Merge remote-tracking branch 'origin/main' into batchSOM

Author: berombau

docs/api.md

@@ -50,7 +50,6 @@ For more background information, see the paper for this software package {cite:p
     :toctree: generated
 
     pp.aggregate_flowframes
-    pp.normalize_estimate_logicle
 ```
 
 ## Models

docs/notebooks/example.ipynb

@@ -25,20 +25,19 @@ urls.Documentation = "https://flowsom.readthedocs.io/en/latest/"
 urls.Source = "https://github.com/saeyslab/FlowSOM_Python"
 urls.Home-page = "https://github.com/saeyslab/FlowSOM_Python"
 dependencies = [
-    "mudata",
+    "mudata<=0.2.4",
     "numpy",
     "matplotlib",
     "pandas",
     "scipy",
     "readfcs",
-    "pytometry",
     "scikit-learn",
     "igraph",
     "session-info",
     "numba",
     "scanpy",
     "seaborn",
-    "anndata",
+    "anndata<=0.10.8",
     "loguru",
 ]
 
@@ -57,6 +56,8 @@ doc = [
     "ipykernel",
     "ipython",
     "sphinx-copybutton",
+    # For example notebooks
+    "pytometry>=0.1.5",
 ]
 test = ["pytest", "coverage"]
 

pyproject.toml

@@ -224,7 +224,16 @@ def plot_numbers(fsom, level="clusters", max_node_size=0, **kwargs):
 
 
 def plot_variable(
-    fsom, variable, cmap=FlowSOM_colors(), labels=None, text_size=5, text_color="black", lim=None, title=None, **kwargs
+    fsom,
+    variable,
+    cmap=FlowSOM_colors(),
+    labels=None,
+    text_size=5,
+    text_color="black",
+    lim=None,
+    title=None,
+    categorical=True,
+    **kwargs,
 ):
     """Plot FlowSOM grid or tree, colored by node values given in variable.
 
@@ -260,7 +269,14 @@ def plot_variable(
     if labels is not None:
         ax = add_text(ax, layout, labels, text_size=text_size, text_color=text_color, ha=["center"], va=["center"])
     ax, fig = add_legend(
-        fig=fig, ax=ax, data=variable, title="Marker", cmap=cmap, location="upper left", bbox_to_anchor=(1.04, 1)
+        fig=fig,
+        ax=ax,
+        data=variable,
+        title="Marker",
+        cmap=cmap,
+        location="upper left",
+        bbox_to_anchor=(1.04, 1),
+        categorical=categorical,
     )
     ax.axis("equal")
     if title is not None:
@@ -294,7 +310,7 @@ def plot_marker(fsom, marker, ref_markers=None, lim=None, cmap=FlowSOM_colors(),
         lim = (mfis[:, indices_markers].min(), mfis[:, indices_markers].max())
     marker = list(get_channels(fsom, marker).keys())[0]
     marker_index = np.where(fsom.get_cell_data().var_names == marker)[0][0]
-    fig = plot_variable(fsom, variable=mfis[:, marker_index], cmap=cmap, lim=lim, **kwargs)
+    fig = plot_variable(fsom, variable=mfis[:, marker_index], cmap=cmap, lim=lim, categorical=False, **kwargs)
     return fig
 
 

src/flowsom/pl/plot_functions.py

@@ -1 +1 @@
-from .fcs_functions import aggregate_flowframes, normalize_estimate_logicle
+from .fcs_functions import aggregate_flowframes

src/flowsom/pp/__init__.py

@@ -4,8 +4,6 @@
 
 import anndata as ad
 import numpy as np
-import pandas as pd
-import pytometry as pm
 
 from flowsom.io import read_FCS
 from flowsom.tl import get_markers
@@ -54,33 +52,3 @@ def aggregate_flowframes(filenames, c_total, channels=None, keep_order=False):
         flow_frame.append(f)
     flow_frame = ad.concat(flow_frame, join="outer", uns_merge="first")
     return flow_frame
-
-
-def normalize_estimate_logicle(adata, channels, m=4.5, q=0.05):
-    """Normalize and estimate logicle parameters.
-
-    :param adata: An AnnData object
-    :type adata: AnnData
-    :param channels: Channels/markers to normalize
-    :type channels: list
-    :param m: Logicle parameter. Default=4.5
-    :type m: float
-    :param q: Quantile to use for negative values. Default=0.05
-    :type q: float
-    """
-    assert isinstance(adata, ad.AnnData), "Please provide an AnnData object"
-    assert isinstance(channels, list), "Please provide a list of channels"
-    channels = list(get_markers(adata, channels).keys())
-    assert all(i in adata.var_names for i in channels), "Channels should be in the AnnData object"
-    neg_marker_quantiles = [
-        np.quantile(adata[:, channel].X[adata[:, channel].X < 0], q) if (adata[:, channel].X < 0).any() else 0.5
-        for channel in channels
-    ]
-    neg_marker_quantiles = pd.Series(neg_marker_quantiles, index=channels, dtype=float)
-    max_range = adata.var["$PnR"][channels].astype(float)
-    w = (m - np.log10(max_range / np.abs(neg_marker_quantiles))) / 2
-    for channel in channels:
-        adata[:, channel].X = pm.tools.normalize_logicle(
-            adata[:, channel], t=max_range[channel], m=m, a=0, w=w[channel], inplace=False
-        ).X
-    return adata