LuckyMD
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diff --git a/‎CONTRIBUTING.md‎
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diff --git a/‎docker/openproblems-python-extras/README.md‎
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diff --git a/‎docker/openproblems-r-extras/README.md‎
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diff --git a/‎openproblems/data/pancreas.py‎
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diff --git a/‎openproblems/tasks/spatial_decomposition/__init__.py‎
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@@ -240,6 +240,28 @@ jobs:
         sudo apt-get update -qq || (sudo rm /etc/apt/sources.list.d/* && sudo apt-get update -yqq)
         sudo apt-get install -qy --no-install-recommends libhdf5-dev pandoc gfortran libblas-dev liblapack-dev libedit-dev llvm-dev
 
+    - name: Check Tower authentication
+      env:
+        TOWER_ACCESS_TOKEN: ${{ secrets.TOWER_ACCESS_KEY }}
+        TOWER_ORG_ID: 9180144064527
+        TOWER_WORKSPACE_ID: 53907369739130
+      run: |
+        result=$(
+          curl -X GET "https://api.tower.nf/orgs/${TOWER_ORG_ID}/workspaces/${TOWER_WORKSPACE_ID}" \
+          -H "Accept: application/json" \
+          -H "Authorization: Bearer ${TOWER_ACCESS_TOKEN}" || \
+          (
+            echo "Tower authentication failed; set TOWER_ACCESS_KEY in repo secrets." >&2 &&
+            echo "See CONTRIBUTING.md for details." >&2 && \
+            exit 1
+          )
+        )
+        if [ -z "${result}" ]; then
+          echo "Tower authentication failed; email singlecellopenproblems@protonmail.com with a request to be added to the openproblems-bio workspace"
+          exit 1
+        fi
+      shell: /bin/bash -e {0}
+
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
 
@@ -381,10 +381,10 @@ pip install --editable .[test,r]
 pytest -v
 ```
 
-You may run specific tests with
+You may run specific tests quickly with
 
 ```shell
-pytest -k my_task
+PYTEST_MAX_RETRIES=0 pytest -k my_task
 ```
 
 The test suite also requires Python>=3.7, R>=4.0, and Docker to be installed.
 
@@ -15,7 +15,9 @@ Python packages:
 * molecular-cross-validation
 * MulticoreTSNE
 * phate
+* scvi-tools
 * neuralee
+* tangram-sc
 * torch
 * tensorflow-cpu
 * ivis
 
@@ -7,6 +7,8 @@ MulticoreTSNE==0.1
 phate==1.0.*
 pybedtools==0.9.*
 pyensembl==2.0.*
+scvi-tools==0.16.*
+tangram-sc==1.0.*
 tensorflow-cpu==2.7.*
 torch==1.11.*
 xgboost==1.6.*
@@ -13,6 +13,8 @@ R packages:
 * batchelor
 * sparsesvd
 * dplyr
+* spacexr
+* Seurat
 
 Python packages:
 
 
@@ -6,6 +6,7 @@ conos@1.4.6
 crayon@1.5.1
 dbplyr@2.2.1
 devtools@2.3.2
+dmcable/spacexr@6a593aa # master
 dplyr@1.0.9
 e1071@1.7-11
 ellipsis@0.3.2
 
@@ -1,5 +1,7 @@
 from . import utils
 
+import anndata as ad
+import numpy as np
 import os
 import scanpy as sc
 import scprep
@@ -47,3 +49,24 @@ def load_pancreas(test=False):
             utils.filter_genes_cells(adata)
 
         return adata
+
+
+def get_pancreas_integer(adata: ad.AnnData):
+    """Transform counts to integer.
+
+    For some platforms the pancreas data set only have processed counts.
+    Here we grab those with integer counts.
+    See https://github.com/theislab/scib-reproducibility/tree/main/notebooks/data_preprocessing/pancreas # noqa: E501
+    """
+    is_int = ["smartseq2"]
+    is_int += ["inDrop{}".format(x) for x in range(1, 5)]
+
+    keep = np.zeros(len(adata)).astype(bool)
+
+    for tech in is_int:
+        idx = adata.obs.tech.values == tech
+        keep = keep | idx
+
+    adata = adata[keep, :].copy()
+
+    return adata
@@ -3,4 +3,5 @@
 from . import label_projection
 from . import multimodal_data_integration
 from . import regulatory_effect_prediction
+from . import spatial_decomposition
 from ._batch_integration import batch_integration_graph
@@ -0,0 +1,50 @@
+# Spatial Decomposition/Deconvolution
+
+## The task
+
+Spatial decomposition (also often referred to as Spatial deconvolution) is
+applicable to spatial transcriptomics data where the transcription profile of
+each capture location (spot, voxel, bead, etc.) do not share a bijective
+relationship with the cells in the tissue, i.e., multiple cells may contribute
+to the same capture location. The task of spatial decomposition then refers to
+estimating the composition of cell types/states that are present at each capture
+location. The cell type/states estimates are presented as proportion values,
+representing the proportion of the cells at each capture location that belong to
+a given cell type.
+
+We distinguish between _reference-based_ decomposition and _de novo_
+decomposition, where the former leverage external data (e.g., scRNA-seq or
+scNuc-seq) to guide the inference process, while the latter only work with the
+spatial data. We require that all datasets have an associated reference single
+cell data set, but methods are free to ignore this information.
+
+## Metrics
+
+### R2
+
+R2 pronounced as "R squared", also known as the "coefficient of determination". R2
+reports the fraction of the true proportion values' (`adata.obsm["proportions_true"]`)
+variance that can be explained by the predicted proportion values
+(`adata.obsm["proportion_pred"]`). The **best score**, and upper bound, is 1.0. There is
+no fixed lower bound for the metric. The _uniform/non-weighted average_ across all cell
+types/states is used to summarize performance. See the
+[sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html)
+documentation for details on the implementation and the
+[wikipedia](https://en.wikipedia.org/wiki/Coefficient_of_determination) site for more
+general information regarding the metric.
+
+## API
+
+Datasets consists of 2 `anndata.AnnData` objects, concatenated by key
+`adata.obs["modality"]` with values:
+
+* `sc` for the single cell reference.
+* `sp` for the target spatial dataset.
+
+In the single cell reference, cell-types are stored in `adata_sc.obs["label"]`.
+In the spatial target, ground-truth cell-type proportions are stored in
+`adata_spatial.obsm["proportions_true"]`.
+Methods should return only the spatial data with inferred proportions stored in
+`adata_spatial.obsm["proportions_pred"]`.
+Metrics shall compare `adata_spatial.obsm['proportions_pred']` to
+`adata_spatial.obsm['proportions_true']`.
@@ -0,0 +1,14 @@
+from ...utils import get_callable_members
+from . import api
+from . import datasets
+from . import methods
+from . import metrics
+
+_task_name = "Spatial Decomposition"
+_task_summary = (
+    "Decompose spatial gene expression data by cell type from single cell reference"
+)
+
+DATASETS = get_callable_members(datasets)
+METHODS = get_callable_members(methods)
+METRICS = get_callable_members(metrics)