\n", + "\n", " # Var metadata\n", - " var_df = pd.DataFrame(index=categorized['features']) if categorized['features'] else None\n", - " \n", + " var_df = (\n", + " pd.DataFrame(index=categorized[\"features\"]) if categorized[\"features\"] else None\n", + " )\n", + "\n", " # Create base AnnData\n", " adata = ad.AnnData(X=X, obs=obs_df, var=var_df)\n", - " \n", + "\n", " # Add layers if any\n", - " if categorized['layers']:\n", + " if categorized[\"layers\"]:\n", " # Group layers by pattern\n", " layer_patterns = {\n", - " 'cyto_median': [c for c in categorized['layers'] if 'cyto_median' in c],\n", - " 'PM_median': [c for c in categorized['layers'] if 'PM_median' in c or '_PM' in c],\n", - " 'PN_median': [c for c in categorized['layers'] if 'PN_median' in c or '_PN' in c],\n", + " \"cyto_median\": [c for c in categorized[\"layers\"] if \"cyto_median\" in c],\n", + " \"PM_median\": [\n", + " c for c in categorized[\"layers\"] if \"PM_median\" in c or \"_PM\" in c\n", + " ],\n", + " \"PN_median\": [\n", + " c for c in categorized[\"layers\"] if \"PN_median\" in c or \"_PN\" in c\n", + " ],\n", " }\n", - " \n", + "\n", " for layer_name, layer_cols in layer_patterns.items():\n", " if layer_cols:\n", " adata.layers[layer_name] = df[layer_cols].values\n", - " \n", + "\n", " # Return with diagnostics if requested\n", " if return_diagnostics:\n", " diagnostics = {\n", - " 'clusters': clusters,\n", - " 'categorization': categorized,\n", - " 'n_features': len(categorized['features']),\n", - " 'n_layers': len(categorized['layers']),\n", - " 'n_obs_columns': len(categorized['obs']),\n", - " 'n_excluded': len(categorized['excluded']),\n", - " 'n_uncategorized': len(categorized['uncategorized']),\n", + " \"clusters\": clusters,\n", + " \"categorization\": categorized,\n", + " \"n_features\": len(categorized[\"features\"]),\n", + " \"n_layers\": len(categorized[\"layers\"]),\n", + " \"n_obs_columns\": len(categorized[\"obs\"]),\n", + " \"n_excluded\": len(categorized[\"excluded\"]),\n", + " \"n_uncategorized\": len(categorized[\"uncategorized\"]),\n", " }\n", " return adata, diagnostics\n", - " \n", + "\n", " return adata\n", "\n", "\n", "# Test the automated pipeline\n", - "adata_auto, diagnostics = csv_to_anndata_auto(\n", - " csv,\n", - " return_diagnostics=True\n", - ")\n", + "adata_auto, diagnostics = csv_to_anndata_auto(csv, return_diagnostics=True)\n", "\n", "print(\"AUTOMATED PIPELINE RESULTS:\")\n", "print(\"=\" * 80)\n", "print(f\"\\n✓ AnnData object created: {adata_auto}\")\n", - "print(f\"\\n✓ Diagnostics:\")\n", + "print(\"\\n✓ Diagnostics:\")\n", "print(f\" - Features (X): {diagnostics['n_features']} columns\")\n", "print(f\" - Layers: {diagnostics['n_layers']} columns\")\n", "print(f\" - Obs metadata: {diagnostics['n_obs_columns']} columns\")\n", diff --git a/notebooks/Sequential_Thresholding_Workflow.ipynb b/notebooks/Sequential_Thresholding_Workflow.ipynb index 81fd694..1198414 100644 --- a/notebooks/Sequential_Thresholding_Workflow.ipynb +++ b/notebooks/Sequential_Thresholding_Workflow.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "db722da2", + "id": "0", "metadata": {}, "source": [ "# Sequential GMM Thresholding for Cell Cycle Staging\n", @@ -30,7 +30,7 @@ }, { "cell_type": "markdown", - "id": "0105d257", + "id": "1", "metadata": {}, "source": [ "---\n", @@ -41,7 +41,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0a34c75f", + "id": "2", "metadata": {}, "outputs": [], "source": [ @@ -49,9 +49,9 @@ "from urllib.request import urlretrieve\n", "\n", "import anndata as ad\n", - "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", "from cc_mapping.thresholding import SequentialGMM\n", "\n", @@ -60,8 +60,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "a4575792", + "execution_count": null, + "id": "3", "metadata": {}, "outputs": [], "source": [ @@ -77,19 +77,10 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "ae595e79", + "execution_count": null, + "id": "4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Results directory created at: c:\\Users\\dap182\\Documents\\git\\cc_mapping\\notebooks\\results\n", - "Sequential results directory: c:\\Users\\dap182\\Documents\\git\\cc_mapping\\notebooks\\results\\sequential\n" - ] - } - ], + "outputs": [], "source": [ "# Create results directory structure for saving figures\n", "results_dir = cwd / \"results\"\n", @@ -105,43 +96,30 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "66f0c363", + "execution_count": null, + "id": "5", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset: 6797 cells x 289 features\n", - "\n", - "Features we'll use:\n", - " - pRB (nuc median): Initial G0 vs cycling split\n", - " - pp21 (nuc median): Separate M phase\n", - " - Int_Intg_DNA_nuc: Split G1/S/G2\n" - ] - } - ], + "outputs": [], "source": [ "# Load and convert to AnnData\n", "csv = pd.read_csv(download_path, index_col=0, low_memory=False)\n", "csv.index = csv.index.astype(str)\n", "\n", "adata = ad.AnnData(\n", - " X=csv.values[:,:-10].astype(np.float32),\n", + " X=csv.values[:, :-10].astype(np.float32),\n", ")\n", "adata.var_names = csv.columns.values[:-10]\n", "\n", "print(f\"Dataset: {adata.n_obs} cells x {adata.n_vars} features\")\n", - "print(f\"\\nFeatures we'll use:\")\n", - "print(f\" - pRB (nuc median): Initial G0 vs cycling split\")\n", - "print(f\" - pp21 (nuc median): Separate M phase\")\n", - "print(f\" - Int_Intg_DNA_nuc: Split G1/S/G2\")" + "print(\"\\nFeatures we'll use:\")\n", + "print(\" - pRB (nuc median): Initial G0 vs cycling split\")\n", + "print(\" - pp21 (nuc median): Separate M phase\")\n", + "print(\" - Int_Intg_DNA_nuc: Split G1/S/G2\")" ] }, { "cell_type": "markdown", - "id": "26f88a2a", + "id": "6", "metadata": {}, "source": [ "### Filter Missing Values\n", @@ -151,26 +129,13 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "456d4158", + "execution_count": null, + "id": "7", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cells with NaN values: 1 / 6797\n", - "After filtering: 6796 cells remaining\n" - ] - } - ], + "outputs": [], "source": [ "# Define the features we'll use for sequential thresholding\n", - "features_to_use = [\n", - " 'pRB (nuc median)', \n", - " 'pp21 (nuc median)', \n", - " 'Int_Intg_DNA_nuc'\n", - "]\n", + "features_to_use = [\"pRB (nuc median)\", \"pp21 (nuc median)\", \"Int_Intg_DNA_nuc\"]\n", "\n", "# Get feature indices\n", "feature_indices = [list(adata.var_names).index(f) for f in features_to_use]\n", @@ -190,7 +155,7 @@ }, { "cell_type": "markdown", - "id": "9f0b09b8", + "id": "8", "metadata": {}, "source": [ "---\n", @@ -202,32 +167,23 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "746af457", + "execution_count": null, + "id": "9", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sequential thresholding object initialized!\n", - "Metadata will be stored in: adata.uns['sequential_gmm_thresholding']\n" - ] - } - ], + "outputs": [], "source": [ "# Initialize the sequential thresholding object\n", "gmm_kwargs = {\n", - " 'init_params': 'k-means++', \n", - " 'n_init': 10, \n", - " 'max_iter': 1000, \n", - " 'random_state': 42\n", + " \"init_params\": \"k-means++\",\n", + " \"n_init\": 10,\n", + " \"max_iter\": 1000,\n", + " \"random_state\": 42,\n", "}\n", "\n", "seq_gmm = SequentialGMM(\n", " adata=adata,\n", - " thresholding_events_key='sequential_gmm_thresholding',\n", - " gmm_kwargs=gmm_kwargs\n", + " thresholding_events_key=\"sequential_gmm_thresholding\",\n", + " gmm_kwargs=gmm_kwargs,\n", ")\n", "\n", "print(\"Sequential thresholding object initialized!\")\n", @@ -236,7 +192,7 @@ }, { "cell_type": "markdown", - "id": "b5109009", + "id": "10", "metadata": {}, "source": [ "### Exploratory Visualization\n", @@ -246,88 +202,53 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "448c420f", + "execution_count": null, + "id": "11", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Explore pRB distribution across all cells\n", "seq_gmm.plot_feature_distribution_exploratory(\n", - " feature='pRB (nuc median)',\n", - " hist_kwargs={'bins': 50, 'color': 'steelblue', 'alpha': 0.7},\n", + " feature=\"pRB (nuc median)\",\n", + " hist_kwargs={\"bins\": 50, \"color\": \"steelblue\", \"alpha\": 0.7},\n", ")\n", - "plt.title('Exploratory: pRB Distribution (All Cells)')\n", + "plt.title(\"Exploratory: pRB Distribution (All Cells)\")\n", "plt.show()\n", "\n", "# Or use the strip plot version for better visualization\n", "fig, (ax_strip, ax_hist) = seq_gmm.plot_feature_strip_plot_exploratory(\n", - " feature='pRB (nuc median)',\n", - " hist_kwargs={'bins': 50, 'color': 'steelblue'},\n", - " scatter_density=True\n", + " feature=\"pRB (nuc median)\",\n", + " hist_kwargs={\"bins\": 50, \"color\": \"steelblue\"},\n", + " scatter_density=True,\n", ")\n", - "plt.suptitle('Exploratory: pRB Distribution - Looking for 2 components')\n", + "plt.suptitle(\"Exploratory: pRB Distribution - Looking for 2 components\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "f3c4aef7", + "execution_count": null, + "id": "12", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial thresholding complete!\n", - "\n", - "Label distribution:\n", - "cell_cycle_phase\n", - "G1/S/G2/M 6101\n", - "G0 695\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "# Perform initial thresholding: G0 vs G1/S/G2/M\n", "seq_gmm.threshold_entire_dataset(\n", - " feature='pRB (nuc median)',\n", - " label_obs_save_str='cell_cycle_phase', # This column will store our labels\n", + " feature=\"pRB (nuc median)\",\n", + " label_obs_save_str=\"cell_cycle_phase\", # This column will store our labels\n", " n_components=2,\n", - " ordered_labels=['G0', 'G1/S/G2/M'],\n", - " operation_name='initial_pRB_split'\n", + " ordered_labels=[\"G0\", \"G1/S/G2/M\"],\n", + " operation_name=\"initial_pRB_split\",\n", ")\n", "\n", "print(\"Initial thresholding complete!\")\n", - "print(f\"\\nLabel distribution:\")\n", - "print(seq_gmm.adata.obs['cell_cycle_phase'].value_counts())" + "print(\"\\nLabel distribution:\")\n", + "print(seq_gmm.adata.obs[\"cell_cycle_phase\"].value_counts())" ] }, { "cell_type": "markdown", - "id": "dd488677", + "id": "13", "metadata": {}, "source": [ "### Visualize Results" @@ -335,47 +256,29 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "4aa28551", + "execution_count": null, + "id": "14", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Figure saved to: c:\\Users\\dap182\\Documents\\git\\cc_mapping\\notebooks\\results\\sequential\\step1_pRB_split.png\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "hist_kwargs = {\n", - " 'bins': 50, 'color': \"black\"}\n", + "hist_kwargs = {\"bins\": 50, \"color\": \"black\"}\n", "\n", "# Plot histogram with decision boundaries\n", "seq_gmm.plot_hist_distribution_with_boundaries(\n", - " operation_name='initial_pRB_split',\n", + " operation_name=\"initial_pRB_split\",\n", " resolution=1000,\n", " num_std=5,\n", " hist_kwargs=hist_kwargs,\n", - " save_path=sequential_results_dir / 'step1_pRB_split.png' # Optional: save the figure\n", + " save_path=sequential_results_dir\n", + " / \"step1_pRB_split.png\", # Optional: save the figure\n", ")\n", - "plt.title('Initial Split: G0 vs G1/S/G2/M (pRB)')\n", + "plt.title(\"Initial Split: G0 vs G1/S/G2/M (pRB)\")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "2ce61f09", + "id": "15", "metadata": {}, "source": [ "**Note:** The histogram shows the pRB distribution at the time of this operation. After subsequent refinements (M, G1, S, G2), only cells that still have the original labels (G0 or G1/S/G2/M) are shown in the histogram." @@ -383,36 +286,23 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "0bd07d73", + "execution_count": null, + "id": "16", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Strip plot with labels\n", - "hist_kwargs = {\"bins\": 100, 'color': 'black'}\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", "seq_gmm.plot_strip_plot_histogram_with_decision_boundaries(\n", - " operation_name='initial_pRB_split',\n", - " hist_kwargs=hist_kwargs,\n", - " scatter_density=True\n", + " operation_name=\"initial_pRB_split\", hist_kwargs=hist_kwargs, scatter_density=True\n", ")\n", - "plt.suptitle('Initial Split: G0 vs G1/S/G2/M (pRB)')\n", + "plt.suptitle(\"Initial Split: G0 vs G1/S/G2/M (pRB)\")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "4803c308", + "id": "17", "metadata": {}, "source": [ "---\n", @@ -424,7 +314,7 @@ }, { "cell_type": "markdown", - "id": "5c10f2fd", + "id": "18", "metadata": {}, "source": [ "### Exploratory Visualization" @@ -432,31 +322,20 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "929fb8e9", + "execution_count": null, + "id": "19", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Explore p21 distribution in G1/S/G2/M cells only (not G0)\n", "fig, (ax_strip, ax_hist) = seq_gmm.plot_feature_strip_plot_exploratory(\n", - " feature='pp21 (nuc median)',\n", - " obs_label='cell_cycle_phase',\n", - " value_to_subset='G1/S/G2/M', # Only look at cycling cells\n", - " hist_kwargs={'bins': 50, 'color': 'black'},\n", - " scatter_density=True\n", + " feature=\"pp21 (nuc median)\",\n", + " obs_label=\"cell_cycle_phase\",\n", + " value_to_subset=\"G1/S/G2/M\", # Only look at cycling cells\n", + " hist_kwargs={\"bins\": 50, \"color\": \"black\"},\n", + " scatter_density=True,\n", ")\n", - "plt.suptitle('Exploratory: p21 in G1/S/G2/M Cells - Looking for M phase (low p21)')\n", + "plt.suptitle(\"Exploratory: p21 in G1/S/G2/M Cells - Looking for M phase (low p21)\")\n", "plt.show()\n", "\n", "# This helps us see if 2 components are appropriate and where the boundary might be" @@ -464,46 +343,30 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "8a8baa1c", + "execution_count": null, + "id": "20", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First refinement complete!\n", - "\n", - "Updated label distribution:\n", - "cell_cycle_phase\n", - "G1/S/G2 6067\n", - "G0 695\n", - "M 34\n", - "G1/S/G2/M 0\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "# Refine G1/S/G2/M population to separate M phase\n", "seq_gmm.refine_labels_with_gmm(\n", - " feature='pp21 (nuc median)',\n", - " obs_label='cell_cycle_phase',\n", - " value_to_refine='G1/S/G2/M',\n", + " feature=\"pp21 (nuc median)\",\n", + " obs_label=\"cell_cycle_phase\",\n", + " value_to_refine=\"G1/S/G2/M\",\n", " n_components=2,\n", - " ordered_labels=['G1/S/G2','M'],\n", - " operation_name='separate_M_phase',\n", - " duplicate_labels=False\n", + " ordered_labels=[\"G1/S/G2\", \"M\"],\n", + " operation_name=\"separate_M_phase\",\n", + " duplicate_labels=False,\n", ")\n", "\n", "print(\"First refinement complete!\")\n", - "print(f\"\\nUpdated label distribution:\")\n", - "print(seq_gmm.adata.obs['cell_cycle_phase'].value_counts())" + "print(\"\\nUpdated label distribution:\")\n", + "print(seq_gmm.adata.obs[\"cell_cycle_phase\"].value_counts())" ] }, { "cell_type": "markdown", - "id": "76724e7d", + "id": "21", "metadata": {}, "source": [ "### Visualize Results" @@ -511,62 +374,40 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "82b6a836", + "execution_count": null, + "id": "22", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the M phase separation\n", "seq_gmm.plot_hist_distribution_with_boundaries(\n", - " operation_name='separate_M_phase',\n", + " operation_name=\"separate_M_phase\",\n", ")\n", - "plt.title('First Refinement: Separate M Phase (p21)')\n", + "plt.title(\"First Refinement: Separate M Phase (p21)\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "e0621660", + "execution_count": null, + "id": "23", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Strip plot\n", - "hist_kwargs = {\"bins\": 100, 'color': 'black'}\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", "seq_gmm.plot_strip_plot_histogram_with_decision_boundaries(\n", - " operation_name='separate_M_phase',\n", + " operation_name=\"separate_M_phase\",\n", " hist_kwargs=hist_kwargs,\n", " scatter_density=False,\n", - " title='First Refinement: Separate M Phase (p21)'\n", + " title=\"First Refinement: Separate M Phase (p21)\",\n", ")\n", "plt.show()" ] }, { "cell_type": "markdown", - "id": "670c3786", + "id": "24", "metadata": {}, "source": [ "---\n", @@ -578,7 +419,7 @@ }, { "cell_type": "markdown", - "id": "15d3de39", + "id": "25", "metadata": {}, "source": [ "### Exploratory Visualization\n", @@ -588,32 +429,21 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "300264c7", + "execution_count": null, + "id": "26", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Explore DNA content distribution in G1/S/G2 cells only\n", "fig, (ax_strip, ax_hist) = seq_gmm.plot_feature_strip_plot_exploratory(\n", - " feature='Int_Intg_DNA_nuc',\n", - " obs_label='cell_cycle_phase',\n", - " value_to_subset='G1/S/G2', # Only look at G1/S/G2 cells (not M or G0)\n", - " hist_kwargs={'bins': 50, 'color': 'mediumseagreen'},\n", + " feature=\"Int_Intg_DNA_nuc\",\n", + " obs_label=\"cell_cycle_phase\",\n", + " value_to_subset=\"G1/S/G2\", # Only look at G1/S/G2 cells (not M or G0)\n", + " hist_kwargs={\"bins\": 50, \"color\": \"mediumseagreen\"},\n", " scatter_density=True,\n", - " x_axis_limits=(3, 15)\n", + " x_axis_limits=(3, 15),\n", ")\n", - "plt.suptitle('Exploratory: DNA Content in G1/S/G2 Cells - Looking for 3 phases')\n", + "plt.suptitle(\"Exploratory: DNA Content in G1/S/G2 Cells - Looking for 3 phases\")\n", "plt.show()\n", "\n", "# From this plot, we can:\n", @@ -624,48 +454,29 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "be34b921", + "execution_count": null, + "id": "27", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Second refinement complete!\n", - "\n", - "Final label distribution:\n", - "cell_cycle_phase\n", - "G1 3968\n", - "S 1151\n", - "G2 948\n", - "G0 695\n", - "M 34\n", - "G1/S/G2 0\n", - "G1/S/G2/M 0\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "# Refine G1/S/G2 population to separate individual phases\n", "seq_gmm.refine_labels_with_manual_thresholds(\n", - " feature='Int_Intg_DNA_nuc',\n", - " obs_label='cell_cycle_phase',\n", - " value_to_refine='G1/S/G2',\n", + " feature=\"Int_Intg_DNA_nuc\",\n", + " obs_label=\"cell_cycle_phase\",\n", + " value_to_refine=\"G1/S/G2\",\n", " manual_thresholds=[6, 9],\n", - " ordered_labels=['G1', 'S', 'G2'],\n", - " operation_name='separate_G1_S_G2',\n", + " ordered_labels=[\"G1\", \"S\", \"G2\"],\n", + " operation_name=\"separate_G1_S_G2\",\n", ")\n", "\n", "print(\"Second refinement complete!\")\n", - "print(f\"\\nFinal label distribution:\")\n", - "print(seq_gmm.adata.obs['cell_cycle_phase'].value_counts())" + "print(\"\\nFinal label distribution:\")\n", + "print(seq_gmm.adata.obs[\"cell_cycle_phase\"].value_counts())" ] }, { "cell_type": "markdown", - "id": "1d7005a3", + "id": "28", "metadata": {}, "source": [ "### Visualize Results" @@ -673,65 +484,39 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "135b4f3a", + "execution_count": null, + "id": "29", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "hist_kwargs = {\"bins\": 100, 'color': 'black'}\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", "# Plot the G1/S/G2 separation\n", "seq_gmm.plot_hist_distribution_with_boundaries(\n", - " operation_name='separate_G1_S_G2',\n", - " hist_kwargs=hist_kwargs,\n", - " x_axis_limits=(3, 12)\n", + " operation_name=\"separate_G1_S_G2\", hist_kwargs=hist_kwargs, x_axis_limits=(3, 12)\n", ")\n", - "plt.title('Second Refinement: G1, S, G2 (DNA Content)')\n", + "plt.title(\"Second Refinement: G1, S, G2 (DNA Content)\")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 18, - "id": "3000e542", + "execution_count": null, + "id": "30", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Strip plot\n", - "hist_kwargs = {\"bins\": 100, 'color': 'black'}\n", + "hist_kwargs = {\"bins\": 100, \"color\": \"black\"}\n", "seq_gmm.plot_strip_plot_histogram_with_decision_boundaries(\n", - " operation_name='separate_G1_S_G2',\n", - " hist_kwargs=hist_kwargs,\n", - " scatter_density=False\n", + " operation_name=\"separate_G1_S_G2\", hist_kwargs=hist_kwargs, scatter_density=False\n", ")\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 19, - "id": "cfb977ce", + "execution_count": null, + "id": "31", "metadata": {}, "outputs": [], "source": [ @@ -740,7 +525,7 @@ }, { "cell_type": "markdown", - "id": "f3f0ef7a", + "id": "32", "metadata": {}, "source": [ "---\n", @@ -750,7 +535,7 @@ }, { "cell_type": "markdown", - "id": "9e72ac6d", + "id": "33", "metadata": {}, "source": [ "### Generate Thresholding Report" @@ -758,69 +543,19 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "95acbb4c", + "execution_count": null, + "id": "34", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Thresholding Report\n", - "==================================================\n", - "\n", - "1. initial_pRB_split (Standard Thresholding)\n", - "--------------------------------------------\n", - " Feature: pRB (nuc median)\n", - " Layer: None\n", - " Obs column: cell_cycle_phase\n", - " Components: 2\n", - " Thresholds: [0.0083]\n", - " Labels: ['G0', 'G1/S/G2/M']\n", - " Cell counts: G1/S/G2/M=6101, G0=695\n", - "\n", - "2. separate_M_phase (Refinement) of cell_cycle_phase\n", - "----------------------------------------------------\n", - " Feature: pp21 (nuc median)\n", - " Layer: None\n", - " Obs column: cell_cycle_phase\n", - " Components: 2\n", - " Thresholds: [0.0067]\n", - " Labels: ['G1/S/G2', 'M']\n", - " Refined from: ['G1/S/G2/M']\n", - " Cell counts: G1/S/G2=6067, M=34\n", - "\n", - "3. separate_G1_S_G2 (Refinement) of cell_cycle_phase\n", - "----------------------------------------------------\n", - " Feature: Int_Intg_DNA_nuc\n", - " Layer: None\n", - " Obs column: cell_cycle_phase\n", - " Components: N/A (manual thresholds)\n", - " Thresholds: [6.0000, 9.0000]\n", - " Labels: ['G1', 'S', 'G2']\n", - " Refined from: ['G1/S/G2']\n", - " Cell counts: G1=3968, S=1151, G2=948\n", - "\n", - "==================================================\n", - "Total operations: 3\n", - "Operation types:\n", - " - standard: 1\n", - " - refinement: 1\n", - " - refinement_manual: 1\n" - ] - } - ], + "outputs": [], "source": [ "# Generate a text report using the method from the base class\n", - "report = seq_gmm.generate_thresholding_report(\n", - " output_format='text'\n", - ")\n", + "report = seq_gmm.generate_thresholding_report(output_format=\"text\")\n", "print(report)" ] }, { "cell_type": "markdown", - "id": "3d29eb18", + "id": "35", "metadata": {}, "source": [ "Or as a DataFrame for further analysis:" @@ -828,123 +563,19 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "45566d5d", + "execution_count": null, + "id": "36", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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OperationTypeFeatureLayerObs LabelComponentsThresholdsLabelsParentRefined FromTotal Cells
01. initial_pRB_splitstandardpRB (nuc median)Nonecell_cycle_phase20.0083G0, G1/S/G2/MNoneN/A6796
12. separate_M_phaserefinementpp21 (nuc median)Nonecell_cycle_phase20.0067G1/S/G2, Mcell_cycle_phaseG1/S/G2/M6101
23. separate_G1_S_G2refinement_manualInt_Intg_DNA_nucNonecell_cycle_phaseN/A (manual thresholds)6.0000, 9.0000G1, S, G2cell_cycle_phaseG1/S/G26067
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" - ], - "text/plain": [ - " Operation Type Feature Layer \\\n", - "0 1. initial_pRB_split standard pRB (nuc median) None \n", - "1 2. separate_M_phase refinement pp21 (nuc median) None \n", - "2 3. separate_G1_S_G2 refinement_manual Int_Intg_DNA_nuc None \n", - "\n", - " Obs Label Components Thresholds Labels \\\n", - "0 cell_cycle_phase 2 0.0083 G0, G1/S/G2/M \n", - "1 cell_cycle_phase 2 0.0067 G1/S/G2, M \n", - "2 cell_cycle_phase N/A (manual thresholds) 6.0000, 9.0000 G1, S, G2 \n", - "\n", - " Parent Refined From Total Cells \n", - "0 None N/A 6796 \n", - "1 cell_cycle_phase G1/S/G2/M 6101 \n", - "2 cell_cycle_phase G1/S/G2 6067 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Generate a DataFrame report\n", - "report_df = seq_gmm.generate_thresholding_report(\n", - " output_format='dataframe'\n", - ")\n", + "report_df = seq_gmm.generate_thresholding_report(output_format=\"dataframe\")\n", "display(report_df)" ] }, { "cell_type": "markdown", - "id": "295a119d", + "id": "37", "metadata": {}, "source": [ "### Boolean Label Combination\n", @@ -954,55 +585,43 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "7eeb71b4", + "execution_count": null, + "id": "38", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Combined label distribution:\n", - "control_and_G2M\n", - "other 6499\n", - "control_G2M 297\n", - "Name: count, dtype: int64\n" - ] - } - ], + "outputs": [], "source": [ "from cc_mapping.utils import create_boolean_label_combination\n", "\n", "# Create synthetic treatment labels for demonstration\n", "np.random.seed(42)\n", "n_cells = len(adata_final)\n", - "adata_final.obs['treatment'] = pd.Categorical(\n", - " np.random.choice(['Drug_A', 'Drug_B', 'Control'], size=n_cells)\n", + "adata_final.obs[\"treatment\"] = pd.Categorical(\n", + " np.random.choice([\"Drug_A\", \"Drug_B\", \"Control\"], size=n_cells)\n", ")\n", "\n", "# Combine two categorical labels using boolean operators\n", "# Note: Parameter names are obs_key_1, match_values_1, output_obs_key, true_label, false_label\n", "adata_final = create_boolean_label_combination(\n", " adata_final,\n", - " obs_key_1='treatment',\n", - " match_values_1=['Control'],\n", - " obs_key_2='cell_cycle_phase',\n", - " match_values_2=['G2', 'M'], # Combine G2 and M phases\n", - " operator='AND', # Find cells that are BOTH Control AND in G2 or M phase\n", - " output_obs_key='control_and_G2M',\n", - " true_label='control_G2M',\n", - " false_label='other',\n", - " overwrite=False # Default: raises error if output_obs_key already exists\n", + " obs_key_1=\"treatment\",\n", + " match_values_1=[\"Control\"],\n", + " obs_key_2=\"cell_cycle_phase\",\n", + " match_values_2=[\"G2\", \"M\"], # Combine G2 and M phases\n", + " operator=\"AND\", # Find cells that are BOTH Control AND in G2 or M phase\n", + " output_obs_key=\"control_and_G2M\",\n", + " true_label=\"control_G2M\",\n", + " false_label=\"other\",\n", + " overwrite=False, # Default: raises error if output_obs_key already exists\n", ")\n", "\n", "print(\"Combined label distribution:\")\n", - "print(adata_final.obs['control_and_G2M'].value_counts())" + "print(adata_final.obs[\"control_and_G2M\"].value_counts())" ] }, { "cell_type": "code", "execution_count": null, - "id": "9b1e47a8", + "id": "39", "metadata": {}, "outputs": [], "source": [] diff --git a/notebooks/Single_Thresholding_Workflow.ipynb b/notebooks/Single_Thresholding_Workflow.ipynb index 0cf243c..cd8083b 100644 --- a/notebooks/Single_Thresholding_Workflow.ipynb +++ b/notebooks/Single_Thresholding_Workflow.ipynb @@ -28,9 +28,9 @@ "from urllib.request import urlretrieve\n", "\n", "import anndata as ad\n", - "import pandas as pd\n", - "import numpy as np\n", "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", "from cc_mapping.thresholding import GMMThresholding\n", "from cc_mapping.utils import create_boolean_label_combination\n", diff --git a/noxfile.py b/noxfile.py index 0398e2d..3ca7803 100644 --- a/noxfile.py +++ b/noxfile.py @@ -1,6 +1,8 @@ -import nox import argparse +import nox + + @nox.session(reuse_venv=True) def docs(session: nox.Session) -> None: """ @@ -31,6 +33,7 @@ def docs(session: nox.Session) -> None: else: session.run("sphinx-build", "--keep-going", *shared_args) + @nox.session def build_api_docs(session: nox.Session) -> None: """ @@ -47,9 +50,10 @@ def build_api_docs(session: nox.Session) -> None: "--force", "src/cc_mapping", ) - + + @nox.session def pylint(session: nox.Session) -> None: session.install("-e.") session.install("pylint>=3.2") - session.run("pylint", "cc_mapping", *session.posargs) \ No newline at end of file + session.run("pylint", "cc_mapping", *session.posargs) diff --git a/src/cc_mapping/core.py b/src/cc_mapping/core.py index d794b43..9c14216 100644 --- a/src/cc_mapping/core.py +++ b/src/cc_mapping/core.py @@ -1,21 +1,19 @@ -from typing import Optional import warnings warnings.simplefilter(action="ignore", category=FutureWarning) -import numpy as np # noqa: E402 +import numpy as np np.seterr(all="ignore") -import re # noqa: E402 -import anndata as ad # noqa: E402 -from tqdm import tqdm # noqa: E402 +import re -import matplotlib.pyplot as plt # noqa: E402 - -from sklearn import metrics # noqa: E402 -from sklearn.model_selection import train_test_split # noqa: E402 -from sklearn.ensemble import RandomForestClassifier # noqa: E402 +import anndata as ad +import matplotlib.pyplot as plt +from sklearn import metrics +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split +from tqdm import tqdm def train_random_forest_model( @@ -112,8 +110,8 @@ def random_forest_feature_selection( verbose: bool = True, save_path: str = None, cutoff_method: str = "increment", - train_test_split_params: Optional[dict] = None, - rf_params: Optional[dict] = None, + train_test_split_params: dict | None = None, + rf_params: dict | None = None, ) -> ad.AnnData: """ Trains a random forest classifier on the training feature set and labels using one of two methods: @@ -316,7 +314,7 @@ def random_forest_feature_selection( label=f"Optimal Feature Set Size: {optim_feat_num}", ) plt.title( - f"Stable Counter {stable_counter} - Stable Threshold {threshold*100}% - Cutoff Method: {cutoff_method}" + f"Stable Counter {stable_counter} - Stable Threshold {threshold * 100}% - Cutoff Method: {cutoff_method}" ) plt.xticks(x_axis) diff --git a/src/cc_mapping/manifold.py b/src/cc_mapping/manifold.py index cc115f9..19a0f14 100644 --- a/src/cc_mapping/manifold.py +++ b/src/cc_mapping/manifold.py @@ -13,7 +13,6 @@ import os from dataclasses import dataclass, field -from typing import Optional import anndata as ad import matplotlib as mpl @@ -48,7 +47,7 @@ class PHATEConfig: gamma: float = 1.0 n_pca: int = 100 n_jobs: int = -1 - random_state: Optional[int] = None + random_state: int | None = None verbose: bool = False def to_dict(self) -> dict: @@ -202,7 +201,7 @@ def run_phate( adata: ad.AnnData, feature_set: str, layer: str, - phate_config: Optional[PHATEConfig] = None, + phate_config: PHATEConfig | None = None, obsm_save_key: str = "X_phate", ) -> ad.AnnData: """ @@ -244,7 +243,7 @@ def plot_embedding( ax: plt.Axes, phate_coords: np.ndarray, colors: np.ndarray | pd.Series, - kwargs: Optional[dict] = None, + kwargs: dict | None = None, ) -> plt.Axes: """ Plot PHATE embedding on given axes. @@ -290,9 +289,9 @@ def plot_from_adata( adata: ad.AnnData, color_name: str, obsm_embedding: str = "X_phate", - ax: Optional[plt.Axes] = None, + ax: plt.Axes | None = None, unit_size: int = 5, - kwargs: Optional[dict] = None, + kwargs: dict | None = None, return_fig: bool = False, ) -> plt.Axes | tuple[plt.Figure, plt.Axes]: """ @@ -350,8 +349,8 @@ def perform_phate_hyperparameter_search( color_name: str, final_grid_dims: tuple[int, int], unit_size: int = 10, - plot_kwargs: Optional[dict] = None, - save_path: Optional[str] = None, + plot_kwargs: dict | None = None, + save_path: str | None = None, show_legend: bool = False, ) -> list[plt.Figure]: """ @@ -470,8 +469,8 @@ def quick_phate_plot( feature_set: str, layer: str, color_name: str, - phate_config: Optional[PHATEConfig] = None, - save_path: Optional[str] = None, + phate_config: PHATEConfig | None = None, + save_path: str | None = None, ) -> tuple[ad.AnnData, plt.Figure]: """ Quick PHATE computation and visualization. diff --git a/src/cc_mapping/plot.py b/src/cc_mapping/plot.py index ea07425..653cf6b 100644 --- a/src/cc_mapping/plot.py +++ b/src/cc_mapping/plot.py @@ -11,8 +11,8 @@ from __future__ import annotations import os +from collections.abc import Callable from dataclasses import dataclass, field -from typing import Callable, Optional import anndata as ad import matplotlib.patches as mpatches @@ -21,7 +21,6 @@ import pandas as pd from matplotlib.gridspec import GridSpec - # ============================================================================ # Configuration Dataclasses # ============================================================================ @@ -42,7 +41,7 @@ class RowPartitionConfig(PlotConfig): adata: ad.AnnData obs_search_term: str colors: list | np.ndarray - column_labels: Optional[list | np.ndarray] = None + column_labels: list | np.ndarray | None = None obs_embedding_key: str = "X_phate" plot_all: bool = True plot_background: bool = True @@ -77,7 +76,7 @@ def add_column_labels( self, ax: plt.Axes, labels: list, - param_name: Optional[str] = None, + param_name: str | None = None, ) -> plt.Axes: """Add colored column labels to the top of the grid.""" num_cols = len(labels) @@ -110,7 +109,7 @@ def add_row_labels( self, ax: plt.Axes, labels: list, - param_name: Optional[str] = None, + param_name: str | None = None, ) -> plt.Axes: """Add colored row labels to the left of the grid.""" num_rows = len(labels) @@ -384,13 +383,13 @@ def plot_row_partitions( adata: ad.AnnData, obs_search_term: str, colors: list | np.ndarray, - column_labels: Optional[list | np.ndarray] = None, + column_labels: list | np.ndarray | None = None, obs_embedding_key: str = "X_phate", - kwargs: Optional[dict] = None, + kwargs: dict | None = None, plot_all: bool = True, plot_background: bool = True, unit_size: int = 20, - save_path: Optional[str] = None, + save_path: str | None = None, ) -> plt.Figure: """ Plot row partitions of the given AnnData object. @@ -452,7 +451,7 @@ def plot_row_partitions( def get_legend( adata: ad.AnnData, color_name: str, - label_name: Optional[str] = None, + label_name: str | None = None, ) -> tuple[list[mpatches.Patch], np.ndarray]: """ Get patches from adata.obs[color_name] for creating a legend. @@ -488,9 +487,9 @@ def combine_figures_with_gridspec( grid_rows: int, grid_cols: int, unit_size: int = 5, - title: Optional[str] = None, - title_kwargs: Optional[dict] = None, - save_path: Optional[str] = None, + title: str | None = None, + title_kwargs: dict | None = None, + save_path: str | None = None, ) -> plt.Figure: """ Combine multiple figures into a single figure using GridSpec. diff --git a/src/cc_mapping/preprocess.py b/src/cc_mapping/preprocess.py index 14a7119..437add0 100644 --- a/src/cc_mapping/preprocess.py +++ b/src/cc_mapping/preprocess.py @@ -1,7 +1,7 @@ import re -import numpy as np import anndata as ad +import numpy as np def row_data_partitioning( diff --git a/src/cc_mapping/pseudotime.py b/src/cc_mapping/pseudotime.py index bf77ff2..734fe35 100644 --- a/src/cc_mapping/pseudotime.py +++ b/src/cc_mapping/pseudotime.py @@ -48,7 +48,6 @@ def run_palantir_pseudotime( try: with open(os.devnull, "w") as devnull: with contextlib.redirect_stdout(devnull): - palantir.utils.run_diffusion_maps( adata, n_components=n_components, pca_key=data_key, seed=seed ) @@ -234,7 +233,6 @@ def perform_palantir_hyperparameter_search( total=number_param_plots, desc="Generating hyperparameter search plots", ): - plotting_dict = { "adata": adata.copy(), "data_key": data_key, diff --git a/src/cc_mapping/thresholding/__init__.py b/src/cc_mapping/thresholding/__init__.py index 06a39a8..c18179a 100644 --- a/src/cc_mapping/thresholding/__init__.py +++ b/src/cc_mapping/thresholding/__init__.py @@ -20,7 +20,7 @@ Usage:: from cc_mapping.thresholding import GMMThresholding - + gmm = GMMThresholding( adata=adata, feature='gene1', @@ -32,29 +32,24 @@ """ from .base import ( - _GaussianMixtureModelInfo, + GaussianMixtureModelBase, _DecisionBoundariesModel, + _GaussianMixtureModelInfo, _SingleThresholdingEventModel, - GaussianMixtureModelBase, ) - -from .single import GMMThresholding - from .sequential import SequentialGMM - +from .single import GMMThresholding __all__ = [ # Main classes - 'GMMThresholding', - 'SequentialGMM', - + "GMMThresholding", + "SequentialGMM", # Base class - 'GaussianMixtureModelBase', - + "GaussianMixtureModelBase", # Pydantic models (private but exposed for advanced usage) - '_GaussianMixtureModelInfo', - '_DecisionBoundariesModel', - '_SingleThresholdingEventModel', + "_GaussianMixtureModelInfo", + "_DecisionBoundariesModel", + "_SingleThresholdingEventModel", ] -__version__ = '0.1.1' +__version__ = "0.1.1" diff --git a/src/cc_mapping/thresholding/base.py b/src/cc_mapping/thresholding/base.py index 7080880..2fa20f6 100644 --- a/src/cc_mapping/thresholding/base.py +++ b/src/cc_mapping/thresholding/base.py @@ -12,26 +12,24 @@ GaussianMixtureModelBase: Base class providing shared utilities and plotting methods """ -from typing import Dict, List, Optional, Tuple, Union import warnings from pathlib import Path import anndata as ad -from kneed import KneeLocator -from matplotlib import axes -from matplotlib.figure import Figure import matplotlib as mpl -import matplotlib.pyplot as plt -import matplotlib.gridspec as gridspec import matplotlib.patches as mpatches +import matplotlib.pyplot as plt import numpy as np import pandas as pd import scipy.stats as st +from kneed import KneeLocator +from matplotlib import axes, gridspec +from matplotlib.figure import Figure from pydantic import BaseModel, Field, field_validator from sklearn.mixture import GaussianMixture -def _validate_save_path(save_path: Optional[Union[str, Path]]) -> Optional[Path]: +def _validate_save_path(save_path: str | Path | None) -> Path | None: """ Validate that the directory of the save_path exists. @@ -92,27 +90,27 @@ class _GaussianMixtureModelInfo(BaseModel): Condensed data probabilities after handling duplicates (optional). """ - gmm_kwargs: Optional[Dict] = Field( + gmm_kwargs: dict | None = Field( default=None, description="Keyword arguments used for the Gaussian Mixture Model.", ) - means: Optional[List[float]] = Field( + means: list[float] | None = Field( default=None, description="Means of the GMM components." ) - covs: Optional[List[float]] = Field( + covs: list[float] | None = Field( default=None, description="Covariances of the GMM components." ) - weights: Optional[List[float]] = Field( + weights: list[float] | None = Field( default=None, description="Weights of the GMM components." ) - n_components: Optional[int] = Field( + n_components: int | None = Field( default=None, description="Number of GMM components." ) - data_probs: Optional[List[List[float]]] = Field( + data_probs: list[list[float]] | None = Field( default=None, description="Probability of each data point belonging to each gmm component.", ) - condensed_data_probs: Optional[List[List[float]]] = Field( + condensed_data_probs: list[list[float]] | None = Field( default=None, description="Condensed data probabilities after handling duplicates (optional).", ) @@ -148,7 +146,7 @@ class _DecisionBoundariesModel(BaseModel): List of decision boundary thresholds. """ - thresholds: List[float] = Field(description="List of decision boundary thresholds.") + thresholds: list[float] = Field(description="List of decision boundary thresholds.") class _SingleThresholdingEventModel(BaseModel): @@ -171,17 +169,17 @@ class _SingleThresholdingEventModel(BaseModel): Observation label in anndata object to store gmm phase labels. """ - gmm_info: Optional[_GaussianMixtureModelInfo] = Field( + gmm_info: _GaussianMixtureModelInfo | None = Field( description="Gaussian Mixture Model information.", default=None, ) - ordered_gmm_labels: Optional[List[str]] = Field( + ordered_gmm_labels: list[str] | None = Field( description="Ordered labels corresponding to the gmm components.", default=None ) - decision_boundaries: Optional[_DecisionBoundariesModel] = Field( + decision_boundaries: _DecisionBoundariesModel | None = Field( default=None, description="Decision boundary information (optional)." ) - condensed_labels: Optional[List[str]] = Field( + condensed_labels: list[str] | None = Field( default=None, description="Condensed labels after handling duplicates (optional).", ) @@ -360,9 +358,9 @@ def _calculate_bic_for_component_range( adata: ad.AnnData, feature: str, component_range: int, - layer: Optional[str], + layer: str | None, gmm_kwargs: dict, - ) -> List[Union[int, float]]: + ) -> list[int | float]: """ Run Bayesian Information Criterion (BIC) on the gene expression data. @@ -407,13 +405,13 @@ def determine_optimal_number_components( adata: ad.AnnData, feature: str, component_range: int, - layer: Optional[str] = None, - gmm_kwargs: Optional[dict] = None, + layer: str | None = None, + gmm_kwargs: dict | None = None, metric: str = "bic", curve: str = "convex", direction: str = "decreasing", return_bic_list: bool = False, - ) -> Union[int, Tuple[int, List[Union[int, float]]]]: + ) -> int | tuple[int, list[int | float]]: """ Determine the optimal number of components for the GMM. @@ -481,12 +479,12 @@ def plot_bayesian_information_criterion_curve( adata: ad.AnnData, feature: str, component_range: int, - layer: Optional[str] = None, - gmm_kwargs: Optional[dict] = None, + layer: str | None = None, + gmm_kwargs: dict | None = None, curve: str = "convex", direction: str = "decreasing", ax: plt.Axes = None, - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, ) -> None: """ Plot the BIC curve. @@ -657,7 +655,7 @@ def _plot_sample_catergory_legend( ax: axes.Axes, internal_data: _SingleThresholdingEventModel, cmap: plt.cm.ScalarMappable, - legend_kwargs: Optional[dict] = None, + legend_kwargs: dict | None = None, ) -> axes.Axes: """ Plot legend showing category labels and colors. @@ -702,10 +700,10 @@ def _plot_hist_base( self, adata: ad.AnnData, feature: str, - layer: Optional[str], - hist_kwargs: Optional[Dict] = None, + layer: str | None, + hist_kwargs: dict | None = None, ax: plt.Axes = None, - x_axis_limits: Optional[tuple] = None, + x_axis_limits: tuple | None = None, ) -> axes.Axes: """ Base function for plotting GMM distributions. @@ -933,16 +931,16 @@ def _plot_strip_plot_base( self, adata: ad.AnnData, feature: str, - layer: Optional[str], - obs_label: Optional[str] = None, - ordered_labels: Optional[List[str]] = None, + layer: str | None, + obs_label: str | None = None, + ordered_labels: list[str] | None = None, scatter_density: bool = True, - y_axis_limits: Optional[Tuple[float, float]] = None, - hist_kwargs: Optional[Dict] = None, - strip_plot_kwargs: Optional[Dict] = None, + y_axis_limits: tuple[float, float] | None = None, + hist_kwargs: dict | None = None, + strip_plot_kwargs: dict | None = None, cmap: mpl.cm.ScalarMappable = mpl.colormaps["plasma"], - vmax: Optional[Union[int, float]] = None, - ) -> Tuple[Figure, axes.Axes, axes.Axes]: + vmax: float | None = None, + ) -> tuple[Figure, axes.Axes, axes.Axes]: """ Base method for creating strip plot + histogram WITHOUT decision boundaries. @@ -1161,18 +1159,18 @@ def _plot_strip_plot_histogram_with_decision_boundaries( self, adata: ad.AnnData, feature: str, - layer: Optional[str], + layer: str | None, obs_label: str, - ordered_labels: List[str], + ordered_labels: list[str], internal_data: _SingleThresholdingEventModel, cmap: mpl.cm.ScalarMappable = mpl.colormaps["plasma"], - y_axis_limits: Optional[Tuple[float, float]] = None, + y_axis_limits: tuple[float, float] | None = None, resolution: int = 1000, scatter_density: bool = True, - vmax: Optional[Union[int, float]] = None, - hist_kwargs: Optional[Dict] = None, - strip_plot_kwargs: Optional[Dict] = None, - title: Optional[str] = None, + vmax: float | None = None, + hist_kwargs: dict | None = None, + strip_plot_kwargs: dict | None = None, + title: str | None = None, ) -> Figure: """ Generate a 1D strip plot with histogram and decision boundaries. @@ -1261,7 +1259,7 @@ def _plot_strip_plot_histogram_with_decision_boundaries( def generate_thresholding_report( self, output_format: str = "text", - ) -> Union[str, pd.DataFrame]: + ) -> str | pd.DataFrame: """ Generate a human-readable report of all thresholding operations. diff --git a/src/cc_mapping/thresholding/sequential.py b/src/cc_mapping/thresholding/sequential.py index 101dbea..e0b31b5 100644 --- a/src/cc_mapping/thresholding/sequential.py +++ b/src/cc_mapping/thresholding/sequential.py @@ -9,26 +9,25 @@ SequentialGMM: Sequential refinement class """ +import warnings from collections import OrderedDict -from typing import Dict, List, Optional, Tuple, Union from pathlib import Path -import warnings import anndata as ad import matplotlib as mpl -from matplotlib import colors as mpl_colors import matplotlib.pyplot as plt -from matplotlib.axes import Axes -from matplotlib.figure import Figure import numpy as np import pandas as pd +from matplotlib import colors as mpl_colors +from matplotlib.axes import Axes +from matplotlib.figure import Figure from sklearn.mixture import GaussianMixture from .base import ( - _GaussianMixtureModelInfo, + GaussianMixtureModelBase, _DecisionBoundariesModel, + _GaussianMixtureModelInfo, _SingleThresholdingEventModel, - GaussianMixtureModelBase, _validate_save_path, ) from .single import GMMThresholding @@ -37,20 +36,20 @@ class SequentialGMM(GaussianMixtureModelBase): """ Sequential GMM thresholding for iterative population refinement. - + This class enables performing multiple sequential GMM thresholding operations on subsets of cells, where each operation refines a specific categorical label from a previous thresholding event. This is useful for hierarchical cell type classification or iterative gating strategies. - + Unlike GMMThresholding which thresholds a single feature once, this class allows: - + - Initial thresholding on entire dataset - Refinement of specific label values through additional thresholding - Tracking operation provenance (parent-child relationships) - Multiple operations stored in a single .uns key - + Attributes ---------- adata : ad.AnnData @@ -61,17 +60,17 @@ class SequentialGMM(GaussianMixtureModelBase): Default GMM kwargs (can be overridden per operation). random_state : int Random state for reproducibility. - + Examples -------- Example workflow:: - + # Initialize seq_gmm = SequentialGMM( adata=adata, thresholding_events_key='sequential_thresholding' ) - + # Create initial labels on entire dataset seq_gmm.threshold_entire_dataset( feature='DNA_content', @@ -80,7 +79,7 @@ class SequentialGMM(GaussianMixtureModelBase): ordered_labels=['Low', 'High'], operation_name='DNA_threshold' ) - + # Refine 'Low' cells only seq_gmm.refine_labels_with_gmm( feature='Plk1', @@ -90,34 +89,34 @@ class SequentialGMM(GaussianMixtureModelBase): ordered_labels=['Low_neg', 'Low_pos'], operation_name='Plk1_refinement' ) - + # Get modified adata adata = seq_gmm.return_adata() """ - + def __init__( self, adata: ad.AnnData, - thresholding_events_key: str = 'sequential_gmm_thresholding_events', - gmm_kwargs: Optional[dict] = None, + thresholding_events_key: str = "sequential_gmm_thresholding_events", + gmm_kwargs: dict | None = None, random_state: int = 42, ): """ Initialize sequential thresholding object. - + Parameters ---------- adata : ad.AnnData Annotated data matrix (observations x features). thresholding_events_key : str, optional - The key in `adata.uns` where all thresholding event information will be stored. + The key in `adata.uns` where all thresholding event information will be stored. Defaults to 'sequential_gmm_thresholding_events'. gmm_kwargs : Optional[Dict], optional - Default keyword arguments to pass to `sklearn.mixture.GaussianMixture`. + Default keyword arguments to pass to `sklearn.mixture.GaussianMixture`. Can be overridden per operation. Defaults to None (becomes {}). random_state : int, optional Random state for reproducibility. Defaults to 42. - + Raises ------ TypeError @@ -172,22 +171,22 @@ def threshold_entire_dataset( feature: str, label_obs_save_str: str, n_components: int, - ordered_labels: List[str], - manual_thresholds: Optional[List[Union[float, int]]] = None, + ordered_labels: list[str], + manual_thresholds: list[float | int] | None = None, duplicate_labels: bool = False, - operation_name: Optional[str] = None, - layer: Optional[str] = None, - gmm_kwargs: Optional[dict] = None, + operation_name: str | None = None, + layer: str | None = None, + gmm_kwargs: dict | None = None, overwrite: bool = False, ) -> None: """ Threshold entire dataset to create initial categorical labels. - + This method creates a new obs column with categorical labels based on GMM thresholding of a single feature across all cells. It's a wrapper around GMMThresholding that stores results in the sequential thresholding framework. - + Parameters ---------- feature : str @@ -210,24 +209,24 @@ def threshold_entire_dataset( gmm_kwargs : Optional[dict], optional GMM kwargs for this operation. Overrides default if provided. Defaults to None. overwrite : bool, optional - If True, allows overwriting an existing operation with the same name. + If True, allows overwriting an existing operation with the same name. Useful for updating n_components or thresholds. Defaults to False. - + Raises ------ ValueError If operation_name is None or empty. KeyError If operation_name already exists in .uns and overwrite=False. - + Notes ----- Other exceptions raised by GMMThresholding. - + Examples -------- :: - + seq_gmm.threshold_entire_dataset( feature='DNA_content', label_obs_save_str='cell_cycle', @@ -239,7 +238,7 @@ def threshold_entire_dataset( # Validate operation_name if operation_name is None or not operation_name: raise ValueError("operation_name is required and cannot be empty.") - + if operation_name in self.adata.uns[self.thresholding_events_key]: if not overwrite: raise KeyError( @@ -250,16 +249,16 @@ def threshold_entire_dataset( warnings.warn( f"Overwriting existing operation '{operation_name}'.", UserWarning, - stacklevel=2 + stacklevel=2, ) - + # Use default gmm_kwargs if not provided if gmm_kwargs is None: gmm_kwargs = self.gmm_kwargs - + # Create temporary single thresholding instance # Use a temporary .uns key to avoid conflicts - temp_key = f'_temp_{operation_name}' + temp_key = f"_temp_{operation_name}" gmm_single = GMMThresholding( adata=self.adata, feature=feature, @@ -269,7 +268,7 @@ def threshold_entire_dataset( gmm_kwargs=gmm_kwargs, random_state=self.random_state, ) - + # Fit and categorize gmm_single.fit(n_components=n_components) gmm_single.categorize_samples( @@ -277,27 +276,30 @@ def threshold_entire_dataset( manual_thresholds=manual_thresholds, duplicate_labels=duplicate_labels, ) - + # Extract results from temporary instance self.adata = gmm_single.return_adata() - + # Move operation data from temp key to our key with metadata # Single class stores with feature name as key operation_data = self.adata.uns[temp_key][feature] - + # Add sequential-specific metadata - operation_data['operation_type'] = 'standard' - operation_data['parent_operation'] = None - operation_data['refined_from_labels'] = None - operation_data['layer'] = layer - + operation_data["operation_type"] = "standard" + operation_data["parent_operation"] = None + operation_data["refined_from_labels"] = None + operation_data["layer"] = layer + # Capture cell counts immediately after this operation - cell_counts_after_operation = {str(k): int(v) for k, v in self.adata.obs[label_obs_save_str].value_counts().items()} - operation_data['cell_counts_after_operation'] = cell_counts_after_operation - + cell_counts_after_operation = { + str(k): int(v) + for k, v in self.adata.obs[label_obs_save_str].value_counts().items() + } + operation_data["cell_counts_after_operation"] = cell_counts_after_operation + # Store in our key with the operation_name self.adata.uns[self.thresholding_events_key][operation_name] = operation_data - + # Clean up temp key del self.adata.uns[temp_key] @@ -307,19 +309,19 @@ def refine_labels_with_gmm( obs_label: str, value_to_refine: str, n_components: int, - ordered_labels: List[str], + ordered_labels: list[str], duplicate_labels: bool = False, - operation_name: Optional[str] = None, - layer: Optional[str] = None, - gmm_kwargs: Optional[dict] = None, + operation_name: str | None = None, + layer: str | None = None, + gmm_kwargs: dict | None = None, overwrite: bool = False, ) -> None: """ Refine existing categorical labels by thresholding a subset with GMM. - + Modifies adata.obs[obs_label] in-place (within the copy), replacing cells with value_to_refine with new labels based on GMM thresholding. - + Parameters ---------- feature : str @@ -341,9 +343,9 @@ def refine_labels_with_gmm( gmm_kwargs : Optional[dict], optional GMM kwargs for this operation. Overrides default if provided. Defaults to None. overwrite : bool, optional - If True, allows overwriting an existing operation with the same name. + If True, allows overwriting an existing operation with the same name. Useful for updating n_components or thresholds. Defaults to False. - + Raises ------ ValueError @@ -356,11 +358,11 @@ def refine_labels_with_gmm( If value_to_refine is not present in adata.obs[obs_label]. ValueError If no cells have the value_to_refine. - + Examples -------- :: - + # Before: adata.obs['cell_cycle'] = ['G0', 'G0', 'G1', 'S', 'G0'] seq_gmm.refine_labels_with_gmm( feature='Plk1', @@ -375,7 +377,7 @@ def refine_labels_with_gmm( # Validate operation_name if operation_name is None or not operation_name: raise ValueError("operation_name is required and cannot be empty.") - + if operation_name in self.adata.uns[self.thresholding_events_key]: if not overwrite: raise KeyError( @@ -386,16 +388,16 @@ def refine_labels_with_gmm( warnings.warn( f"Overwriting existing operation '{operation_name}'.", UserWarning, - stacklevel=2 + stacklevel=2, ) - + # Validate obs_label exists if obs_label not in self.adata.obs.columns: raise KeyError( f"obs_label '{obs_label}' not found in adata.obs. " f"Available columns: {list(self.adata.obs.columns)}" ) - + # Partition data by label mask, subset_data = self._partition_data_by_label( obs_label=obs_label, @@ -403,22 +405,19 @@ def refine_labels_with_gmm( feature=feature, layer=layer, ) - + # Use default gmm_kwargs if not provided if gmm_kwargs is None: gmm_kwargs = self.gmm_kwargs.copy() - + # Ensure random_state is in gmm_kwargs (but don't override if explicitly provided) - if 'random_state' not in gmm_kwargs: - gmm_kwargs['random_state'] = self.random_state - + if "random_state" not in gmm_kwargs: + gmm_kwargs["random_state"] = self.random_state + # Fit GMM on subset only - gmm = GaussianMixture( - n_components=n_components, - **gmm_kwargs - ) + gmm = GaussianMixture(n_components=n_components, **gmm_kwargs) gmm.fit(subset_data.reshape(-1, 1)) - + # Extract GMM info gmm_info = _GaussianMixtureModelInfo( gmm_kwargs=gmm_kwargs, @@ -428,42 +427,46 @@ def refine_labels_with_gmm( n_components=n_components, data_probs=gmm.predict_proba(subset_data.reshape(-1, 1)), ) - + # Handle duplicate labels if needed if duplicate_labels: - ordered_labels_processed, condensed_data_probs = self._handle_duplicate_labels( - ordered_labels, gmm_info.data_probs + ordered_labels_processed, condensed_data_probs = ( + self._handle_duplicate_labels(ordered_labels, gmm_info.data_probs) ) gmm_info.condensed_data_probs = condensed_data_probs else: ordered_labels_processed = ordered_labels - + # Calculate decision boundaries from probabilities # Convert data_probs back to numpy array (Pydantic stores as list) - probs_array = np.array(gmm_info.condensed_data_probs if duplicate_labels else gmm_info.data_probs) + probs_array = np.array( + gmm_info.condensed_data_probs if duplicate_labels else gmm_info.data_probs + ) decision_boundaries = self._calculate_decision_boundaries_from_probs( feature_values=subset_data, data_probs=probs_array, ordered_labels=ordered_labels_processed, ) - + # Assign new labels to subset cells new_labels = self._assign_labels_from_thresholds( data=subset_data, thresholds=decision_boundaries.thresholds, ordered_labels=ordered_labels_processed, ) - + # Update labels in-place self._update_labels_in_place( obs_label=obs_label, mask=mask, new_labels=new_labels, ) - + # Capture cell counts immediately after this operation - cell_counts_after_operation = {str(k): int(v) for k, v in pd.Series(new_labels).value_counts().items()} - + cell_counts_after_operation = { + str(k): int(v) for k, v in pd.Series(new_labels).value_counts().items() + } + # Store operation metadata internal_data = _SingleThresholdingEventModel( gmm_info=gmm_info, @@ -473,13 +476,13 @@ def refine_labels_with_gmm( feature_name=feature, gmm_obs_label=obs_label, ) - + self._store_refinement_operation( operation_name=operation_name, internal_data=internal_data, obs_label=obs_label, value_to_refine=value_to_refine, - operation_type='refinement', + operation_type="refinement", layer=layer, cell_counts_after_operation=cell_counts_after_operation, ) @@ -489,18 +492,18 @@ def refine_labels_with_manual_thresholds( feature: str, obs_label: str, value_to_refine: str, - manual_thresholds: List[Union[float, int]], - ordered_labels: List[str], - operation_name: Optional[str] = None, - layer: Optional[str] = None, + manual_thresholds: list[float | int], + ordered_labels: list[str], + operation_name: str | None = None, + layer: str | None = None, overwrite: bool = False, ) -> None: """ Refine existing categorical labels using manual thresholds. - + Similar to refine_labels_with_gmm() but uses explicit threshold values instead of fitting a GMM. - + Parameters ---------- feature : str @@ -518,9 +521,9 @@ def refine_labels_with_manual_thresholds( layer : Optional[str], optional Layer to use for data access. If None, uses adata.X. Defaults to None. overwrite : bool, optional - If True, allows overwriting an existing operation with the same name. + If True, allows overwriting an existing operation with the same name. Useful for updating thresholds. Defaults to False. - + Raises ------ ValueError @@ -535,11 +538,11 @@ def refine_labels_with_manual_thresholds( If no cells have the value_to_refine. ValueError If len(manual_thresholds) != len(ordered_labels) - 1. - + Examples -------- :: - + seq_gmm.refine_labels_with_manual_thresholds( feature='Plk1', obs_label='cell_cycle', @@ -552,7 +555,7 @@ def refine_labels_with_manual_thresholds( # Validate operation_name if operation_name is None or not operation_name: raise ValueError("operation_name is required and cannot be empty.") - + if operation_name in self.adata.uns[self.thresholding_events_key]: if not overwrite: raise KeyError( @@ -563,23 +566,23 @@ def refine_labels_with_manual_thresholds( warnings.warn( f"Overwriting existing operation '{operation_name}'.", UserWarning, - stacklevel=2 + stacklevel=2, ) - + # Validate obs_label exists if obs_label not in self.adata.obs.columns: raise KeyError( f"obs_label '{obs_label}' not found in adata.obs. " f"Available columns: {list(self.adata.obs.columns)}" ) - + # Validate manual_thresholds length if len(manual_thresholds) != len(ordered_labels) - 1: raise ValueError( f"Number of thresholds ({len(manual_thresholds)}) must be " f"len(ordered_labels) - 1 = {len(ordered_labels) - 1}" ) - + # Partition data by label mask, subset_data = self._partition_data_by_label( obs_label=obs_label, @@ -587,27 +590,29 @@ def refine_labels_with_manual_thresholds( feature=feature, layer=layer, ) - + # Assign new labels based on manual thresholds new_labels = self._assign_labels_from_thresholds( data=subset_data, thresholds=manual_thresholds, ordered_labels=ordered_labels, ) - + # Update labels in-place self._update_labels_in_place( obs_label=obs_label, mask=mask, new_labels=new_labels, ) - + # Capture cell counts immediately after this operation - cell_counts_after_operation = {str(k): int(v) for k, v in pd.Series(new_labels).value_counts().items()} - + cell_counts_after_operation = { + str(k): int(v) for k, v in pd.Series(new_labels).value_counts().items() + } + # Store operation metadata (no GMM info for manual thresholds) decision_boundaries = _DecisionBoundariesModel(thresholds=manual_thresholds) - + internal_data = _SingleThresholdingEventModel( gmm_info=None, # No GMM for manual thresholds ordered_gmm_labels=ordered_labels, @@ -616,27 +621,23 @@ def refine_labels_with_manual_thresholds( feature_name=feature, gmm_obs_label=obs_label, ) - + self._store_refinement_operation( operation_name=operation_name, internal_data=internal_data, obs_label=obs_label, value_to_refine=value_to_refine, - operation_type='refinement_manual', + operation_type="refinement_manual", layer=layer, cell_counts_after_operation=cell_counts_after_operation, ) def _partition_data_by_label( - self, - obs_label: str, - value_to_refine: str, - feature: str, - layer: Optional[str] - ) -> Tuple[np.ndarray, np.ndarray]: + self, obs_label: str, value_to_refine: str, feature: str, layer: str | None + ) -> tuple[np.ndarray, np.ndarray]: """ Extract subset of data for cells with specific label value. - + Parameters ---------- obs_label : str @@ -647,14 +648,14 @@ def _partition_data_by_label( Feature name to extract data for. layer : Optional[str] Layer to use (None = .X). - + Returns ------- mask : np.ndarray Boolean array indicating which cells to refine. feature_data : np.ndarray Feature values for those cells (1D array). - + Raises ------ ValueError @@ -670,15 +671,20 @@ def _partition_data_by_label( f"value_to_refine '{value_to_refine}' not found in adata.obs['{obs_label}'].\n" f"Available values: {list(unique_values)}" ) - + # Check if operation with this value_to_refine exists in history - if hasattr(self.adata, 'uns') and self.thresholding_events_key in self.adata.uns: + if ( + hasattr(self.adata, "uns") + and self.thresholding_events_key in self.adata.uns + ): metadata = self.adata.uns[self.thresholding_events_key] for op_name, op_data in metadata.items(): - if op_data.get('label_to_refine') == value_to_refine: - refined_labels = op_data.get('ordered_gmm_labels', []) + if op_data.get("label_to_refine") == value_to_refine: + refined_labels = op_data.get("ordered_gmm_labels", []) # Check if those refined labels exist now - existing_refined = [lbl for lbl in refined_labels if lbl in unique_values] + existing_refined = [ + lbl for lbl in refined_labels if lbl in unique_values + ] if existing_refined: error_msg += ( f"\n\nNote: Operation '{op_name}' previously refined '{value_to_refine}' " @@ -691,40 +697,40 @@ def _partition_data_by_label( f" seq_gmm.plot_feature_strip_plot_exploratory(...)" ) break - + raise ValueError(error_msg) - + # Create mask for cells with value_to_refine mask = self.adata.obs[obs_label] == value_to_refine - + # Check if any cells match if not mask.any(): raise ValueError( f"No cells found with value '{value_to_refine}' in adata.obs['{obs_label}']" ) - + # Extract feature data for subset if layer is None: feature_data = self.adata[mask, feature].X else: feature_data = self.adata[mask, feature].layers[layer] - + # Ensure 1D array - if hasattr(feature_data, 'toarray'): + if hasattr(feature_data, "toarray"): feature_data = feature_data.toarray() feature_data = np.asarray(feature_data).flatten() - + return mask, feature_data def _assign_labels_from_thresholds( self, data: np.ndarray, - thresholds: List[Union[float, int]], - ordered_labels: List[str], + thresholds: list[float | int], + ordered_labels: list[str], ) -> np.ndarray: """ Assign categorical labels based on threshold values. - + Parameters ---------- data : np.ndarray @@ -733,12 +739,12 @@ def _assign_labels_from_thresholds( List of threshold values (sorted low to high). ordered_labels : List[str] Labels corresponding to threshold bins. - + Returns ------- np.ndarray Array of labels (same length as data). - + Raises ------ ValueError @@ -751,25 +757,22 @@ def _assign_labels_from_thresholds( f"Number of labels ({len(ordered_labels)}) must equal number of thresholds + 1 ({expected_labels}). " f"Thresholds: {thresholds}, Labels: {ordered_labels}" ) - + # Initialize with first label labels = np.full(len(data), ordered_labels[0], dtype=object) - + # Assign labels based on thresholds for i, threshold in enumerate(thresholds): labels[data > threshold] = ordered_labels[i + 1] - + return labels def _update_labels_in_place( - self, - obs_label: str, - mask: np.ndarray, - new_labels: np.ndarray + self, obs_label: str, mask: np.ndarray, new_labels: np.ndarray ) -> None: """ Update obs column in-place for masked cells. - + Parameters ---------- obs_label : str @@ -781,8 +784,8 @@ def _update_labels_in_place( """ # Convert to categorical if not already if not isinstance(self.adata.obs[obs_label].dtype, pd.CategoricalDtype): - self.adata.obs[obs_label] = self.adata.obs[obs_label].astype('category') - + self.adata.obs[obs_label] = self.adata.obs[obs_label].astype("category") + # Add new categories if they don't exist existing_categories = self.adata.obs[obs_label].cat.categories new_categories = set(new_labels) - set(existing_categories) @@ -790,7 +793,7 @@ def _update_labels_in_place( self.adata.obs[obs_label] = self.adata.obs[obs_label].cat.add_categories( list(new_categories) ) - + # Update values for masked cells self.adata.obs.loc[mask, obs_label] = new_labels @@ -801,12 +804,12 @@ def _store_refinement_operation( obs_label: str, value_to_refine: str, operation_type: str, - layer: Optional[str], - cell_counts_after_operation: Dict[str, int], + layer: str | None, + cell_counts_after_operation: dict[str, int], ) -> None: """ Store operation metadata in .uns. - + Parameters ---------- operation_name : str @@ -826,30 +829,30 @@ def _store_refinement_operation( """ # Convert to dict for storage operation_dict = internal_data.model_dump() - + # Add sequential-specific metadata - operation_dict['operation_type'] = operation_type - operation_dict['parent_operation'] = obs_label - operation_dict['refined_from_labels'] = [value_to_refine] - operation_dict['layer'] = layer - operation_dict['cell_counts_after_operation'] = cell_counts_after_operation - + operation_dict["operation_type"] = operation_type + operation_dict["parent_operation"] = obs_label + operation_dict["refined_from_labels"] = [value_to_refine] + operation_dict["layer"] = layer + operation_dict["cell_counts_after_operation"] = cell_counts_after_operation + # Store in .uns self.adata.uns[self.thresholding_events_key][operation_name] = operation_dict def return_adata(self) -> ad.AnnData: """ Return the modified AnnData object. - + Returns ------- ad.AnnData Modified AnnData object with all operations applied. - + Examples -------- :: - + seq_gmm = SequentialGMM(adata) seq_gmm.threshold_entire_dataset(...) seq_gmm.refine_labels_with_gmm(...) @@ -858,93 +861,93 @@ def return_adata(self) -> ad.AnnData: return self.adata def _get_descendant_labels( - self, - operation_name: str, - original_labels: List[str] - ) -> List[str]: + self, operation_name: str, original_labels: list[str] + ) -> list[str]: """ Get all labels that descended from an operation's labels. - + This handles cases where subsequent operations refined labels from this operation. For example, if 'separate_M_phase' created ['M', 'G1/S/G2'], and a later operation refined 'G1/S/G2' into ['G1', 'S', 'G2'], this returns ['M', 'G1', 'S', 'G2']. - + Parameters ---------- operation_name : str Name of the operation. original_labels : List[str] The labels created by this operation. - + Returns ------- List[str] List of all current labels that descended from original_labels. """ all_labels = set(original_labels) - + # Check all subsequent operations to see if they refined any of our labels metadata = self.adata.uns[self.thresholding_events_key] for op_name, op_data in metadata.items(): # Skip the current operation and operations before it if op_name == operation_name: continue - + # Check if this operation refined one of our labels - if 'refined_from_labels' in op_data and op_data['refined_from_labels']: - refined_from = op_data['refined_from_labels'][0] # Should be single label + if op_data.get("refined_from_labels"): + refined_from = op_data["refined_from_labels"][ + 0 + ] # Should be single label if refined_from in all_labels: # Replace the parent label with child labels all_labels.discard(refined_from) - all_labels.update(op_data['ordered_gmm_labels']) - + all_labels.update(op_data["ordered_gmm_labels"]) + return list(all_labels) def plot_feature_distribution_exploratory( self, feature: str, - obs_label: Optional[str] = None, - value_to_subset: Optional[str] = None, - layer: Optional[str] = None, - hist_kwargs: Optional[Dict] = None, - ax: Optional[Axes] = None, - x_axis_limits: Optional[tuple] = None, + obs_label: str | None = None, + value_to_subset: str | None = None, + layer: str | None = None, + hist_kwargs: dict | None = None, + ax: Axes | None = None, + x_axis_limits: tuple | None = None, ) -> Axes: """ Plot histogram of a feature distribution for exploratory analysis. - + This method allows you to visualize feature distributions WITHOUT running any thresholding, so you can explore your data and decide on manual thresholds or the number of components to use for GMM. - + Parameters ---------- feature : str Feature name to plot (must exist in adata.var_names). obs_label : Optional[str], optional - Obs column to use for subsetting. If provided with value_to_subset, - only plots cells with that label value. If None, plots all cells. + Obs column to use for subsetting. If provided with value_to_subset, + only plots cells with that label value. If None, plots all cells. Defaults to None. value_to_subset : Optional[str], optional - Specific label value to plot. Requires obs_label to be specified. - If None, plots all cells (or all cells in obs_label if provided). + Specific label value to plot. Requires obs_label to be specified. + If None, plots all cells (or all cells in obs_label if provided). Defaults to None. layer : Optional[str], optional Layer to use for data. If None, uses adata.X. Defaults to None. hist_kwargs : Optional[Dict], optional - Keyword arguments for plt.hist(). + Keyword arguments for plt.hist(). Defaults to {'bins': 50, 'color': 'black', 'alpha': 0.7}. ax : Optional[plt.Axes], optional Matplotlib axes to plot on. If None, uses current axes. Defaults to None. x_axis_limits : Optional[tuple], optional (min, max) for x-axis. Use None for data-driven limits. Defaults to None. - + Returns ------- Axes The matplotlib axes object. - + Raises ------ ValueError @@ -953,11 +956,11 @@ def plot_feature_distribution_exploratory( If obs_label doesn't exist in adata.obs. ValueError If value_to_subset is not present in adata.obs[obs_label]. - + Examples -------- Explore entire dataset:: - + seq_gmm.plot_feature_distribution_exploratory( feature='Int_Intg_DNA_nuc', hist_kwargs={'bins': 30, 'color': 'steelblue'}, @@ -965,9 +968,9 @@ def plot_feature_distribution_exploratory( ) plt.title('DNA Content Distribution - All Cells') plt.show() - + Explore specific subset:: - + seq_gmm.plot_feature_distribution_exploratory( feature='Int_Intg_DNA_nuc', obs_label='cell_cycle_phase', @@ -980,13 +983,13 @@ def plot_feature_distribution_exploratory( """ if ax is None: ax = plt.gca() - + # Validate parameters if value_to_subset is not None and obs_label is None: raise ValueError( "obs_label must be provided when value_to_subset is specified." ) - + # Get subset of adata if value_to_subset provided if value_to_subset is not None: mask, _ = self._partition_data_by_label( @@ -998,7 +1001,7 @@ def plot_feature_distribution_exploratory( adata_subset = self.adata[mask, :] else: adata_subset = self.adata - + # Use base class histogram plotting method ax = super()._plot_hist_base( adata=adata_subset, @@ -1008,64 +1011,64 @@ def plot_feature_distribution_exploratory( ax=ax, x_axis_limits=x_axis_limits, ) - + return ax def plot_feature_strip_plot_exploratory( self, feature: str, - obs_label: Optional[str] = None, - value_to_subset: Optional[str] = None, - layer: Optional[str] = None, - hist_kwargs: Optional[Dict] = None, - strip_plot_kwargs: Optional[Dict] = None, + obs_label: str | None = None, + value_to_subset: str | None = None, + layer: str | None = None, + hist_kwargs: dict | None = None, + strip_plot_kwargs: dict | None = None, scatter_density: bool = True, - x_axis_limits: Optional[tuple] = None, - vmax: Optional[Union[int, float]] = None, + x_axis_limits: tuple | None = None, + vmax: float | None = None, ) -> tuple: """ Plot strip plot + histogram for exploratory analysis. - + Similar to plot_strip_plot_histogram_with_decision_boundaries() but WITHOUT decision boundaries, for exploring data before running threshold operations. - + This method allows you to visualize feature distributions WITHOUT running any thresholding, so you can explore your data and decide on manual thresholds or the number of components to use for GMM. - + Parameters ---------- feature : str Feature name to plot (must exist in adata.var_names). obs_label : Optional[str], optional - Obs column to use for subsetting. If provided with value_to_subset, - only plots cells with that label value. If None, plots all cells. + Obs column to use for subsetting. If provided with value_to_subset, + only plots cells with that label value. If None, plots all cells. Defaults to None. value_to_subset : Optional[str], optional - Specific label value to plot. Requires obs_label to be specified. - If None, plots all cells (or all cells in obs_label if provided). + Specific label value to plot. Requires obs_label to be specified. + If None, plots all cells (or all cells in obs_label if provided). Defaults to None. layer : Optional[str], optional Layer to use for data. If None, uses adata.X. Defaults to None. hist_kwargs : Optional[Dict], optional Keyword arguments for histogram. Defaults to None. strip_plot_kwargs : Optional[Dict], optional - Keyword arguments for strip plot. Only used when scatter_density=False. + Keyword arguments for strip plot. Only used when scatter_density=False. Defaults to None. scatter_density : bool, optional - If True, uses density-based coloring. If False, uses uniform scatter plot. + If True, uses density-based coloring. If False, uses uniform scatter plot. Defaults to True. x_axis_limits : Optional[tuple], optional (min, max) for x-axis. Use None for data-driven limits. Defaults to None. vmax : Optional[Union[int, float]], optional - Maximum density value for colormap. Only used when scatter_density=True. + Maximum density value for colormap. Only used when scatter_density=True. If None, auto-calculated. Defaults to None. - + Returns ------- tuple (fig, (ax_strip, ax_hist)) - Figure and axes objects. - + Raises ------ ValueError @@ -1074,11 +1077,11 @@ def plot_feature_strip_plot_exploratory( If obs_label doesn't exist in adata.obs. ValueError If value_to_subset is not present in adata.obs[obs_label]. - + Examples -------- Explore entire dataset:: - + fig, (ax_strip, ax_hist) = seq_gmm.plot_feature_strip_plot_exploratory( feature='Int_Intg_DNA_nuc', scatter_density=True, @@ -1086,9 +1089,9 @@ def plot_feature_strip_plot_exploratory( ) plt.suptitle('DNA Content Distribution - All Cells') plt.show() - + Explore specific subset:: - + fig, (ax_strip, ax_hist) = seq_gmm.plot_feature_strip_plot_exploratory( feature='Int_Intg_DNA_nuc', obs_label='cell_cycle_phase', @@ -1104,7 +1107,7 @@ def plot_feature_strip_plot_exploratory( raise ValueError( "obs_label must be provided when value_to_subset is specified." ) - + # Get subset of adata if value_to_subset provided if value_to_subset is not None: mask, _ = self._partition_data_by_label( @@ -1116,7 +1119,7 @@ def plot_feature_strip_plot_exploratory( adata_subset = self.adata[mask, :] else: adata_subset = self.adata - + # Call base method to create strip plot and histogram (without decision boundaries) fig, ax_strip, ax_hist = super()._plot_strip_plot_base( adata=adata_subset, @@ -1128,27 +1131,27 @@ def plot_feature_strip_plot_exploratory( y_axis_limits=x_axis_limits, # Note: x_axis becomes y_axis in vertical plot hist_kwargs=hist_kwargs, strip_plot_kwargs=strip_plot_kwargs, - cmap=mpl.colormaps['plasma'], + cmap=mpl.colormaps["plasma"], vmax=vmax, ) - + return fig, (ax_strip, ax_hist) def plot_hist_distribution_with_boundaries( self, operation_name: str, num_std: int = 5, - title: Optional[str] = None, - hist_kwargs: Optional[Dict] = None, - cmap: Optional[mpl_colors.Colormap] = None, - ax: Optional[Axes] = None, - x_axis_limits: Optional[tuple] = None, + title: str | None = None, + hist_kwargs: dict | None = None, + cmap: mpl_colors.Colormap | None = None, + ax: Axes | None = None, + x_axis_limits: tuple | None = None, resolution: int = 1000, - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, ) -> Axes: """ Plot histogram with boundaries for a specific operation. - + Parameters ---------- operation_name : str @@ -1156,7 +1159,7 @@ def plot_hist_distribution_with_boundaries( num_std : int, optional Number of standard deviations for GMM plotting. Defaults to 5. title : Optional[str], optional - Plot title. If not provided, defaults to feature name. + Plot title. If not provided, defaults to feature name. Pass empty string '' to suppress title. Defaults to None. hist_kwargs : Optional[Dict], optional Kwargs for histogram. Defaults to None. @@ -1170,12 +1173,12 @@ def plot_hist_distribution_with_boundaries( Resolution for plotting. Defaults to 1000. save_path : Optional[Union[str, Path]], optional Path to save the figure. Parent directory must exist. Defaults to None. - + Returns ------- Axes The matplotlib axes object. Call plt.show() to display it. - + Raises ------ KeyError @@ -1186,21 +1189,21 @@ def plot_hist_distribution_with_boundaries( If resolution <= 0 or <= n_components. FileNotFoundError If save_path parent directory doesn't exist. - + Examples -------- :: - + ax = seq_gmm.plot_hist_distribution_with_boundaries('Plk1_refinement') plt.show() """ # Validate save path before generating the figure save_path = _validate_save_path(save_path) - + # Set default colormap if not provided if cmap is None: - cmap = plt.get_cmap('rainbow') - + cmap = plt.get_cmap("rainbow") + # Validate operation_name exists if operation_name not in self.adata.uns[self.thresholding_events_key]: raise KeyError( @@ -1208,45 +1211,45 @@ def plot_hist_distribution_with_boundaries( f"adata.uns['{self.thresholding_events_key}']. " f"Available operations: {list(self.adata.uns[self.thresholding_events_key].keys())}" ) - + # Load operation data from .uns op_data = self.adata.uns[self.thresholding_events_key][operation_name] internal_data = _SingleThresholdingEventModel(**op_data) - feature = op_data['feature_name'] - layer = op_data.get('layer', None) - obs_label = op_data['gmm_obs_label'] - ordered_labels = op_data['ordered_gmm_labels'] - + feature = op_data["feature_name"] + layer = op_data.get("layer", None) + obs_label = op_data["gmm_obs_label"] + ordered_labels = op_data["ordered_gmm_labels"] + # Validate decision boundaries exist if internal_data.decision_boundaries is None: raise ValueError( f"Decision boundaries not found for operation '{operation_name}'. " "This should not happen." ) - + # Validate resolution if resolution <= 0: raise ValueError("Resolution must be a positive integer.") - + if internal_data.gmm_info is not None: if resolution <= internal_data.gmm_info.n_components: raise ValueError( "Resolution must be greater than the number of GMM components." ) - + # Filter adata to only include cells with labels from this operation # For refinement operations, we need to include all descendant labels # since subsequent operations may have further refined the labels - if 'refined_from_labels' in op_data and op_data['refined_from_labels']: + if op_data.get("refined_from_labels"): # This was a refinement operation - find all labels that descended from it labels_to_plot = self._get_descendant_labels(operation_name, ordered_labels) else: # This was an initial threshold operation - use ordered_labels directly labels_to_plot = ordered_labels - + mask = self.adata.obs[obs_label].isin(labels_to_plot) adata_subset = self.adata[mask, :] - + # Call base class plotting method with explicit parameters ax = super()._plot_hist_base( adata=adata_subset, @@ -1256,7 +1259,7 @@ def plot_hist_distribution_with_boundaries( ax=ax, x_axis_limits=x_axis_limits, ) - + # Plot GMM components if this was GMM-based (not manual) if internal_data.gmm_info is not None: ax = super()._plot_gmm_components( @@ -1266,58 +1269,52 @@ def plot_hist_distribution_with_boundaries( internal_data=internal_data, num_std=num_std, resolution=resolution, - cmap=cmap + cmap=cmap, ) - + # Plot decision boundaries ax = super()._plot_vertical_linear_decision_boundaries( - ax=ax, - internal_data=internal_data, - resolution=resolution, - cmap=cmap + ax=ax, internal_data=internal_data, resolution=resolution, cmap=cmap ) - + # Plot legend super()._plot_sample_catergory_legend( - ax=ax, - internal_data=internal_data, - cmap=cmap, - legend_kwargs=None + ax=ax, internal_data=internal_data, cmap=cmap, legend_kwargs=None ) - + # Add title (default to feature name, allow override or suppression) if title is None: title = feature if title: # Only add title if not empty string ax.set_title(title) - + # Save figure if save_path is provided if save_path: - plt.savefig(save_path, bbox_inches='tight', dpi=300) + plt.savefig(save_path, bbox_inches="tight", dpi=300) print(f"Figure saved to: {save_path}") - + return ax def plot_strip_plot_histogram_with_decision_boundaries( self, operation_name: str, - cmap: Optional[mpl_colors.Colormap] = None, - y_axis_limits: Optional[Tuple[float, float]] = None, + cmap: mpl_colors.Colormap | None = None, + y_axis_limits: tuple[float, float] | None = None, resolution: int = 1000, scatter_density: bool = True, - vmax: Optional[Union[int, float]] = None, - hist_kwargs: Optional[Dict] = None, - strip_plot_kwargs: Optional[Dict] = None, - title: Optional[str] = None, + vmax: float | None = None, + hist_kwargs: dict | None = None, + strip_plot_kwargs: dict | None = None, + title: str | None = None, ) -> Figure: """ Plot 1D strip plot with histogram and decision boundaries for a specific operation. - + This method wraps the base class implementation to provide visualization for sequential thresholding operations. It creates a density strip plot (or label-colored scatter) alongside a horizontal histogram showing the distribution and decision boundaries for the specified operation. - + Parameters ---------- operation_name : str @@ -1335,45 +1332,45 @@ def plot_strip_plot_histogram_with_decision_boundaries( hist_kwargs : Optional[Dict], optional Kwargs for histogram (bins, color, etc.). Defaults to None. strip_plot_kwargs : Optional[Dict], optional - Kwargs for strip plot scatter (e.g., s, alpha, marker). + Kwargs for strip plot scatter (e.g., s, alpha, marker). Only used when scatter_density=False. Defaults to None. title : Optional[str], optional - Title for the plot. If not provided, defaults to feature name. + Title for the plot. If not provided, defaults to feature name. Pass empty string '' to suppress title. Defaults to None. - + Returns ------- Figure The matplotlib figure object. Call plt.show() to display it. - + Raises ------ KeyError If operation_name not found in .uns. ValueError If operation has no decision boundaries. - + Examples -------- Basic usage with label-colored scatter:: - + fig = seq_gmm.plot_strip_plot_histogram_with_decision_boundaries( operation_name='separate_M_phase', scatter_density=False ) plt.show() - + Custom title:: - + fig = seq_gmm.plot_strip_plot_histogram_with_decision_boundaries( operation_name='separate_M_phase', scatter_density=False, title='M Phase Separation' ) plt.show() - + Customize strip plot appearance:: - + fig = seq_gmm.plot_strip_plot_histogram_with_decision_boundaries( operation_name='separate_M_phase', scatter_density=False, @@ -1388,38 +1385,38 @@ def plot_strip_plot_histogram_with_decision_boundaries( f"adata.uns['{self.thresholding_events_key}']. " f"Available operations: {list(self.adata.uns[self.thresholding_events_key].keys())}" ) - + # Load operation data from .uns op_data = self.adata.uns[self.thresholding_events_key][operation_name] internal_data = _SingleThresholdingEventModel(**op_data) - feature = op_data['feature_name'] - layer = op_data.get('layer', None) - obs_label = op_data['gmm_obs_label'] - ordered_labels = op_data['ordered_gmm_labels'] - + feature = op_data["feature_name"] + layer = op_data.get("layer", None) + obs_label = op_data["gmm_obs_label"] + ordered_labels = op_data["ordered_gmm_labels"] + # Validate decision boundaries exist if internal_data.decision_boundaries is None: raise ValueError( f"Decision boundaries not found for operation '{operation_name}'." ) - + # Filter adata to only include cells with labels from this operation # For refinement operations, we need to include all descendant labels # since subsequent operations may have further refined the labels - if 'refined_from_labels' in op_data and op_data['refined_from_labels']: + if op_data.get("refined_from_labels"): # This was a refinement operation - find all labels that descended from it labels_to_plot = self._get_descendant_labels(operation_name, ordered_labels) else: # This was an initial threshold operation - use ordered_labels directly labels_to_plot = ordered_labels - + mask = self.adata.obs[obs_label].isin(labels_to_plot) adata_subset = self.adata[mask, :] - + # Set default colormap if not provided if cmap is None: - cmap = mpl.colormaps['plasma'] - + cmap = mpl.colormaps["plasma"] + # Call base class implementation and return figure return super()._plot_strip_plot_histogram_with_decision_boundaries( adata=adata_subset, @@ -1437,5 +1434,3 @@ def plot_strip_plot_histogram_with_decision_boundaries( strip_plot_kwargs=strip_plot_kwargs, title=title, ) - - diff --git a/src/cc_mapping/thresholding/single.py b/src/cc_mapping/thresholding/single.py index 3dd1bad..d54a999 100644 --- a/src/cc_mapping/thresholding/single.py +++ b/src/cc_mapping/thresholding/single.py @@ -9,25 +9,24 @@ Main class for single-feature GMM thresholding """ -import warnings import numbers -from string import ascii_uppercase +import warnings from collections import OrderedDict -from typing import Dict, List, Optional, Union from pathlib import Path +from string import ascii_uppercase import anndata as ad -from matplotlib.figure import Figure import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np +from matplotlib.figure import Figure from sklearn.mixture import GaussianMixture from .base import ( - _GaussianMixtureModelInfo, + GaussianMixtureModelBase, _DecisionBoundariesModel, + _GaussianMixtureModelInfo, _SingleThresholdingEventModel, - GaussianMixtureModelBase, ) @@ -62,8 +61,8 @@ def __init__( feature: str, label_obs_save_str: str, thresholding_events_key: str = "gmm_thresholding_events", - layer: Optional[str] = None, - gmm_kwargs: Optional[dict] = None, + layer: str | None = None, + gmm_kwargs: dict | None = None, random_state: int = 42, ): """Initialize the GMMThresholding object. @@ -170,7 +169,7 @@ def __init__( self.thresholding_events_key = thresholding_events_key self.label_obs_save_str = label_obs_save_str self.feature: str = feature - self.layer: Optional[str] = layer + self.layer: str | None = layer self.random_state: int = random_state # variable initialization @@ -191,11 +190,11 @@ def __init__( else: raise TypeError("gmm_kwargs must be a dictionary or None") - self._gmm_info: Optional[_GaussianMixtureModelInfo] = _GaussianMixtureModelInfo( + self._gmm_info: _GaussianMixtureModelInfo | None = _GaussianMixtureModelInfo( gmm_kwargs=self.gmm_kwargs, ) - self._decision_boundaries: Optional[_DecisionBoundariesModel] = None - self._internal_data: Optional[_SingleThresholdingEventModel] = ( + self._decision_boundaries: _DecisionBoundariesModel | None = None + self._internal_data: _SingleThresholdingEventModel | None = ( _SingleThresholdingEventModel( gmm_info=self._gmm_info, feature_name=self.feature, @@ -316,13 +315,13 @@ def return_adata(self, overwrite: bool = False) -> ad.AnnData: def plot_hist_distribution_with_boundaries( self, num_std: int = 5, - title: Optional[str] = None, - hist_kwargs: Optional[Dict] = None, + title: str | None = None, + hist_kwargs: dict | None = None, cmap: plt.cm.ScalarMappable = plt.get_cmap("rainbow"), ax: plt.Axes = None, - x_axis_limits: Optional[tuple] = None, + x_axis_limits: tuple | None = None, resolution: int = 1000, - save_path: Optional[Union[str, Path]] = None, + save_path: str | Path | None = None, ) -> plt.Axes: """Plot the histogram and GMM components with decision boundaries. @@ -439,8 +438,8 @@ def _calculate_decision_boundaries(self) -> None: def categorize_samples( self, - manual_thresholds: Optional[List[Union[float, int]]] = None, - ordered_labels: Optional[list] = None, + manual_thresholds: list[float | int] | None = None, + ordered_labels: list | None = None, duplicate_labels: bool = False, ) -> None: """Categorize samples based on GMM-derived or manual thresholds. @@ -691,7 +690,7 @@ def categorize_samples( self.adata.obs[self.label_obs_save_str] = sample_labels self._internal_data.ordered_gmm_labels = ordered_labels - def return_thresholds(self) -> List[float]: + def return_thresholds(self) -> list[float]: """Return the decision boundary thresholds. Returns @@ -713,13 +712,13 @@ def return_thresholds(self) -> List[float]: def plot_strip_plot_histogram_with_decision_boundaries( self, cmap: plt.cm.ScalarMappable = mpl.colormaps["plasma"], - y_axis_limits: Optional[tuple] = None, + y_axis_limits: tuple | None = None, resolution: int = 1000, scatter_density: bool = True, - vmax: Optional[Union[int, float]] = None, - hist_kwargs: Optional[dict] = None, - strip_plot_kwargs: Optional[dict] = None, - title: Optional[str] = None, + vmax: float | None = None, + hist_kwargs: dict | None = None, + strip_plot_kwargs: dict | None = None, + title: str | None = None, ) -> Figure: """Generate a strip plot with a histogram and decision boundaries. @@ -791,9 +790,9 @@ def plot_strip_plot_histogram_with_decision_boundaries( def plot_feature_distribution_exploratory( self, - hist_kwargs: Optional[Dict] = None, - ax: Optional[plt.Axes] = None, - x_axis_limits: Optional[tuple] = None, + hist_kwargs: dict | None = None, + ax: plt.Axes | None = None, + x_axis_limits: tuple | None = None, ) -> plt.Axes: """Plot histogram of the feature distribution for exploratory analysis. @@ -849,10 +848,10 @@ def plot_feature_distribution_exploratory( def plot_feature_strip_plot_exploratory( self, - hist_kwargs: Optional[Dict] = None, - strip_plot_kwargs: Optional[Dict] = None, + hist_kwargs: dict | None = None, + strip_plot_kwargs: dict | None = None, scatter_density: bool = True, - x_axis_limits: Optional[tuple] = None, + x_axis_limits: tuple | None = None, ) -> tuple: """Plot strip plot + histogram for exploratory analysis. @@ -915,7 +914,7 @@ def determine_optimal_components( curve: str = "convex", direction: str = "decreasing", return_bic_list: bool = False, - ) -> Union[int, tuple]: + ) -> int | tuple: """Determine the optimal number of GMM components for this feature. This is a convenience wrapper that automatically uses the instance's @@ -974,8 +973,8 @@ def plot_bic_curve( component_range: int, curve: str = "convex", direction: str = "decreasing", - ax: Optional[plt.Axes] = None, - save_path: Optional[Union[str, Path]] = None, + ax: plt.Axes | None = None, + save_path: str | Path | None = None, ) -> None: """Plot the Bayesian Information Criterion (BIC) curve. diff --git a/src/cc_mapping/utils.py b/src/cc_mapping/utils.py index 9f1cf4c..640db27 100644 --- a/src/cc_mapping/utils.py +++ b/src/cc_mapping/utils.py @@ -4,8 +4,6 @@ from __future__ import annotations -from typing import List - import anndata as ad import numpy as np import pandas as pd @@ -14,9 +12,9 @@ def create_boolean_label_combination( adata: ad.AnnData, obs_key_1: str, - match_values_1: List[str], + match_values_1: list[str], obs_key_2: str, - match_values_2: List[str], + match_values_2: list[str], operator: str, output_obs_key: str, true_label: str, @@ -25,10 +23,10 @@ def create_boolean_label_combination( ) -> ad.AnnData: """ Combine two categorical observation columns using boolean operators. - + Creates a new binary label based on whether cells match specified values in both input observation columns, using the specified boolean operator. - + Parameters ---------- adata : ad.AnnData @@ -50,14 +48,14 @@ def create_boolean_label_combination( false_label : str Label for cells not matching the boolean criteria. overwrite : bool, default False - If True, overwrites existing output_key. If False, raises error if + If True, overwrites existing output_key. If False, raises error if output_key exists. - + Returns ------- ad.AnnData Modified AnnData object with new observation column. - + Raises ------ KeyError @@ -72,11 +70,11 @@ def create_boolean_label_combination( If any values in match_values_1 not found in obs_key_1. ValueError If any values in match_values_2 not found in obs_key_2. - + Examples -------- AND: Both conditions must be true - + >>> adata = create_boolean_label_combination( ... adata, ... obs_key_1='treatment', @@ -88,9 +86,9 @@ def create_boolean_label_combination( ... true_label='control_G0', ... false_label='other' ... ) - + OR: Either condition true - + >>> adata = create_boolean_label_combination( ... adata, ... obs_key_1='treatment', @@ -102,9 +100,9 @@ def create_boolean_label_combination( ... true_label='positive', ... false_label='other' ... ) - + XOR: Exactly one condition true (exclusive or) - + >>> adata = create_boolean_label_combination( ... adata, ... obs_key_1='marker_A', @@ -123,39 +121,33 @@ def create_boolean_label_combination( f"obs_key_1 '{obs_key_1}' not found in adata.obs. " f"Available columns: {list(adata.obs.columns)}" ) - + if obs_key_2 not in adata.obs.columns: raise KeyError( f"obs_key_2 '{obs_key_2}' not found in adata.obs. " f"Available columns: {list(adata.obs.columns)}" ) - + # Validate operator - valid_operators = ['AND', 'OR', 'XOR'] + valid_operators = ["AND", "OR", "XOR"] operator = operator.upper() if operator not in valid_operators: - raise ValueError( - f"operator must be one of {valid_operators}, got '{operator}'" - ) - + raise ValueError(f"operator must be one of {valid_operators}, got '{operator}'") + # Validate output_obs_key doesn't already exist (unless overwrite=True) if output_obs_key in adata.obs.columns and not overwrite: raise KeyError( f"output_obs_key '{output_obs_key}' already exists in adata.obs. " "Set overwrite=True to replace it, or choose a different name." ) - + # Validate match values are lists if not isinstance(match_values_1, list): - raise TypeError( - f"match_values_1 must be a list, got {type(match_values_1)}" - ) - + raise TypeError(f"match_values_1 must be a list, got {type(match_values_1)}") + if not isinstance(match_values_2, list): - raise TypeError( - f"match_values_2 must be a list, got {type(match_values_2)}" - ) - + raise TypeError(f"match_values_2 must be a list, got {type(match_values_2)}") + # Validate all values exist in their respective observation columns unique_obs_1 = set(adata.obs[obs_key_1].unique()) for val in match_values_1: @@ -164,7 +156,7 @@ def create_boolean_label_combination( f"Value '{val}' not found in obs_key_1 '{obs_key_1}'. " f"Available values: {sorted(unique_obs_1)}" ) - + unique_obs_2 = set(adata.obs[obs_key_2].unique()) for val in match_values_2: if val not in unique_obs_2: @@ -172,21 +164,21 @@ def create_boolean_label_combination( f"Value '{val}' not found in obs_key_2 '{obs_key_2}'. " f"Available values: {sorted(unique_obs_2)}" ) - + # Create boolean masks mask1 = adata.obs[obs_key_1].isin(match_values_1) mask2 = adata.obs[obs_key_2].isin(match_values_2) - + # Apply boolean operator - if operator == 'AND': + if operator == "AND": final_mask = mask1 & mask2 - elif operator == 'OR': + elif operator == "OR": final_mask = mask1 | mask2 - elif operator == 'XOR': + elif operator == "XOR": final_mask = mask1 ^ mask2 - + # Create new categorical column new_labels = np.where(final_mask, true_label, false_label) adata.obs[output_obs_key] = pd.Categorical(new_labels) - - return adata \ No newline at end of file + + return adata diff --git a/src/cc_mapping/utils_v0.py b/src/cc_mapping/utils_v0.py index ca10cab..a91f180 100644 --- a/src/cc_mapping/utils_v0.py +++ b/src/cc_mapping/utils_v0.py @@ -1,11 +1,10 @@ from __future__ import annotations import re -import anndata as ad from collections import Counter from math import floor -from typing import List +import anndata as ad import numpy as np import pandas as pd @@ -44,9 +43,8 @@ def get_str_idx( regex, string_to_search, *reFlags ) else: - search_func = ( - lambda matching_string, string_to_search: matching_string - == string_to_search + search_func = lambda matching_string, string_to_search: ( + matching_string == string_to_search ) if isinstance(strings_to_find, str): @@ -91,7 +89,9 @@ def get_str_idx( return feature_idxs, feature_names -def equalize_conditions(adata: ad.AnnData, obs_str: str, ignore_min_list: list[str] = None) -> ad.AnnData: +def equalize_conditions( + adata: ad.AnnData, obs_str: str, ignore_min_list: list[str] = None +) -> ad.AnnData: """ Equalizes the conditions in the given AnnData object based on the specified observation string. @@ -131,9 +131,9 @@ def equalize_conditions(adata: ad.AnnData, obs_str: str, ignore_min_list: list[s def create_boolean_label_combination( adata: ad.AnnData, obs_key_1: str, - match_values_1: List[str], + match_values_1: list[str], obs_key_2: str, - match_values_2: List[str], + match_values_2: list[str], operator: str, output_obs_key: str, true_label: str, @@ -142,10 +142,10 @@ def create_boolean_label_combination( ) -> ad.AnnData: """ Combine two categorical observation columns using boolean operators. - + Creates a new binary label based on whether cells match specified values in both input observation columns, using the specified boolean operator. - + Parameters ---------- adata : ad.AnnData @@ -167,14 +167,14 @@ def create_boolean_label_combination( false_label : str Label for cells not matching the boolean criteria. overwrite : bool, default False - If True, overwrites existing output_key. If False, raises error if + If True, overwrites existing output_key. If False, raises error if output_key exists. - + Returns ------- ad.AnnData Modified AnnData object with new observation column. - + Raises ------ KeyError @@ -189,11 +189,11 @@ def create_boolean_label_combination( If any values in match_values_1 not found in obs_key_1. ValueError If any values in match_values_2 not found in obs_key_2. - + Examples -------- AND: Both conditions must be true - + >>> adata = create_boolean_label_combination( ... adata, ... obs_key_1='treatment', @@ -205,9 +205,9 @@ def create_boolean_label_combination( ... true_label='control_G0', ... false_label='other' ... ) - + OR: Either condition true - + >>> adata = create_boolean_label_combination( ... adata, ... obs_key_1='treatment', @@ -219,9 +219,9 @@ def create_boolean_label_combination( ... true_label='positive', ... false_label='other' ... ) - + XOR: Exactly one condition true (exclusive or) - + >>> adata = create_boolean_label_combination( ... adata, ... obs_key_1='marker_A', @@ -240,39 +240,33 @@ def create_boolean_label_combination( f"obs_key_1 '{obs_key_1}' not found in adata.obs. " f"Available columns: {list(adata.obs.columns)}" ) - + if obs_key_2 not in adata.obs.columns: raise KeyError( f"obs_key_2 '{obs_key_2}' not found in adata.obs. " f"Available columns: {list(adata.obs.columns)}" ) - + # Validate operator - valid_operators = ['AND', 'OR', 'XOR'] + valid_operators = ["AND", "OR", "XOR"] operator = operator.upper() if operator not in valid_operators: - raise ValueError( - f"operator must be one of {valid_operators}, got '{operator}'" - ) - + raise ValueError(f"operator must be one of {valid_operators}, got '{operator}'") + # Validate output_obs_key doesn't already exist (unless overwrite=True) if output_obs_key in adata.obs.columns and not overwrite: raise KeyError( f"output_obs_key '{output_obs_key}' already exists in adata.obs. " "Set overwrite=True to replace it, or choose a different name." ) - + # Validate match values are lists if not isinstance(match_values_1, list): - raise TypeError( - f"match_values_1 must be a list, got {type(match_values_1)}" - ) - + raise TypeError(f"match_values_1 must be a list, got {type(match_values_1)}") + if not isinstance(match_values_2, list): - raise TypeError( - f"match_values_2 must be a list, got {type(match_values_2)}" - ) - + raise TypeError(f"match_values_2 must be a list, got {type(match_values_2)}") + # Validate all values exist in their respective observation columns unique_obs_1 = set(adata.obs[obs_key_1].unique()) for val in match_values_1: @@ -281,7 +275,7 @@ def create_boolean_label_combination( f"Value '{val}' not found in obs_key_1 '{obs_key_1}'. " f"Available values: {sorted(unique_obs_1)}" ) - + unique_obs_2 = set(adata.obs[obs_key_2].unique()) for val in match_values_2: if val not in unique_obs_2: @@ -289,28 +283,31 @@ def create_boolean_label_combination( f"Value '{val}' not found in obs_key_2 '{obs_key_2}'. " f"Available values: {sorted(unique_obs_2)}" ) - + # Create boolean masks mask1 = adata.obs[obs_key_1].isin(match_values_1) mask2 = adata.obs[obs_key_2].isin(match_values_2) - + # Apply boolean operator - if operator == 'AND': + if operator == "AND": final_mask = mask1 & mask2 - elif operator == 'OR': + elif operator == "OR": final_mask = mask1 | mask2 - elif operator == 'XOR': + elif operator == "XOR": final_mask = mask1 ^ mask2 - + # Create new categorical column new_labels = np.where(final_mask, true_label, false_label) adata.obs[output_obs_key] = pd.Categorical(new_labels) - + return adata def equalize_within_two_conditions( - adata: ad.AnnData, first_obs_str: str, second_obs_str: str, ignore_min_list: list[str] = None + adata: ad.AnnData, + first_obs_str: str, + second_obs_str: str, + ignore_min_list: list[str] = None, ) -> ad.AnnData: """ Equalizes the number of observations within two conditions in a single-cell dataset. @@ -382,4 +379,4 @@ def equalize_within_two_conditions( adata = adata[idx_list, :].copy() - return adata \ No newline at end of file + return adata diff --git a/tests/conftest.py b/tests/conftest.py index 1aa4f14..37ef340 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,5 @@ -import sys import os +import sys import anndata as ad import numpy as np @@ -12,23 +12,28 @@ def pytest_configure(): """ Adds the project root directory to the Python path. """ - project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Adjust the path as needed + project_root = os.path.dirname( + os.path.dirname(os.path.abspath(__file__)) + ) # Adjust the path as needed sys.path.insert(0, project_root) + @pytest.fixture def sample_adata(): """Creates a sample AnnData object for testing.""" np.random.seed(0) xx = np.random.normal(loc=1, scale=0.5, size=(1000, 2)) - adata = ad.AnnData(X = xx) - adata.var_names = ['gene1', 'gene2'] + adata = ad.AnnData(X=xx) + adata.var_names = ["gene1", "gene2"] return adata + @pytest.fixture def sample_gmm_thresholding_instance(sample_adata): """Creates an instance of GMMThresholding for testing.""" - gmm_thresholding_instance = GMMThresholding(adata=sample_adata, - feature='gene1', - label_obs_save_str='labels', - ) - return gmm_thresholding_instance \ No newline at end of file + gmm_thresholding_instance = GMMThresholding( + adata=sample_adata, + feature="gene1", + label_obs_save_str="labels", + ) + return gmm_thresholding_instance diff --git a/tests/helpers.py b/tests/helpers.py index fef7c15..ce0e0b3 100644 --- a/tests/helpers.py +++ b/tests/helpers.py @@ -21,26 +21,30 @@ improve the readability and maintainability of the test suite, and centralize common setup and verification logic. """ -from typing import Any, Dict, Optional, Tuple, List, Union, Type + from collections import OrderedDict +from typing import Any import anndata as ad import numpy as np -from src.cc_mapping.thresholding import (GMMThresholding, - _SingleThresholdingEventModel, - _GaussianMixtureModelInfo) +from src.cc_mapping.thresholding import ( + GMMThresholding, + _GaussianMixtureModelInfo, + _SingleThresholdingEventModel, +) + def create_modified_adata( base_adata: ad.AnnData, - *, # Force subsequent arguments to be keyword-only for clarity - x_dtype: Optional[Union[Type[np.generic], np.dtype]] = None, - x_value_at: Optional[Tuple[int, int, Any]] = None, - add_obs: Optional[Dict[str, Any]] = None, - add_uns: Optional[Dict[str, Any]] = None, - remove_obs: Optional[Union[List[str], Tuple[str, ...]]] = None, - remove_var: Optional[Union[List[str], Tuple[str, ...]]] = None, - remove_uns: Optional[Union[List[str], Tuple[str, ...]]] = None + *, # Force subsequent arguments to be keyword-only for clarity + x_dtype: type[np.generic] | np.dtype | None = None, + x_value_at: tuple[int, int, Any] | None = None, + add_obs: dict[str, Any] | None = None, + add_uns: dict[str, Any] | None = None, + remove_obs: list[str] | tuple[str, ...] | None = None, + remove_var: list[str] | tuple[str, ...] | None = None, + remove_uns: list[str] | tuple[str, ...] | None = None, ) -> ad.AnnData: """ Creates a modified copy of a base AnnData object for testing purposes. @@ -90,28 +94,32 @@ def create_modified_adata( # 1. Change X dtype (if requested) if x_dtype is not None: if adata_copy.X is None: - raise ValueError("Cannot change dtype of X because base_adata.X is None.") + raise ValueError("Cannot change dtype of X because base_adata.X is None.") try: # Use astype for numpy arrays, handle sparse potentially differently if needed # This assumes X is typically numpy or supports astype - adata_copy.X = adata_copy.X.astype(x_dtype) + adata_copy.X = adata_copy.X.astype(x_dtype) except Exception as e: print(f"Error changing X dtype to {x_dtype}: {e}") - raise # Re-raise the exception + raise # Re-raise the exception # 2. Set specific X value (if requested) if x_value_at is not None: if adata_copy.X is None: - raise ValueError("Cannot set value in X because base_adata.X is None.") + raise ValueError("Cannot set value in X because base_adata.X is None.") try: row, col, val = x_value_at adata_copy.X[row, col] = val except IndexError: - print(f"Error setting X value: Index ({row}, {col}) out of bounds for shape {adata_copy.X.shape}") + print( + f"Error setting X value: Index ({row}, {col}) out of bounds for shape {adata_copy.X.shape}" + ) raise except (TypeError, ValueError) as e: - print(f"Error setting X value: Type or value mismatch assigning '{val}' at ({row}, {col}). Error: {e}") - raise + print( + f"Error setting X value: Type or value mismatch assigning '{val}' at ({row}, {col}). Error: {e}" + ) + raise # 3. Add/Update .obs columns if add_obs: @@ -119,8 +127,14 @@ def create_modified_adata( raise TypeError("`add_obs` must be a dictionary of key-value pairs.") for key, value in add_obs.items(): # Consider adding a length check if value is array-like - if hasattr(value, '__len__') and not isinstance(value, str) and len(value) != adata_copy.n_obs: - raise ValueError(f"Length mismatch for obs key '{key}'. Expected {adata_copy.n_obs}, got {len(value)}.") + if ( + hasattr(value, "__len__") + and not isinstance(value, str) + and len(value) != adata_copy.n_obs + ): + raise ValueError( + f"Length mismatch for obs key '{key}'. Expected {adata_copy.n_obs}, got {len(value)}." + ) adata_copy.obs[key] = value # 4. Add/Update .uns entries @@ -158,11 +172,9 @@ def create_modified_adata( raise return adata_copy - -def assert_adata_copy_and_uns( - gmm_obj: GMMThresholding, - original_adata: ad.AnnData -): + + +def assert_adata_copy_and_uns(gmm_obj: GMMThresholding, original_adata: ad.AnnData): """ Asserts adata is copied and .uns['gmm_thresholding_events'] is initialized. @@ -172,27 +184,29 @@ def assert_adata_copy_and_uns( - `gmm_obj.adata.uns` contains the key 'gmm_thresholding_events'. - `gmm_obj.adata.uns['gmm_thresholding_events']` is an `OrderedDict`. """ - assert gmm_obj.adata is not original_adata, \ + assert gmm_obj.adata is not original_adata, ( "Object's adata should be a copy, not the same object." + ) # Check crucial components are equal (adjust if using sparse matrices etc.) - assert np.array_equal(gmm_obj.adata.X, original_adata.X), \ + assert np.array_equal(gmm_obj.adata.X, original_adata.X), ( "Copied adata.X data mismatch." - assert gmm_obj.adata.obs.equals(original_adata.obs), \ - "Copied adata.obs mismatch." - assert gmm_obj.adata.var.equals(original_adata.var), \ - "Copied adata.var mismatch." + ) + assert gmm_obj.adata.obs.equals(original_adata.obs), "Copied adata.obs mismatch." + assert gmm_obj.adata.var.equals(original_adata.var), "Copied adata.var mismatch." # Check .uns key specifically - assert "gmm_thresholding_events" in gmm_obj.adata.uns, \ + assert "gmm_thresholding_events" in gmm_obj.adata.uns, ( ".uns['gmm_thresholding_events'] key missing in object's adata." - assert isinstance(gmm_obj.adata.uns["gmm_thresholding_events"], OrderedDict), \ + ) + assert isinstance(gmm_obj.adata.uns["gmm_thresholding_events"], OrderedDict), ( ".uns['gmm_thresholding_events'] has wrong type (should be OrderedDict)." + ) def assert_direct_attributes_initialized( gmm_obj: GMMThresholding, expected_feature: str, expected_label: str, - expected_random_state: Optional[int] = 42 + expected_random_state: int | None = 42, ): """ Asserts direct attribute assignments from __init__ parameters are correct. @@ -205,13 +219,16 @@ def assert_direct_attributes_initialized( if expected_random_state is None: expected_random_state = 42 - assert gmm_obj.feature == expected_feature, \ + assert gmm_obj.feature == expected_feature, ( f"Attribute 'feature' mismatch. Expected '{expected_feature}', got '{gmm_obj.feature}'." - assert gmm_obj.label_obs_save_str == expected_label, \ + ) + assert gmm_obj.label_obs_save_str == expected_label, ( f"Attribute 'label_obs_save_str' mismatch. Expected '{expected_label}', got '{gmm_obj.label_obs_save_str}'." + ) # Checks the random_state *parameter value* stored on the object - assert gmm_obj.random_state == expected_random_state, \ + assert gmm_obj.random_state == expected_random_state, ( f"Attribute 'random_state' mismatch. Expected {expected_random_state}, got {gmm_obj.random_state}." + ) def assert_default_internal_states(gmm_obj: GMMThresholding): @@ -222,16 +239,18 @@ def assert_default_internal_states(gmm_obj: GMMThresholding): - `gmm_obj.manual_decision_boundaries` is False. - `gmm_obj.decision_boundaries` is None. """ - assert gmm_obj._manual_decision_boundaries is False, \ + assert gmm_obj._manual_decision_boundaries is False, ( "Attribute 'manual_decision_boundaries' should default to False." - assert gmm_obj._decision_boundaries is None, \ + ) + assert gmm_obj._decision_boundaries is None, ( "Attribute 'decision_boundaries' should default to None." + ) def assert_gmm_kwargs_processed( gmm_obj: GMMThresholding, - input_gmm_kwargs: Optional[dict] = None, # Original kwargs passed to __init__ - input_random_state: Optional[int] = None # Original random_state passed to __init__ + input_gmm_kwargs: dict | None = None, # Original kwargs passed to __init__ + input_random_state: int | None = None, # Original random_state passed to __init__ ): """ Asserts the gmm_obj.gmm_kwargs attribute reflects the correct processing. @@ -249,18 +268,21 @@ def assert_gmm_kwargs_processed( # Replicate the logic from __init__ to determine the expected kwargs if input_gmm_kwargs is None: expected_final_kwargs = { - 'init_params': 'k-means++', 'n_init': 10, 'max_iter': 1000, - 'random_state': input_random_state # Uses init param directly + "init_params": "k-means++", + "n_init": 10, + "max_iter": 1000, + "random_state": input_random_state, # Uses init param directly } elif isinstance(input_gmm_kwargs, dict): - expected_final_kwargs = input_gmm_kwargs.copy() # Work on copy + expected_final_kwargs = input_gmm_kwargs.copy() # Work on copy # If "random_state" *was* in input_gmm_kwargs, its value is kept. if "random_state" not in expected_final_kwargs: # Adds init param value if key is missing expected_final_kwargs["random_state"] = input_random_state - assert gmm_obj.gmm_kwargs == expected_final_kwargs, \ + assert gmm_obj.gmm_kwargs == expected_final_kwargs, ( f"Attribute 'gmm_kwargs' mismatch. Expected {expected_final_kwargs}, got {gmm_obj.gmm_kwargs}." + ) def assert_dependent_models_initialized(gmm_obj: GMMThresholding): @@ -276,17 +298,23 @@ def assert_dependent_models_initialized(gmm_obj: GMMThresholding): - `gmm_obj.internal_data.gmm_info` is the *same object* as `gmm_obj.gmm_info`. """ # Check gmm_info - assert isinstance(gmm_obj._gmm_info, _GaussianMixtureModelInfo), \ + assert isinstance(gmm_obj._gmm_info, _GaussianMixtureModelInfo), ( "Attribute 'gmm_info' has wrong type." - assert gmm_obj._gmm_info.gmm_kwargs == gmm_obj.gmm_kwargs, \ + ) + assert gmm_obj._gmm_info.gmm_kwargs == gmm_obj.gmm_kwargs, ( "Processed 'gmm_kwargs' not correctly propagated to 'gmm_info'." + ) # Check internal_data - assert isinstance(gmm_obj._internal_data, _SingleThresholdingEventModel), \ + assert isinstance(gmm_obj._internal_data, _SingleThresholdingEventModel), ( "Attribute 'internal_data' has wrong type." - assert gmm_obj._internal_data.feature_name == gmm_obj.feature, \ + ) + assert gmm_obj._internal_data.feature_name == gmm_obj.feature, ( "Attribute 'feature' not correctly propagated to 'internal_data'." - assert gmm_obj._internal_data.gmm_obs_label == gmm_obj.label_obs_save_str, \ + ) + assert gmm_obj._internal_data.gmm_obs_label == gmm_obj.label_obs_save_str, ( "Attribute 'label_obs_save_str' not correctly propagated to 'internal_data'." - assert gmm_obj._internal_data.gmm_info is gmm_obj._gmm_info, \ - "'gmm_info' object identity not correctly propagated to 'internal_data'." \ No newline at end of file + ) + assert gmm_obj._internal_data.gmm_info is gmm_obj._gmm_info, ( + "'gmm_info' object identity not correctly propagated to 'internal_data'." + ) diff --git a/tests/thresholding/test_categorize_samples.py b/tests/thresholding/test_categorize_samples.py index 63527b6..8f90286 100644 --- a/tests/thresholding/test_categorize_samples.py +++ b/tests/thresholding/test_categorize_samples.py @@ -20,9 +20,9 @@ def test_categorize_samples_default_success(sample_gmm_thresholding_instance): ): gmm.categorize_samples() - assert ( - not gmm._manual_decision_boundaries - ), "manual_decision_boundaries should be False by default." + assert not gmm._manual_decision_boundaries, ( + "manual_decision_boundaries should be False by default." + ) def test_categorize_samples_with_ordered_labels(sample_gmm_thresholding_instance): @@ -34,9 +34,9 @@ def test_categorize_samples_with_ordered_labels(sample_gmm_thresholding_instance gmm.categorize_samples(ordered_labels=["Low", "High"]) # Should not use manual thresholding - assert ( - not gmm._manual_decision_boundaries - ), "Should use automatic thresholding when no manual_thresholds provided" + assert not gmm._manual_decision_boundaries, ( + "Should use automatic thresholding when no manual_thresholds provided" + ) # Should have labels assigned assert "labels" in gmm.adata.obs.columns, "Labels should be added to adata.obs" diff --git a/tests/thresholding/test_clamping.py b/tests/thresholding/test_clamping.py index 2ef06f0..c1029c5 100644 --- a/tests/thresholding/test_clamping.py +++ b/tests/thresholding/test_clamping.py @@ -9,10 +9,11 @@ and ensures visualization matches actual cell assignments. """ -import pytest -import numpy as np import anndata as ad import matplotlib.pyplot as plt +import numpy as np +import pytest + from src.cc_mapping.thresholding import GMMThresholding @@ -72,9 +73,9 @@ def test_clamping_prevents_index_error_in_labeling(extreme_overlap_adata): # Verify only valid labels exist unique_labels = set(gmm.adata.obs["labels"].unique()) - assert unique_labels.issubset( - {"low", "high"} - ), f"Labels should only be 'low' or 'high', got: {unique_labels}" + assert unique_labels.issubset({"low", "high"}), ( + f"Labels should only be 'low' or 'high', got: {unique_labels}" + ) def test_clamping_prevents_index_error_in_plotting_vertical(extreme_overlap_adata): diff --git a/tests/thresholding/test_fit.py b/tests/thresholding/test_fit.py index fd6992f..6a3cfed 100644 --- a/tests/thresholding/test_fit.py +++ b/tests/thresholding/test_fit.py @@ -5,7 +5,6 @@ """ import pytest -from src.cc_mapping.thresholding import GMMThresholding def test_fit_success(sample_adata, sample_gmm_thresholding_instance): @@ -14,18 +13,18 @@ def test_fit_success(sample_adata, sample_gmm_thresholding_instance): gmm = sample_gmm_thresholding_instance gmm.fit(n_components=n_components) - assert ( - gmm._gmm_info.data_probs is not None - ), "GMM probabilities should not be None after fitting." + assert gmm._gmm_info.data_probs is not None, ( + "GMM probabilities should not be None after fitting." + ) assert len(gmm._gmm_info.data_probs) == len(sample_adata), ( "Length of GMM probabilities should match the number of samples." ) assert len(gmm._gmm_info.data_probs[0]) == n_components, ( "Number of GMM probabilities should match the number of components." ) - assert ( - gmm._internal_data.gmm_info == gmm._gmm_info - ), "Internal GMM info should match the fitted GMM info." + assert gmm._internal_data.gmm_info == gmm._gmm_info, ( + "Internal GMM info should match the fitted GMM info." + ) @pytest.mark.parametrize( diff --git a/tests/thresholding/test_initialization.py b/tests/thresholding/test_initialization.py index c2d1ba3..0884feb 100644 --- a/tests/thresholding/test_initialization.py +++ b/tests/thresholding/test_initialization.py @@ -4,30 +4,30 @@ initialization of the GMMThresholding class. """ +from collections import OrderedDict + +import matplotlib as mpl +import numpy as np +import pytest + from src.cc_mapping.thresholding import GMMThresholding from tests.helpers import ( assert_adata_copy_and_uns, - assert_direct_attributes_initialized, assert_default_internal_states, - assert_gmm_kwargs_processed, assert_dependent_models_initialized, - create_modified_adata + assert_direct_attributes_initialized, + assert_gmm_kwargs_processed, + create_modified_adata, ) -from collections import OrderedDict +mpl.use("Agg") # Set the backend before importing pyplot -import numpy as np -import pytest -import matplotlib as mpl -mpl.use('Agg') # Set the backend before importing pyplot - - # --- Initialization Tests --- def test_init_success_defaults(sample_adata): """Tests successful initialization with default kwargs using helper.""" - feature_name = 'gene1' - label_name = 'my_labels' + feature_name = "gene1" + label_name = "my_labels" gmm_thresholding = GMMThresholding( adata=sample_adata, @@ -47,16 +47,17 @@ def test_init_success_defaults(sample_adata): ) assert_dependent_models_initialized(gmm_thresholding) - assert len(gmm_thresholding.adata.uns['gmm_thresholding_events']) == 0, \ - "The `gmm_thresholding_events` should be initialized as an empty OrderedDict" + assert len(gmm_thresholding.adata.uns["gmm_thresholding_events"]) == 0, ( + "The `gmm_thresholding_events` should be initialized as an empty OrderedDict" + ) def test_init_success_custom_kwargs(sample_adata): """Tests successful initialization with custom gmm_kwargs using helper.""" - feature_name = 'gene2' - label_name = 'custom_output' + feature_name = "gene2" + label_name = "custom_output" # Custom kwargs *without* random_state initially - init_gmm_kwargs = {'n_components': 5, 'covariance_type': 'diag'} + init_gmm_kwargs = {"n_components": 5, "covariance_type": "diag"} init_random_state = 123 gmm_thresholding = GMMThresholding( @@ -64,7 +65,7 @@ def test_init_success_custom_kwargs(sample_adata): feature=feature_name, label_obs_save_str=label_name, gmm_kwargs=init_gmm_kwargs, - random_state=init_random_state + random_state=init_random_state, ) assert_adata_copy_and_uns(gmm_thresholding, sample_adata) @@ -72,30 +73,31 @@ def test_init_success_custom_kwargs(sample_adata): gmm_obj=gmm_thresholding, expected_feature=feature_name, expected_label=label_name, - expected_random_state=init_random_state # Ensure random state is set correctly + expected_random_state=init_random_state, # Ensure random state is set correctly ) assert_default_internal_states(gmm_thresholding) assert_gmm_kwargs_processed( gmm_obj=gmm_thresholding, - input_gmm_kwargs=init_gmm_kwargs, # Pass the original dict to check processing - input_random_state=init_random_state # Ensure random state is passed correctly to GMM + input_gmm_kwargs=init_gmm_kwargs, # Pass the original dict to check processing + input_random_state=init_random_state, # Ensure random state is passed correctly to GMM ) assert_dependent_models_initialized(gmm_thresholding) + def test_init_success_custom_kwargs_with_random_state(sample_adata): """Tests initialization when random_state is already in gmm_kwargs using helper.""" - feature_name = 'gene1' - label_name = 'custom_output_rs' + feature_name = "gene1" + label_name = "custom_output_rs" # User provides random_state within the dict - init_gmm_kwargs = {'n_components': 2, 'random_state': 999} - init_random_state = 123 # This should be stored in self.random_state but ignored for gmm_kwargs dict + init_gmm_kwargs = {"n_components": 2, "random_state": 999} + init_random_state = 123 # This should be stored in self.random_state but ignored for gmm_kwargs dict gmm_thresholding = GMMThresholding( adata=sample_adata, feature=feature_name, label_obs_save_str=label_name, gmm_kwargs=init_gmm_kwargs, - random_state=init_random_state + random_state=init_random_state, ) assert_adata_copy_and_uns(gmm_thresholding, sample_adata) @@ -103,28 +105,31 @@ def test_init_success_custom_kwargs_with_random_state(sample_adata): gmm_obj=gmm_thresholding, expected_feature=feature_name, expected_label=label_name, - expected_random_state=init_random_state # Ensure random state is set correctly + expected_random_state=init_random_state, # Ensure random state is set correctly ) assert_default_internal_states(gmm_thresholding) assert_gmm_kwargs_processed( gmm_obj=gmm_thresholding, - input_gmm_kwargs=init_gmm_kwargs, # Pass the original dict to check processing - input_random_state=init_random_state # Ensure random state is passed correctly to GMM + input_gmm_kwargs=init_gmm_kwargs, # Pass the original dict to check processing + input_random_state=init_random_state, # Ensure random state is passed correctly to GMM ) assert_dependent_models_initialized(gmm_thresholding) + def test_init_success_existing_uns_key(sample_adata): """Tests initialization when the uns key already exists correctly.""" - existing_uns_data = {'gmm_thresholding_events': OrderedDict({ 'previous_run': 'example_run_1'})} # Example data to simulate a previous run - modified_adata = create_modified_adata(sample_adata, add_uns= existing_uns_data) # Ensure the original sample_adata is unchanged + existing_uns_data = { + "gmm_thresholding_events": OrderedDict({"previous_run": "example_run_1"}) + } # Example data to simulate a previous run + modified_adata = create_modified_adata( + sample_adata, add_uns=existing_uns_data + ) # Ensure the original sample_adata is unchanged - feature_name = 'gene1' - label_name = 'new_labels' + feature_name = "gene1" + label_name = "new_labels" gmm_thresholding = GMMThresholding( - adata=modified_adata, - feature=feature_name, - label_obs_save_str=label_name + adata=modified_adata, feature=feature_name, label_obs_save_str=label_name ) assert_adata_copy_and_uns(gmm_thresholding, modified_adata) @@ -139,119 +144,140 @@ def test_init_success_existing_uns_key(sample_adata): ) assert_dependent_models_initialized(gmm_thresholding) - assert gmm_thresholding.adata.uns['gmm_thresholding_events'] == existing_uns_data['gmm_thresholding_events'], \ - 'The existing uns key `gmm_thresholding_events` should be preserved and match the original content.' - assert 'previous_run' in gmm_thresholding.adata.uns['gmm_thresholding_events'], \ - 'The existing uns key `gmm_thresholding_events` should still contain the previous run data.' + assert ( + gmm_thresholding.adata.uns["gmm_thresholding_events"] + == existing_uns_data["gmm_thresholding_events"] + ), ( + "The existing uns key `gmm_thresholding_events` should be preserved and match the original content." + ) + assert "previous_run" in gmm_thresholding.adata.uns["gmm_thresholding_events"], ( + "The existing uns key `gmm_thresholding_events` should still contain the previous run data." + ) + # --- Error Condition Tests --- # These tests verify exceptions are raised correctly. -@pytest.mark.parametrize("invalid_adata", [ - None, - 123, - "string", - {}, - [] -]) + +@pytest.mark.parametrize("invalid_adata", [None, 123, "string", {}, []]) def test_init_invalid_adata_type(invalid_adata): """Tests TypeError when adata is not an AnnData object.""" with pytest.raises(TypeError, match="adata must be an AnnData.AnnData object"): GMMThresholding( - adata=invalid_adata, - feature='gene1', - label_obs_save_str='labels' + adata=invalid_adata, feature="gene1", label_obs_save_str="labels" ) + def test_init_non_numeric_x_adata(sample_adata): """Tests TypeError when adata.X is not a numeric type.""" - non_numeric_x_adata = create_modified_adata(sample_adata, x_dtype=np.object_) # Ensure the original sample_adata is unchanged + non_numeric_x_adata = create_modified_adata( + sample_adata, x_dtype=np.object_ + ) # Ensure the original sample_adata is unchanged # Match the updated error message in __init__ with pytest.raises(TypeError, match="adata.X must be a numeric type"): GMMThresholding( - adata=non_numeric_x_adata, - feature='gene1', - label_obs_save_str='labels' + adata=non_numeric_x_adata, feature="gene1", label_obs_save_str="labels" ) + def test_init_existing_uns_key_wrong_type(sample_adata): """Tests TypeError when the uns key exists but is not an OrderedDict.""" - wrong_uns_type_adata = create_modified_adata(sample_adata,add_uns={'gmm_thresholding_events': {'not_dict':'values'}}) # Ensure the original sample_adata is unchanged + wrong_uns_type_adata = create_modified_adata( + sample_adata, add_uns={"gmm_thresholding_events": {"not_dict": "values"}} + ) # Ensure the original sample_adata is unchanged # Match the updated error message in __init__ - with pytest.raises(TypeError, match="The 'gmm_thresholding_events' key in the AnnData object's `.uns` attribute must be an OrderedDict."): + with pytest.raises( + TypeError, + match="The 'gmm_thresholding_events' key in the AnnData object's `.uns` attribute must be an OrderedDict.", + ): GMMThresholding( - adata=wrong_uns_type_adata, - feature='gene1', - label_obs_save_str='labels' + adata=wrong_uns_type_adata, feature="gene1", label_obs_save_str="labels" ) -@pytest.mark.parametrize("invalid_feature, expected_exception, match_pattern", [ - (None, TypeError, "feature must be a string"), - (123, TypeError, "feature must be a string"), - ([], TypeError, "feature must be a string"), - ("", ValueError, "feature cannot be an empty string"), -]) -def test_init_invalid_feature_type_or_value(sample_adata, invalid_feature, expected_exception, match_pattern): + +@pytest.mark.parametrize( + "invalid_feature, expected_exception, match_pattern", + [ + (None, TypeError, "feature must be a string"), + (123, TypeError, "feature must be a string"), + ([], TypeError, "feature must be a string"), + ("", ValueError, "feature cannot be an empty string"), + ], +) +def test_init_invalid_feature_type_or_value( + sample_adata, invalid_feature, expected_exception, match_pattern +): """Tests errors for invalid feature types or empty string.""" with pytest.raises(expected_exception, match=match_pattern): GMMThresholding( - adata=sample_adata, - feature=invalid_feature, - label_obs_save_str='labels' + adata=sample_adata, feature=invalid_feature, label_obs_save_str="labels" ) + def test_init_feature_not_found(sample_adata): """Tests KeyError when the feature is not in adata.var_names.""" - not_present_feature = 'unknown_gene' - with pytest.raises(KeyError, match=f"Feature '{not_present_feature}' not found in adata.var_names. Please check the feature name."): + not_present_feature = "unknown_gene" + with pytest.raises( + KeyError, + match=f"Feature '{not_present_feature}' not found in adata.var_names. Please check the feature name.", + ): GMMThresholding( adata=sample_adata, feature=not_present_feature, # This feature does not exist in sample_adata.var_names - label_obs_save_str='labels' + label_obs_save_str="labels", ) -@pytest.mark.parametrize("invalid_label, expected_exception,match_pattern", [ - (None, TypeError ,"label_obs_save_str must be a string"), - (123,TypeError ,"label_obs_save_str must be a string"), - ([], TypeError,"label_obs_save_str must be a string"), - ("", ValueError ,"label_obs_save_str cannot be an empty string") -]) -def test_init_invalid_label_type(sample_adata, invalid_label, expected_exception, match_pattern): + +@pytest.mark.parametrize( + "invalid_label, expected_exception,match_pattern", + [ + (None, TypeError, "label_obs_save_str must be a string"), + (123, TypeError, "label_obs_save_str must be a string"), + ([], TypeError, "label_obs_save_str must be a string"), + ("", ValueError, "label_obs_save_str cannot be an empty string"), + ], +) +def test_init_invalid_label_type( + sample_adata, invalid_label, expected_exception, match_pattern +): """Tests TypeError for invalid label_obs_save_str types.""" with pytest.raises(expected_exception, match=match_pattern): GMMThresholding( - adata=sample_adata, - feature='gene1', - label_obs_save_str=invalid_label + adata=sample_adata, feature="gene1", label_obs_save_str=invalid_label ) + def test_init_existing_obs_label(sample_adata): """Tests KeyError when the label_obs_save_str already exists in adata.obs.""" adata_copy = sample_adata.copy() - label_name = 'existing_labels' - adata_copy.obs[label_name] = 'some_value' # Add the column - existing_obs_label_adata = create_modified_adata(sample_adata, add_obs={label_name: np.repeat(0, len(sample_adata))}) # Ensure the original sample_adata is unchanged - - with pytest.raises(KeyError, match=f"obs key '{label_name}' already exists in the AnnData object. Please choose a different label."): + label_name = "existing_labels" + adata_copy.obs[label_name] = "some_value" # Add the column + existing_obs_label_adata = create_modified_adata( + sample_adata, add_obs={label_name: np.repeat(0, len(sample_adata))} + ) # Ensure the original sample_adata is unchanged + + with pytest.raises( + KeyError, + match=f"obs key '{label_name}' already exists in the AnnData object. Please choose a different label.", + ): GMMThresholding( adata=existing_obs_label_adata, - feature='gene1', - label_obs_save_str=label_name + feature="gene1", + label_obs_save_str=label_name, ) -@pytest.mark.parametrize("invalid_kwargs", [ - "not_a_dict", - 123, - ["list", "is", "not", "dict"] -]) + +@pytest.mark.parametrize( + "invalid_kwargs", ["not_a_dict", 123, ["list", "is", "not", "dict"]] +) def test_init_invalid_gmm_kwargs_type(sample_adata, invalid_kwargs): """Tests TypeError when gmm_kwargs is provided but is not a dictionary.""" # Match the updated error message in __init__ with pytest.raises(TypeError, match="gmm_kwargs must be a dictionary or None"): GMMThresholding( adata=sample_adata, - feature='gene1', - label_obs_save_str='labels', - gmm_kwargs=invalid_kwargs + feature="gene1", + label_obs_save_str="labels", + gmm_kwargs=invalid_kwargs, ) diff --git a/tests/thresholding/test_integration.py b/tests/thresholding/test_integration.py index 1b274e2..776b4a6 100644 --- a/tests/thresholding/test_integration.py +++ b/tests/thresholding/test_integration.py @@ -7,8 +7,6 @@ Priority 3 - To be implemented after Priority 1 & 2 tests pass. """ -import pytest -from src.cc_mapping.thresholding import GMMThresholding # TODO: Add tests for: # - Complete workflow: fit → automatic categorize → plot diff --git a/tests/thresholding/test_label_collapsing.py b/tests/thresholding/test_label_collapsing.py index c5a9db0..8148ffa 100644 --- a/tests/thresholding/test_label_collapsing.py +++ b/tests/thresholding/test_label_collapsing.py @@ -5,9 +5,8 @@ where many components provide adaptive boundaries but fewer categories are desired. """ -import pytest import numpy as np - +import pytest ### Basic Label Collapsing Tests ### @@ -23,9 +22,9 @@ def test_collapse_to_binary_automatic_thresholding(sample_gmm_thresholding_insta ) # Should use automatic thresholding - assert ( - not gmm._manual_decision_boundaries - ), "Should use automatic thresholding when manual_thresholds not provided" + assert not gmm._manual_decision_boundaries, ( + "Should use automatic thresholding when manual_thresholds not provided" + ) # Should have 1 threshold (2 categories) thresholds = gmm.return_thresholds() @@ -69,14 +68,14 @@ def test_collapse_to_binary_manual_thresholding(sample_gmm_thresholding_instance ) # Should use manual thresholding - assert ( - gmm._manual_decision_boundaries - ), "Should use manual thresholding when manual_thresholds provided" + assert gmm._manual_decision_boundaries, ( + "Should use manual thresholding when manual_thresholds provided" + ) # Should use the exact threshold provided - assert ( - gmm.return_thresholds() == manual_threshold - ), "Should use the provided manual threshold" + assert gmm.return_thresholds() == manual_threshold, ( + "Should use the provided manual threshold" + ) # Verify labels unique_labels = set(gmm.adata.obs["labels"].unique()) @@ -124,12 +123,12 @@ def test_collapse_many_to_few(sample_gmm_thresholding_instance): assert len(gmm.return_thresholds()) == 1, "Should have 1 threshold for 2 categories" # Verify condensed probabilities were created - assert ( - gmm._internal_data.gmm_info.condensed_data_probs is not None - ), "Condensed probabilities should be created" - assert ( - gmm._internal_data.gmm_info.condensed_data_probs.shape[1] == 2 - ), "Condensed probabilities should have 2 columns for 2 categories" + assert gmm._internal_data.gmm_info.condensed_data_probs is not None, ( + "Condensed probabilities should be created" + ) + assert gmm._internal_data.gmm_info.condensed_data_probs.shape[1] == 2, ( + "Condensed probabilities should have 2 columns for 2 categories" + ) # Verify labels unique_labels = set(gmm.adata.obs["labels"].unique()) @@ -162,9 +161,9 @@ def test_collapse_preserves_n_components(sample_gmm_thresholding_instance): ) # n_components should still be 8, not changed to 2 - assert ( - gmm._gmm_info.n_components == n_components - ), f"n_components should remain {n_components} after collapsing, not be mutated" + assert gmm._gmm_info.n_components == n_components, ( + f"n_components should remain {n_components} after collapsing, not be mutated" + ) def test_collapse_creates_condensed_probabilities(sample_gmm_thresholding_instance): @@ -214,9 +213,9 @@ def test_collapse_with_sequential_groups(sample_gmm_thresholding_instance): }, "Should handle sequential duplicate patterns" # Should have 1 threshold - assert ( - len(gmm.return_thresholds()) == 1 - ), "Should have 1 threshold for 2 unique labels" + assert len(gmm.return_thresholds()) == 1, ( + "Should have 1 threshold for 2 unique labels" + ) def test_collapse_rejects_non_contiguous_groups(sample_gmm_thresholding_instance): @@ -277,9 +276,9 @@ def test_collapse_with_all_same_label(sample_gmm_thresholding_instance): # Should have 0 thresholds (only 1 category) thresholds = gmm.return_thresholds() - assert ( - len(thresholds) == 0 - ), "Should have no thresholds when all components map to one category" + assert len(thresholds) == 0, ( + "Should have no thresholds when all components map to one category" + ) # All samples should get same label unique_labels = set(gmm.adata.obs["labels"].unique()) diff --git a/tests/thresholding/test_manual_thresholds.py b/tests/thresholding/test_manual_thresholds.py index c0c904d..94a0cf2 100644 --- a/tests/thresholding/test_manual_thresholds.py +++ b/tests/thresholding/test_manual_thresholds.py @@ -5,9 +5,8 @@ thresholding across different datasets or experimental conditions. """ -import pytest import numpy as np - +import pytest ### Basic Manual Threshold Tests ### @@ -23,14 +22,14 @@ def test_manual_thresholds_override_gmm(sample_gmm_thresholding_instance): ) # Should use manual thresholding - assert ( - gmm._manual_decision_boundaries - ), "Should indicate manual thresholding is active" + assert gmm._manual_decision_boundaries, ( + "Should indicate manual thresholding is active" + ) # Should return exact threshold provided - assert ( - gmm.return_thresholds() == manual_threshold - ), "Should return the exact manual threshold provided, not GMM-derived" + assert gmm.return_thresholds() == manual_threshold, ( + "Should return the exact manual threshold provided, not GMM-derived" + ) def test_manual_thresholds_set_flag(sample_gmm_thresholding_instance): @@ -40,17 +39,17 @@ def test_manual_thresholds_set_flag(sample_gmm_thresholding_instance): # Without manual thresholds gmm.categorize_samples(ordered_labels=["Low", "Medium", "High"]) - assert ( - not gmm._manual_decision_boundaries - ), "Should be False when using automatic thresholding" + assert not gmm._manual_decision_boundaries, ( + "Should be False when using automatic thresholding" + ) # With manual thresholds gmm.categorize_samples( ordered_labels=["Low", "Medium", "High"], manual_thresholds=[1.0, 2.0] ) - assert ( - gmm._manual_decision_boundaries - ), "Should be True when using manual thresholding" + assert gmm._manual_decision_boundaries, ( + "Should be True when using manual thresholding" + ) def test_manual_thresholds_with_collapsed_labels(sample_gmm_thresholding_instance): @@ -99,9 +98,9 @@ def test_manual_thresholds_multiple_thresholds(sample_gmm_thresholding_instance) assert gmm._manual_decision_boundaries, "Should use manual thresholding" # Should return both thresholds - assert ( - gmm.return_thresholds() == manual_thresholds - ), "Should return both manual thresholds in order" + assert gmm.return_thresholds() == manual_thresholds, ( + "Should return both manual thresholds in order" + ) # Verify all three labels are assigned unique_labels = set(gmm.adata.obs["labels"].unique()) @@ -196,9 +195,9 @@ def test_manual_threshold_assigns_correctly(sample_gmm_thresholding_instance): # With threshold at 0.0, there should be clear separation # All low values should be <= threshold, all high values should be > threshold # (or vice versa depending on sorting) - assert ( - np.max(low_values) <= threshold or np.min(high_values) > threshold - ), "Manual threshold should create separation at the specified value" + assert np.max(low_values) <= threshold or np.min(high_values) > threshold, ( + "Manual threshold should create separation at the specified value" + ) def test_manual_threshold_with_extreme_value(sample_gmm_thresholding_instance): @@ -218,9 +217,9 @@ def test_manual_threshold_with_extreme_value(sample_gmm_thresholding_instance): high_count = (labels == "High").sum() # With threshold way above data range, expect most samples in 'Low' - assert ( - low_count > high_count - ), "Extreme high threshold should assign most samples to first category" + assert low_count > high_count, ( + "Extreme high threshold should assign most samples to first category" + ) def test_manual_threshold_ordering(sample_gmm_thresholding_instance): @@ -270,9 +269,9 @@ def test_manual_then_automatic_thresholding(sample_gmm_thresholding_instance): automatic_threshold = gmm.return_thresholds() # Automatic threshold should be different from manual - assert ( - automatic_threshold != manual_threshold - ), "Automatic thresholding should produce different threshold than manual" + assert automatic_threshold != manual_threshold, ( + "Automatic thresholding should produce different threshold than manual" + ) def test_manual_threshold_consistency_across_calls(sample_gmm_thresholding_instance): @@ -290,9 +289,9 @@ def test_manual_threshold_consistency_across_calls(sample_gmm_thresholding_insta second_labels = gmm.adata.obs["labels"].copy() # Should get identical results - assert ( - first_labels == second_labels - ).all(), "Manual threshold should produce consistent results across calls" + assert (first_labels == second_labels).all(), ( + "Manual threshold should produce consistent results across calls" + ) def test_manual_threshold_with_single_category(sample_gmm_thresholding_instance): diff --git a/tests/thresholding/test_overlapping_components.py b/tests/thresholding/test_overlapping_components.py index da07f5b..d0b2497 100644 --- a/tests/thresholding/test_overlapping_components.py +++ b/tests/thresholding/test_overlapping_components.py @@ -4,9 +4,10 @@ causing the condensed probabilities to flip-flop and create multiple transitions. """ -import pytest -import numpy as np import anndata as ad +import numpy as np +import pytest + from src.cc_mapping.thresholding import GMMThresholding @@ -146,12 +147,12 @@ def test_no_warning_for_clean_separation(): or "overlapping" in str(w.message).lower() ) ] - assert ( - len(threshold_warnings) == 0 - ), f"Should not warn for clean separated data, but got: {[str(w.message) for w in threshold_warnings]}" + assert len(threshold_warnings) == 0, ( + f"Should not warn for clean separated data, but got: {[str(w.message) for w in threshold_warnings]}" + ) # Should have exactly 1 threshold for 2 unique labels thresholds = gmm.return_thresholds() - assert ( - len(thresholds) == 1 - ), f"Expected 1 threshold for clean data, got {len(thresholds)}" + assert len(thresholds) == 1, ( + f"Expected 1 threshold for clean data, got {len(thresholds)}" + ) diff --git a/tests/thresholding/test_plotting.py b/tests/thresholding/test_plotting.py index 16cb6f3..f172907 100644 --- a/tests/thresholding/test_plotting.py +++ b/tests/thresholding/test_plotting.py @@ -7,8 +7,6 @@ Priority 2 - To be implemented after Priority 1 tests pass. """ -import pytest -from src.cc_mapping.thresholding import GMMThresholding # TODO: Add tests for: # - _plot_vertical_linear_decision_boundaries color count diff --git a/tests/thresholding/test_sequential.py b/tests/thresholding/test_sequential.py index 80f8af3..fcce75f 100644 --- a/tests/thresholding/test_sequential.py +++ b/tests/thresholding/test_sequential.py @@ -10,72 +10,73 @@ - Plotting methods """ -import pytest +from collections import OrderedDict + +import anndata as ad import numpy as np import pandas as pd -import anndata as ad -from collections import OrderedDict +import pytest from cc_mapping.thresholding import SequentialGMM - # ===================== # Fixtures # ===================== + @pytest.fixture def basic_adata(): """Create basic AnnData object for testing.""" np.random.seed(42) n_obs = 100 n_vars = 3 - + X = np.random.randn(n_obs, n_vars) obs = pd.DataFrame({"cell_id": [f"cell_{i}" for i in range(n_obs)]}) var = pd.DataFrame(index=["DNA", "Plk1", "CyclinB"]) - + return ad.AnnData(X=X, obs=obs, var=var) @pytest.fixture def adata_with_bimodal_dist(): """Create AnnData with clear bimodal distribution for testing. - + This creates a hierarchical structure: - DNA separates into Low (G0) and High (S) populations - Within Low population, Plk1 separates into G0_low and G0_high - Within High population, Plk1 separates into S_low and S_high """ np.random.seed(42) - + # Create 4 populations with well-separated bimodal distributions # Population 1: DNA Low + Plk1 Low (50 cells) pop1_dna = np.random.normal(0, 0.3, 50) pop1_plk1 = np.random.normal(-2, 0.3, 50) - + # Population 2: DNA Low + Plk1 High (50 cells) pop2_dna = np.random.normal(0, 0.3, 50) pop2_plk1 = np.random.normal(1, 0.3, 50) - + # Population 3: DNA High + Plk1 Low (50 cells) pop3_dna = np.random.normal(5, 0.3, 50) pop3_plk1 = np.random.normal(-2, 0.3, 50) - + # Population 4: DNA High + Plk1 High (50 cells) pop4_dna = np.random.normal(5, 0.3, 50) pop4_plk1 = np.random.normal(1, 0.3, 50) - + # Combine all populations dna = np.concatenate([pop1_dna, pop2_dna, pop3_dna, pop4_dna]) plk1 = np.concatenate([pop1_plk1, pop2_plk1, pop3_plk1, pop4_plk1]) - + # Third feature (not used for thresholding in most tests) cyclin = np.random.randn(200) - + X = np.column_stack([dna, plk1, cyclin]) obs = pd.DataFrame({"cell_id": [f"cell_{i}" for i in range(200)]}) var = pd.DataFrame(index=["DNA", "Plk1", "CyclinB"]) - + return ad.AnnData(X=X, obs=obs, var=var) @@ -83,187 +84,192 @@ def adata_with_bimodal_dist(): # Initialization Tests # ===================== + class TestInitialization: """Test SequentialGMM initialization.""" - + def test_init_success_defaults(self, basic_adata): """Test successful initialization with default parameters.""" seq_gmm = SequentialGMM(adata=basic_adata) - + assert seq_gmm.adata is not None assert seq_gmm.adata.shape == basic_adata.shape - assert seq_gmm.thresholding_events_key == 'sequential_gmm_thresholding_events' + assert seq_gmm.thresholding_events_key == "sequential_gmm_thresholding_events" assert seq_gmm.random_state == 42 assert seq_gmm.gmm_kwargs == {} - assert 'sequential_gmm_thresholding_events' in seq_gmm.adata.uns - assert isinstance(seq_gmm.adata.uns['sequential_gmm_thresholding_events'], OrderedDict) - + assert "sequential_gmm_thresholding_events" in seq_gmm.adata.uns + assert isinstance( + seq_gmm.adata.uns["sequential_gmm_thresholding_events"], OrderedDict + ) + def test_init_success_custom_key(self, basic_adata): """Test initialization with custom .uns key.""" seq_gmm = SequentialGMM( - adata=basic_adata, - thresholding_events_key='my_custom_key' + adata=basic_adata, thresholding_events_key="my_custom_key" ) - - assert seq_gmm.thresholding_events_key == 'my_custom_key' - assert 'my_custom_key' in seq_gmm.adata.uns - assert isinstance(seq_gmm.adata.uns['my_custom_key'], OrderedDict) - + + assert seq_gmm.thresholding_events_key == "my_custom_key" + assert "my_custom_key" in seq_gmm.adata.uns + assert isinstance(seq_gmm.adata.uns["my_custom_key"], OrderedDict) + def test_init_success_custom_gmm_kwargs(self, basic_adata): """Test initialization with custom GMM kwargs.""" - custom_kwargs = {'covariance_type': 'diag', 'max_iter': 200} - seq_gmm = SequentialGMM( - adata=basic_adata, - gmm_kwargs=custom_kwargs - ) - + custom_kwargs = {"covariance_type": "diag", "max_iter": 200} + seq_gmm = SequentialGMM(adata=basic_adata, gmm_kwargs=custom_kwargs) + assert seq_gmm.gmm_kwargs == custom_kwargs - + def test_init_success_existing_uns_key(self, basic_adata): """Test initialization with existing .uns key (should not error).""" - basic_adata.uns['sequential_gmm_thresholding_events'] = OrderedDict() - + basic_adata.uns["sequential_gmm_thresholding_events"] = OrderedDict() + seq_gmm = SequentialGMM(adata=basic_adata) - - assert 'sequential_gmm_thresholding_events' in seq_gmm.adata.uns - + + assert "sequential_gmm_thresholding_events" in seq_gmm.adata.uns + def test_init_error_not_anndata(self): """Test initialization fails with non-AnnData object.""" with pytest.raises(TypeError, match="adata must be an AnnData.AnnData object"): SequentialGMM(adata="not_anndata") - + def test_init_error_invalid_thresholding_key_type(self, basic_adata): """Test initialization fails with non-string thresholding_events_key.""" with pytest.raises(TypeError, match="thresholding_events_key must be a string"): - SequentialGMM( - adata=basic_adata, - thresholding_events_key=123 - ) - + SequentialGMM(adata=basic_adata, thresholding_events_key=123) + def test_init_error_empty_thresholding_key(self, basic_adata): """Test initialization fails with empty thresholding_events_key.""" - with pytest.raises(ValueError, match="thresholding_events_key cannot be an empty string"): - SequentialGMM( - adata=basic_adata, - thresholding_events_key="" - ) - + with pytest.raises( + ValueError, match="thresholding_events_key cannot be an empty string" + ): + SequentialGMM(adata=basic_adata, thresholding_events_key="") + def test_init_error_invalid_uns_key_type(self, basic_adata): """Test initialization fails when existing .uns key is not OrderedDict.""" - basic_adata.uns['sequential_gmm_thresholding_events'] = {} # dict, not OrderedDict - + basic_adata.uns[ + "sequential_gmm_thresholding_events" + ] = {} # dict, not OrderedDict + with pytest.raises(TypeError, match="must be an OrderedDict"): SequentialGMM(adata=basic_adata) - + def test_init_error_invalid_gmm_kwargs_type(self, basic_adata): """Test initialization fails with non-dict gmm_kwargs.""" with pytest.raises(TypeError, match="gmm_kwargs must be a dictionary"): - SequentialGMM( - adata=basic_adata, - gmm_kwargs="not_a_dict" - ) + SequentialGMM(adata=basic_adata, gmm_kwargs="not_a_dict") # ===================== # threshold_entire_dataset Tests # ===================== + class TestThresholdEntireDataset: """Test threshold_entire_dataset() method.""" - + def test_threshold_entire_dataset_success(self, adata_with_bimodal_dist): """Test successful thresholding of entire dataset.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['Low', 'High'], - operation_name='DNA_threshold' + ordered_labels=["Low", "High"], + operation_name="DNA_threshold", ) - + # Check obs column created - assert 'cell_cycle' in seq_gmm.adata.obs.columns - assert set(seq_gmm.adata.obs['cell_cycle'].unique()) == {'Low', 'High'} - + assert "cell_cycle" in seq_gmm.adata.obs.columns + assert set(seq_gmm.adata.obs["cell_cycle"].unique()) == {"Low", "High"} + # Check operation stored - assert 'DNA_threshold' in seq_gmm.adata.uns['sequential_gmm_thresholding_events'] - + assert ( + "DNA_threshold" in seq_gmm.adata.uns["sequential_gmm_thresholding_events"] + ) + # Check metadata - op_data = seq_gmm.adata.uns['sequential_gmm_thresholding_events']['DNA_threshold'] - assert op_data['operation_type'] == 'standard' - assert op_data['parent_operation'] is None - assert op_data['refined_from_labels'] is None - assert op_data['feature_name'] == 'DNA' - assert op_data['gmm_obs_label'] == 'cell_cycle' - - def test_threshold_entire_dataset_with_manual_thresholds(self, adata_with_bimodal_dist): + op_data = seq_gmm.adata.uns["sequential_gmm_thresholding_events"][ + "DNA_threshold" + ] + assert op_data["operation_type"] == "standard" + assert op_data["parent_operation"] is None + assert op_data["refined_from_labels"] is None + assert op_data["feature_name"] == "DNA" + assert op_data["gmm_obs_label"] == "cell_cycle" + + def test_threshold_entire_dataset_with_manual_thresholds( + self, adata_with_bimodal_dist + ): """Test thresholding with manual thresholds.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['Low', 'High'], + ordered_labels=["Low", "High"], manual_thresholds=[1.5], - operation_name='DNA_manual' + operation_name="DNA_manual", ) - - assert 'cell_cycle' in seq_gmm.adata.obs.columns - assert 'DNA_manual' in seq_gmm.adata.uns['sequential_gmm_thresholding_events'] - - def test_threshold_entire_dataset_with_duplicate_labels(self, adata_with_bimodal_dist): + + assert "cell_cycle" in seq_gmm.adata.obs.columns + assert "DNA_manual" in seq_gmm.adata.uns["sequential_gmm_thresholding_events"] + + def test_threshold_entire_dataset_with_duplicate_labels( + self, adata_with_bimodal_dist + ): """Test thresholding with duplicate labels (label collapsing).""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=4, - ordered_labels=['Low', 'Low', 'High', 'High'], + ordered_labels=["Low", "Low", "High", "High"], duplicate_labels=True, - operation_name='DNA_collapsed' + operation_name="DNA_collapsed", ) - + # Should only have 2 final labels despite 4 components - assert set(seq_gmm.adata.obs['cell_cycle'].unique()) == {'Low', 'High'} - + assert set(seq_gmm.adata.obs["cell_cycle"].unique()) == {"Low", "High"} + def test_threshold_entire_dataset_error_no_operation_name(self, basic_adata): """Test error when operation_name is None.""" seq_gmm = SequentialGMM(adata=basic_adata) - + with pytest.raises(ValueError, match="operation_name is required"): seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['Low', 'High'], - operation_name=None + ordered_labels=["Low", "High"], + operation_name=None, ) - - def test_threshold_entire_dataset_error_duplicate_operation_name(self, adata_with_bimodal_dist): + + def test_threshold_entire_dataset_error_duplicate_operation_name( + self, adata_with_bimodal_dist + ): """Test error when operation_name already exists.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + # First operation seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['Low', 'High'], - operation_name='DNA_threshold' + ordered_labels=["Low", "High"], + operation_name="DNA_threshold", ) - + # Try to use same name again with pytest.raises(KeyError, match="already exists"): seq_gmm.threshold_entire_dataset( - feature='Plk1', - label_obs_save_str='plk1_level', + feature="Plk1", + label_obs_save_str="plk1_level", n_components=2, - ordered_labels=['Low', 'High'], - operation_name='DNA_threshold' # Same name + ordered_labels=["Low", "High"], + operation_name="DNA_threshold", # Same name ) @@ -271,106 +277,111 @@ def test_threshold_entire_dataset_error_duplicate_operation_name(self, adata_wit # refine_labels_with_gmm Tests # ===================== + class TestRefineLabelsWithGMM: """Test refine_labels_with_gmm() method.""" - + def test_refine_labels_success(self, adata_with_bimodal_dist): """Test successful label refinement.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + # Initial thresholding seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['G0', 'S'], - operation_name='DNA_threshold' + ordered_labels=["G0", "S"], + operation_name="DNA_threshold", ) - + # Refine G0 cells seq_gmm.refine_labels_with_gmm( - feature='Plk1', - obs_label='cell_cycle', - value_to_refine='G0', + feature="Plk1", + obs_label="cell_cycle", + value_to_refine="G0", n_components=2, - ordered_labels=['G0_low', 'G0_high'], - operation_name='Plk1_refinement' + ordered_labels=["G0_low", "G0_high"], + operation_name="Plk1_refinement", ) - + # Check labels updated - assert 'G0_low' in seq_gmm.adata.obs['cell_cycle'].values - assert 'G0_high' in seq_gmm.adata.obs['cell_cycle'].values + assert "G0_low" in seq_gmm.adata.obs["cell_cycle"].values + assert "G0_high" in seq_gmm.adata.obs["cell_cycle"].values # Original 'G0' should be replaced # (though some cells might still have 'S') - + # Check operation stored - assert 'Plk1_refinement' in seq_gmm.adata.uns['sequential_gmm_thresholding_events'] - + assert ( + "Plk1_refinement" in seq_gmm.adata.uns["sequential_gmm_thresholding_events"] + ) + # Check metadata - op_data = seq_gmm.adata.uns['sequential_gmm_thresholding_events']['Plk1_refinement'] - assert op_data['operation_type'] == 'refinement' - assert op_data['parent_operation'] == 'cell_cycle' - assert op_data['refined_from_labels'] == ['G0'] - assert op_data['feature_name'] == 'Plk1' - + op_data = seq_gmm.adata.uns["sequential_gmm_thresholding_events"][ + "Plk1_refinement" + ] + assert op_data["operation_type"] == "refinement" + assert op_data["parent_operation"] == "cell_cycle" + assert op_data["refined_from_labels"] == ["G0"] + assert op_data["feature_name"] == "Plk1" + def test_refine_labels_error_no_operation_name(self, adata_with_bimodal_dist): """Test error when operation_name is None.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + # Create initial labels seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['G0', 'S'], - operation_name='DNA_threshold' + ordered_labels=["G0", "S"], + operation_name="DNA_threshold", ) - + with pytest.raises(ValueError, match="operation_name is required"): seq_gmm.refine_labels_with_gmm( - feature='Plk1', - obs_label='cell_cycle', - value_to_refine='G0', + feature="Plk1", + obs_label="cell_cycle", + value_to_refine="G0", n_components=2, - ordered_labels=['G0_low', 'G0_high'], - operation_name=None + ordered_labels=["G0_low", "G0_high"], + operation_name=None, ) - + def test_refine_labels_error_obs_label_not_found(self, basic_adata): """Test error when obs_label doesn't exist.""" seq_gmm = SequentialGMM(adata=basic_adata) - + with pytest.raises(KeyError, match="not found in adata.obs"): seq_gmm.refine_labels_with_gmm( - feature='Plk1', - obs_label='nonexistent_column', - value_to_refine='G0', + feature="Plk1", + obs_label="nonexistent_column", + value_to_refine="G0", n_components=2, - ordered_labels=['G0_low', 'G0_high'], - operation_name='Plk1_refinement' + ordered_labels=["G0_low", "G0_high"], + operation_name="Plk1_refinement", ) - + def test_refine_labels_error_value_not_found(self, adata_with_bimodal_dist): """Test error when value_to_refine doesn't exist.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + # Create initial labels seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['G0', 'S'], - operation_name='DNA_threshold' + ordered_labels=["G0", "S"], + operation_name="DNA_threshold", ) - + with pytest.raises(ValueError, match="not found in adata.obs"): seq_gmm.refine_labels_with_gmm( - feature='Plk1', - obs_label='cell_cycle', - value_to_refine='G2M', # Doesn't exist + feature="Plk1", + obs_label="cell_cycle", + value_to_refine="G2M", # Doesn't exist n_components=2, - ordered_labels=['G2M_low', 'G2M_high'], - operation_name='Plk1_refinement' + ordered_labels=["G2M_low", "G2M_high"], + operation_name="Plk1_refinement", ) @@ -378,67 +389,68 @@ def test_refine_labels_error_value_not_found(self, adata_with_bimodal_dist): # refine_labels_with_manual_thresholds Tests # ===================== + class TestRefineLabelsWithManualThresholds: """Test refine_labels_with_manual_thresholds() method.""" - + def test_refine_manual_success(self, adata_with_bimodal_dist): """Test successful manual threshold refinement.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + # Initial thresholding seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['G0', 'S'], - operation_name='DNA_threshold' + ordered_labels=["G0", "S"], + operation_name="DNA_threshold", ) - + # Refine with manual thresholds # G0 cells have Plk1 ~N(-1, 0.3), so use -1 as threshold to split them seq_gmm.refine_labels_with_manual_thresholds( - feature='Plk1', - obs_label='cell_cycle', - value_to_refine='G0', + feature="Plk1", + obs_label="cell_cycle", + value_to_refine="G0", manual_thresholds=[-1.0], - ordered_labels=['G0_low', 'G0_high'], - operation_name='Plk1_manual' + ordered_labels=["G0_low", "G0_high"], + operation_name="Plk1_manual", ) - + # Check labels updated - assert 'G0_low' in seq_gmm.adata.obs['cell_cycle'].values - assert 'G0_high' in seq_gmm.adata.obs['cell_cycle'].values - + assert "G0_low" in seq_gmm.adata.obs["cell_cycle"].values + assert "G0_high" in seq_gmm.adata.obs["cell_cycle"].values + # Check operation stored - assert 'Plk1_manual' in seq_gmm.adata.uns['sequential_gmm_thresholding_events'] - + assert "Plk1_manual" in seq_gmm.adata.uns["sequential_gmm_thresholding_events"] + # Check metadata (should not have GMM info) - op_data = seq_gmm.adata.uns['sequential_gmm_thresholding_events']['Plk1_manual'] - assert op_data['operation_type'] == 'refinement_manual' - assert op_data['gmm_info'] is None - assert op_data['decision_boundaries']['thresholds'] == [-1.0] - + op_data = seq_gmm.adata.uns["sequential_gmm_thresholding_events"]["Plk1_manual"] + assert op_data["operation_type"] == "refinement_manual" + assert op_data["gmm_info"] is None + assert op_data["decision_boundaries"]["thresholds"] == [-1.0] + def test_refine_manual_error_wrong_threshold_count(self, adata_with_bimodal_dist): """Test error when threshold count doesn't match labels.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + # Initial thresholding seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['G0', 'S'], - operation_name='DNA_threshold' + ordered_labels=["G0", "S"], + operation_name="DNA_threshold", ) - + with pytest.raises(ValueError, match="Number of thresholds"): seq_gmm.refine_labels_with_manual_thresholds( - feature='Plk1', - obs_label='cell_cycle', - value_to_refine='G0', + feature="Plk1", + obs_label="cell_cycle", + value_to_refine="G0", manual_thresholds=[0.5, 1.5], # 2 thresholds for 2 labels (should be 1) - ordered_labels=['G0_low', 'G0_high'], - operation_name='Plk1_manual' + ordered_labels=["G0_low", "G0_high"], + operation_name="Plk1_manual", ) @@ -446,93 +458,103 @@ def test_refine_manual_error_wrong_threshold_count(self, adata_with_bimodal_dist # return_adata Tests # ===================== + class TestReturnAdata: """Test return_adata() method.""" - + def test_return_adata(self, adata_with_bimodal_dist): """Test return_adata returns modified object.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['G0', 'S'], - operation_name='DNA_threshold' + ordered_labels=["G0", "S"], + operation_name="DNA_threshold", ) - + adata_result = seq_gmm.return_adata() - + assert isinstance(adata_result, ad.AnnData) - assert 'cell_cycle' in adata_result.obs.columns - assert 'DNA_threshold' in adata_result.uns['sequential_gmm_thresholding_events'] + assert "cell_cycle" in adata_result.obs.columns + assert "DNA_threshold" in adata_result.uns["sequential_gmm_thresholding_events"] # ===================== # Integration Tests # ===================== + class TestIntegration: """Integration tests for complete workflows.""" - + def test_full_sequential_workflow(self, adata_with_bimodal_dist): """Test complete sequential thresholding workflow.""" seq_gmm = SequentialGMM(adata=adata_with_bimodal_dist) - + # Step 1: Initial thresholding on DNA seq_gmm.threshold_entire_dataset( - feature='DNA', - label_obs_save_str='cell_cycle', + feature="DNA", + label_obs_save_str="cell_cycle", n_components=2, - ordered_labels=['Low', 'High'], - operation_name='DNA_initial' + ordered_labels=["Low", "High"], + operation_name="DNA_initial", ) - + # Step 2: Refine 'Low' cells with Plk1 seq_gmm.refine_labels_with_gmm( - feature='Plk1', - obs_label='cell_cycle', - value_to_refine='Low', + feature="Plk1", + obs_label="cell_cycle", + value_to_refine="Low", n_components=2, - ordered_labels=['G0', 'G1'], - operation_name='Plk1_low_refinement' + ordered_labels=["G0", "G1"], + operation_name="Plk1_low_refinement", ) - + # Step 3: Refine 'High' cells with CyclinB seq_gmm.refine_labels_with_gmm( - feature='CyclinB', - obs_label='cell_cycle', - value_to_refine='High', + feature="CyclinB", + obs_label="cell_cycle", + value_to_refine="High", n_components=2, - ordered_labels=['S', 'G2M'], - operation_name='CyclinB_high_refinement' + ordered_labels=["S", "G2M"], + operation_name="CyclinB_high_refinement", ) - + # Step 4: Refine G2M with manual threshold seq_gmm.refine_labels_with_manual_thresholds( - feature='DNA', - obs_label='cell_cycle', - value_to_refine='G2M', + feature="DNA", + obs_label="cell_cycle", + value_to_refine="G2M", manual_thresholds=[2.5], - ordered_labels=['G2', 'M'], - operation_name='DNA_G2M_manual' + ordered_labels=["G2", "M"], + operation_name="DNA_G2M_manual", ) - + # Get result adata_result = seq_gmm.return_adata() - + # Check all operations stored - assert len(adata_result.uns['sequential_gmm_thresholding_events']) == 4 - assert 'DNA_initial' in adata_result.uns['sequential_gmm_thresholding_events'] - assert 'Plk1_low_refinement' in adata_result.uns['sequential_gmm_thresholding_events'] - assert 'CyclinB_high_refinement' in adata_result.uns['sequential_gmm_thresholding_events'] - assert 'DNA_G2M_manual' in adata_result.uns['sequential_gmm_thresholding_events'] - + assert len(adata_result.uns["sequential_gmm_thresholding_events"]) == 4 + assert "DNA_initial" in adata_result.uns["sequential_gmm_thresholding_events"] + assert ( + "Plk1_low_refinement" + in adata_result.uns["sequential_gmm_thresholding_events"] + ) + assert ( + "CyclinB_high_refinement" + in adata_result.uns["sequential_gmm_thresholding_events"] + ) + assert ( + "DNA_G2M_manual" in adata_result.uns["sequential_gmm_thresholding_events"] + ) + # Check final labels exist - unique_labels = set(adata_result.obs['cell_cycle'].unique()) - expected_labels = {'G0', 'G1', 'S', 'G2', 'M'} + unique_labels = set(adata_result.obs["cell_cycle"].unique()) + expected_labels = {"G0", "G1", "S", "G2", "M"} assert expected_labels.issubset(unique_labels) or len(unique_labels) > 0 - + print("Full workflow test passed!") print(f"Final unique labels: {unique_labels}") print(f"Label counts: {adata_result.obs['cell_cycle'].value_counts()}") diff --git a/tests/thresholding/test_utils.py b/tests/thresholding/test_utils.py index 576f560..c2aa744 100644 --- a/tests/thresholding/test_utils.py +++ b/tests/thresholding/test_utils.py @@ -7,28 +7,26 @@ - Error handling and validation """ +import anndata as ad import numpy as np import pandas as pd import pytest -from collections import OrderedDict - -import anndata as ad -from cc_mapping.utils import create_boolean_label_combination from cc_mapping.thresholding import GMMThresholding +from cc_mapping.utils import create_boolean_label_combination @pytest.fixture def adata_with_labels(): """Create test AnnData with two categorical labels.""" adata = ad.AnnData(X=np.random.randn(100, 10)) - + # Create two categorical labels - adata.obs['treatment'] = pd.Categorical(['control'] * 50 + ['drug'] * 50) - adata.obs['cell_cycle'] = pd.Categorical( - ['G0'] * 25 + ['G1'] * 25 + ['S'] * 25 + ['G2'] * 25 + adata.obs["treatment"] = pd.Categorical(["control"] * 50 + ["drug"] * 50) + adata.obs["cell_cycle"] = pd.Categorical( + ["G0"] * 25 + ["G1"] * 25 + ["S"] * 25 + ["G2"] * 25 ) - + return adata @@ -38,392 +36,412 @@ def gmm_with_thresholding(sample_adata): # Use the sample_adata and perform thresholding gmm = GMMThresholding( adata=sample_adata, - feature='gene1', - label_obs_save_str='cell_cycle', - thresholding_events_key='gmm_thresholding_events' + feature="gene1", + label_obs_save_str="cell_cycle", + thresholding_events_key="gmm_thresholding_events", ) gmm.fit(n_components=2) - gmm.categorize_samples(ordered_labels=['Low', 'High']) + gmm.categorize_samples(ordered_labels=["Low", "High"]) gmm.return_adata() # This saves the operation to uns - + return gmm # ===== Tests for create_boolean_label_combination ===== + class TestCreateBooleanLabelCombination: """Tests for create_boolean_label_combination function.""" - + def test_and_operator(self, adata_with_labels): """Test AND operator combines labels correctly.""" adata = create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1=['control'], - obs_key_2='cell_cycle', - match_values_2=['G0'], - operator='AND', - output_obs_key='control_G0', - true_label='yes', - false_label='no', + obs_key_1="treatment", + match_values_1=["control"], + obs_key_2="cell_cycle", + match_values_2=["G0"], + operator="AND", + output_obs_key="control_G0", + true_label="yes", + false_label="no", ) - + # Check output exists - assert 'control_G0' in adata.obs.columns - + assert "control_G0" in adata.obs.columns + # Check correct cells are labeled - control_mask = adata.obs['treatment'] == 'control' - g0_mask = adata.obs['cell_cycle'] == 'G0' + control_mask = adata.obs["treatment"] == "control" + g0_mask = adata.obs["cell_cycle"] == "G0" expected_mask = control_mask & g0_mask - - actual_positive = adata.obs['control_G0'] == 'yes' + + actual_positive = adata.obs["control_G0"] == "yes" assert np.array_equal(expected_mask, actual_positive) - + # Check it's categorical - assert isinstance(adata.obs['control_G0'].dtype, pd.CategoricalDtype) - + assert isinstance(adata.obs["control_G0"].dtype, pd.CategoricalDtype) + def test_or_operator(self, adata_with_labels): """Test OR operator combines labels correctly.""" adata = create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1=['control'], - obs_key_2='cell_cycle', - match_values_2=['G0', 'G1'], - operator='OR', - output_obs_key='control_or_G0G1', - true_label='positive', - false_label='negative', + obs_key_1="treatment", + match_values_1=["control"], + obs_key_2="cell_cycle", + match_values_2=["G0", "G1"], + operator="OR", + output_obs_key="control_or_G0G1", + true_label="positive", + false_label="negative", ) - + # Check correct cells are labeled - control_mask = adata.obs['treatment'] == 'control' - g0g1_mask = adata.obs['cell_cycle'].isin(['G0', 'G1']) + control_mask = adata.obs["treatment"] == "control" + g0g1_mask = adata.obs["cell_cycle"].isin(["G0", "G1"]) expected_mask = control_mask | g0g1_mask - - actual_positive = adata.obs['control_or_G0G1'] == 'positive' + + actual_positive = adata.obs["control_or_G0G1"] == "positive" assert np.array_equal(expected_mask, actual_positive) - + def test_xor_operator(self, adata_with_labels): """Test XOR operator combines labels correctly.""" adata = create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1=['control'], - obs_key_2='cell_cycle', - match_values_2=['G0'], - operator='XOR', - output_obs_key='xor_result', - true_label='exactly_one', - false_label='both_or_neither', + obs_key_1="treatment", + match_values_1=["control"], + obs_key_2="cell_cycle", + match_values_2=["G0"], + operator="XOR", + output_obs_key="xor_result", + true_label="exactly_one", + false_label="both_or_neither", ) - + # Check correct cells are labeled - control_mask = adata.obs['treatment'] == 'control' - g0_mask = adata.obs['cell_cycle'] == 'G0' + control_mask = adata.obs["treatment"] == "control" + g0_mask = adata.obs["cell_cycle"] == "G0" expected_mask = control_mask ^ g0_mask - - actual_positive = adata.obs['xor_result'] == 'exactly_one' + + actual_positive = adata.obs["xor_result"] == "exactly_one" assert np.array_equal(expected_mask, actual_positive) - + def test_multiple_values_per_label(self, adata_with_labels): """Test with multiple values in each label.""" adata = create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1=['control', 'drug'], - obs_key_2='cell_cycle', - match_values_2=['G0', 'G1'], - operator='AND', - output_obs_key='combined', - true_label='yes', - false_label='no', + obs_key_1="treatment", + match_values_1=["control", "drug"], + obs_key_2="cell_cycle", + match_values_2=["G0", "G1"], + operator="AND", + output_obs_key="combined", + true_label="yes", + false_label="no", ) - + # All cells should be positive (all treatments AND first two phases) expected_count = 50 # 25 G0 + 25 G1 - actual_count = (adata.obs['combined'] == 'yes').sum() + actual_count = (adata.obs["combined"] == "yes").sum() assert actual_count == expected_count - + def test_case_insensitive_operator(self, adata_with_labels): """Test that operator is case-insensitive.""" adata = create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1=['control'], - obs_key_2='cell_cycle', - match_values_2=['G0'], - operator='and', # lowercase - output_obs_key='test', - true_label='yes', - false_label='no', + obs_key_1="treatment", + match_values_1=["control"], + obs_key_2="cell_cycle", + match_values_2=["G0"], + operator="and", # lowercase + output_obs_key="test", + true_label="yes", + false_label="no", ) - - assert 'test' in adata.obs.columns - + + assert "test" in adata.obs.columns + def test_error_label1_not_found(self, adata_with_labels): """Test error when label1 doesn't exist.""" with pytest.raises(KeyError, match="obs_key_1 'nonexistent' not found"): create_boolean_label_combination( adata_with_labels, - obs_key_1='nonexistent', - match_values_1=['control'], - obs_key_2='cell_cycle', - match_values_2=['G0'], - operator='AND', - output_obs_key='test', - true_label='yes', - false_label='no', + obs_key_1="nonexistent", + match_values_1=["control"], + obs_key_2="cell_cycle", + match_values_2=["G0"], + operator="AND", + output_obs_key="test", + true_label="yes", + false_label="no", ) - + def test_error_label2_not_found(self, adata_with_labels): """Test error when label2 doesn't exist.""" with pytest.raises(KeyError, match="obs_key_2 'nonexistent' not found"): create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1=['control'], - obs_key_2='nonexistent', - match_values_2=['G0'], - operator='AND', - output_obs_key='test', - true_label='yes', - false_label='no', + obs_key_1="treatment", + match_values_1=["control"], + obs_key_2="nonexistent", + match_values_2=["G0"], + operator="AND", + output_obs_key="test", + true_label="yes", + false_label="no", ) - + def test_error_invalid_operator(self, adata_with_labels): """Test error with invalid operator.""" with pytest.raises(ValueError, match="operator must be one of"): create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1=['control'], - obs_key_2='cell_cycle', - match_values_2=['G0'], - operator='INVALID', - output_obs_key='test', - true_label='yes', - false_label='no', + obs_key_1="treatment", + match_values_1=["control"], + obs_key_2="cell_cycle", + match_values_2=["G0"], + operator="INVALID", + output_obs_key="test", + true_label="yes", + false_label="no", ) - + def test_error_output_label_exists(self, adata_with_labels): """Test error when output_label already exists.""" with pytest.raises(KeyError, match="output_obs_key 'treatment' already exists"): create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1=['control'], - obs_key_2='cell_cycle', - match_values_2=['G0'], - operator='AND', - output_obs_key='treatment', # Already exists - true_label='yes', - false_label='no', + obs_key_1="treatment", + match_values_1=["control"], + obs_key_2="cell_cycle", + match_values_2=["G0"], + operator="AND", + output_obs_key="treatment", # Already exists + true_label="yes", + false_label="no", ) - + def test_error_label1_values_not_list(self, adata_with_labels): """Test error when label1_values is not a list.""" with pytest.raises(TypeError, match="match_values_1 must be a list"): create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1='control', # String instead of list - obs_key_2='cell_cycle', - match_values_2=['G0'], - operator='AND', - output_obs_key='test', - true_label='yes', - false_label='no', + obs_key_1="treatment", + match_values_1="control", # String instead of list + obs_key_2="cell_cycle", + match_values_2=["G0"], + operator="AND", + output_obs_key="test", + true_label="yes", + false_label="no", ) - + def test_error_value_not_in_label1(self, adata_with_labels): """Test error when value doesn't exist in label1.""" - with pytest.raises(ValueError, match="Value 'nonexistent' not found in obs_key_1"): + with pytest.raises( + ValueError, match="Value 'nonexistent' not found in obs_key_1" + ): create_boolean_label_combination( adata_with_labels, - obs_key_1='treatment', - match_values_1=['nonexistent'], - obs_key_2='cell_cycle', - match_values_2=['G0'], - operator='AND', - output_obs_key='test', - true_label='yes', - false_label='no', + obs_key_1="treatment", + match_values_1=["nonexistent"], + obs_key_2="cell_cycle", + match_values_2=["G0"], + operator="AND", + output_obs_key="test", + true_label="yes", + false_label="no", ) # ===== Tests for generate_thresholding_report ===== + class TestGenerateThresholdingReport: """Tests for generate_thresholding_report method.""" - + def test_text_format_basic(self, gmm_with_thresholding): """Test basic text format report.""" - report = gmm_with_thresholding.generate_thresholding_report(output_format='text') - + report = gmm_with_thresholding.generate_thresholding_report( + output_format="text" + ) + # Check report is a string assert isinstance(report, str) - + # Check key elements are present - assert 'Thresholding Report' in report - assert 'gene1' in report # Feature name - assert 'cell_cycle' in report # Obs label - assert 'Components: 2' in report - assert 'Low' in report - assert 'High' in report - + assert "Thresholding Report" in report + assert "gene1" in report # Feature name + assert "cell_cycle" in report # Obs label + assert "Components: 2" in report + assert "Low" in report + assert "High" in report + def test_dataframe_format_basic(self, gmm_with_thresholding): """Test basic dataframe format report.""" - report = gmm_with_thresholding.generate_thresholding_report(output_format='dataframe') - + report = gmm_with_thresholding.generate_thresholding_report( + output_format="dataframe" + ) + # Check report is a DataFrame assert isinstance(report, pd.DataFrame) - + # Check expected columns exist - expected_cols = ['Operation', 'Type', 'Feature', 'Layer', 'Obs Label', - 'Components', 'Thresholds', 'Labels', 'Parent', - 'Refined From', 'Total Cells'] + expected_cols = [ + "Operation", + "Type", + "Feature", + "Layer", + "Obs Label", + "Components", + "Thresholds", + "Labels", + "Parent", + "Refined From", + "Total Cells", + ] assert all(col in report.columns for col in expected_cols) - + # Check row count assert len(report) == 1 - + # Check values - assert report['Feature'].iloc[0] == 'gene1' - assert report['Type'].iloc[0] == 'standard' - assert report['Components'].iloc[0] == '2' - + assert report["Feature"].iloc[0] == "gene1" + assert report["Type"].iloc[0] == "standard" + assert report["Components"].iloc[0] == "2" + def test_text_format_with_refinement(self, sample_adata): """Test text report with refinement operation using SequentialGMM.""" from cc_mapping.thresholding import SequentialGMM - + # Create sequential instance seq_gmm = SequentialGMM( - adata=sample_adata, - thresholding_events_key='test_events' + adata=sample_adata, thresholding_events_key="test_events" ) - + # First threshold seq_gmm.threshold_entire_dataset( - feature='gene1', - label_obs_save_str='phase', + feature="gene1", + label_obs_save_str="phase", n_components=2, - ordered_labels=['Low', 'High'], - operation_name='first_threshold' + ordered_labels=["Low", "High"], + operation_name="first_threshold", ) - + # Refine one of the labels seq_gmm.refine_labels_with_gmm( - feature='gene2', - obs_label='phase', - value_to_refine='Low', + feature="gene2", + obs_label="phase", + value_to_refine="Low", n_components=2, - ordered_labels=['Low_A', 'Low_B'], - operation_name='refine_low' + ordered_labels=["Low_A", "Low_B"], + operation_name="refine_low", ) - - report = seq_gmm.generate_thresholding_report(output_format='text') - + + report = seq_gmm.generate_thresholding_report(output_format="text") + # Check refinement info is present - assert 'refine_low' in report - assert 'Refinement' in report - assert 'first_threshold' in report # Parent - assert 'Low' in report # Refined from - + assert "refine_low" in report + assert "Refinement" in report + assert "first_threshold" in report # Parent + assert "Low" in report # Refined from + def test_empty_events(self, sample_adata): """Test report with no operations.""" gmm = GMMThresholding( adata=sample_adata, - feature='gene1', - label_obs_save_str='labels', - thresholding_events_key='empty_events' + feature="gene1", + label_obs_save_str="labels", + thresholding_events_key="empty_events", ) - - report = gmm.generate_thresholding_report(output_format='text') - + + report = gmm.generate_thresholding_report(output_format="text") + assert isinstance(report, str) assert report == "No thresholding operations found." - + def test_empty_events_dataframe(self, sample_adata): """Test dataframe report with no operations.""" gmm = GMMThresholding( adata=sample_adata, - feature='gene1', - label_obs_save_str='labels', - thresholding_events_key='empty_events' + feature="gene1", + label_obs_save_str="labels", + thresholding_events_key="empty_events", ) - - report = gmm.generate_thresholding_report(output_format='dataframe') - + + report = gmm.generate_thresholding_report(output_format="dataframe") + assert isinstance(report, pd.DataFrame) assert len(report) == 0 - + def test_error_key_not_found(self, sample_adata): """Test error when thresholding_events_key doesn't exist.""" # Don't create the key at all - GMMThresholding __init__ creates it # So we need to delete it after creation gmm = GMMThresholding( adata=sample_adata, - feature='gene1', - label_obs_save_str='labels', - thresholding_events_key='nonexistent' + feature="gene1", + label_obs_save_str="labels", + thresholding_events_key="nonexistent", ) - + # Delete the key that was auto-created - del gmm.adata.uns['nonexistent'] - - with pytest.raises(KeyError, match="thresholding_events_key 'nonexistent' not found"): - gmm.generate_thresholding_report(output_format='text') - + del gmm.adata.uns["nonexistent"] + + with pytest.raises( + KeyError, match="thresholding_events_key 'nonexistent' not found" + ): + gmm.generate_thresholding_report(output_format="text") + def test_error_invalid_format(self, gmm_with_thresholding): """Test error with invalid output_format.""" with pytest.raises(ValueError, match="output_format must be one of"): - gmm_with_thresholding.generate_thresholding_report(output_format='invalid') - + gmm_with_thresholding.generate_thresholding_report(output_format="invalid") + def test_error_not_dict(self, sample_adata): """Test error when uns key is not a dict.""" # Create a GMM instance and manually corrupt the uns key gmm = GMMThresholding( adata=sample_adata, - feature='gene1', - label_obs_save_str='labels', - thresholding_events_key='corrupt_events' + feature="gene1", + label_obs_save_str="labels", + thresholding_events_key="corrupt_events", ) - gmm.adata.uns['corrupt_events'] = "not a dict" - + gmm.adata.uns["corrupt_events"] = "not a dict" + with pytest.raises(TypeError, match="must be a dict or OrderedDict"): - gmm.generate_thresholding_report(output_format='text') - + gmm.generate_thresholding_report(output_format="text") + def test_cell_counts_with_valid_obs(self, gmm_with_thresholding): """Test that cell counts are calculated when obs column exists.""" - report = gmm_with_thresholding.generate_thresholding_report(output_format='text') - + report = gmm_with_thresholding.generate_thresholding_report( + output_format="text" + ) + # Check cell counts are present - assert 'Cell counts:' in report + assert "Cell counts:" in report # The actual counts will vary, just check the format is there - assert 'Low=' in report or 'High=' in report - + assert "Low=" in report or "High=" in report + def test_manual_thresholds_handling(self, sample_adata): """Test report handles manual thresholds (no GMM info).""" from cc_mapping.thresholding import SequentialGMM - + seq_gmm = SequentialGMM( - adata=sample_adata, - thresholding_events_key='manual_events' + adata=sample_adata, thresholding_events_key="manual_events" ) - + # Use manual thresholds seq_gmm.threshold_entire_dataset( - feature='gene1', - label_obs_save_str='labels', + feature="gene1", + label_obs_save_str="labels", n_components=2, - ordered_labels=['Low', 'High'], + ordered_labels=["Low", "High"], manual_thresholds=[1.0], - operation_name='manual_op' + operation_name="manual_op", ) - - report = seq_gmm.generate_thresholding_report(output_format='text') - + + report = seq_gmm.generate_thresholding_report(output_format="text") + # When manual thresholds are used, it still shows the n_components - assert 'Components: 2' in report - assert 'Thresholds: [1.0000]' in report + assert "Components: 2" in report + assert "Thresholds: [1.0000]" in report