Merge pull request #11 from ChoBioLab/qa

2.0.1

Author: ctastad

.gitignore

@@ -29,6 +29,7 @@ var/
 wheels/
 share/python-wheels/
 *.egg-info/
+*.wolf*.py
 .installed.cfg
 *.egg
 MANIFEST

README.md

@@ -23,12 +23,36 @@ with desired environment name):
 `git clone https://github.com/ChoBioLab/corescpy.git`, or
 look above for the green "Code" button and press it for instructions.
 
-5. Naviate to the repository directory (replace <DIRECTORY> with your path):
+5. Navigate to the repository directory (replace <DIRECTORY> with your path):
 `cd <DIRECTORY>`
 
 6. Install the package with pip. (Ensure you have pip installed.)
 `pip install .`
 
+7. If you have issues with resolving/finding the most up-to-date version of the `spatialdata` and/or `spatialdata-io` packages, try running:
+```
+pip install git+https://github.com/scverse/spatialdata
+pip install git+https://github.com/scverse/spatialdata-io
+```
+in your terminal while in your conda environment, then re-try step (6).
+
+8. If you're planning to use this environment with Jupyter notebooks, run `conda install nb_conda_kernels`, then `pip install ipykernel`.
+
+
+** Note: To use GPU resources, use `conda install -c rapidsai -c nvidia -c conda-forge cugraph cuml cudf` and install the gpu version of coreSCpy (which should `pip install scanpy[rapids]`).
+
+** Tip: If you run out of space, run:
+```
+pip cache purge
+conda clean -i
+conda clean -t
+```
+
+If you have issues seeing the environment when choosing the kernel for your Jupyter notebook:
+```
+conda install nb_conda_kernels
+```
+
 ## Usage
 
 1. You can now load `corescpy` like any other distributed Python package.
@@ -64,7 +88,7 @@ Here are the methods (applicable to scRNA-seq generally, not just perturbations)
 
 * `self.preprocess(...)`: Perform filtering, normalization, scaling, quality control analysis, selection for highly variable genes, regressing out confounds, and intial exploration of the data (e.g., cell counts, mitochondrial counts, etc.).
 * `self.cluster(...)`: Perform dimensionality reduction (e.g., PCA) and clustering (Louvain or Leiden), as well as related visualization.
-* `self.plot(...)`: Create additional basic plots (e.g., dot, matrix, and violin gene expression plots).
+* `self.plot(...)`: Create additional basic plots (e.g., dot, matrix, and violin gene expression plots). Use `self.plot_compare(...)` to make plots comparing gene expression across groups.
 
 The following perturbation-specific methods can be executed optionally and in any order:
 

build.sh

@@ -2,7 +2,7 @@
 # Build & Install Package (& Test if desired)
 
 # python3 -m build
-# pip install dist/corescpy-0.1.0.tar.gz
+# pip install dist/corescpy-0.2.0.tar.gz
 # python3 setup.py install
 # python3 setup.py develop
 

corescpy/__init__.py

@@ -1,20 +1,28 @@
 # __init__.py
 # pylint: disable=unused-import
 
+import sys
+from .class_sc import Omics
+from .class_crispr import Crispr
+from .class_spatial import Spatial
 from . import utils as tl
 from . import processing as pp
 from . import analysis as ax
 from . import visualization as pl
-from .class_sc import Omics
-from .class_crispr import Crispr
-from .class_spatial import Spatial
+from . import class_crispr, class_sc, class_spatial
 
-import sys
+mod = ["ax", "pl", "pp", "tl", "Omics", "Crispr", "Spatial"]
+sys.modules.update({f"{__name__}.{m}": globals()[m] for m in mod})
+
+
+def get_panel_constants(**kwargs):
+    from .constants import get_panel_constants as gpc  # noqa: E402
+    return gpc(**kwargs)
 
-sys.modules.update({f"{__name__}.{m}": globals()[m]
-                    for m in ["ax", "pl", "pp", "tl",
-                              "Omics", "Crispr", "Spatial"]})
 
 __all__ = [
-    "ax", "pl", "pp", "tl", "Omics", "Crispr", "Spatial"
+    "ax", "pl", "pp", "tl", "Omics", "Crispr", "Spatial",
+    "processing", "analysis", "visualization", "utils",
+    "class_sc", "class_crispr", "class_spatial", "defaults",
+    "get_panel_constants"
 ]

corescpy/analysis/__init__.py

@@ -6,15 +6,21 @@
     perform_mixscape, perform_augur, perform_differential_prioritization,
     compute_distance, perform_gsea, perform_gsea_pt,
     perform_pathway_interference, perform_dea, calculate_dea_deseq2)
-from .clustering import cluster, find_marker_genes, perform_celltypist
+from .clustering import (cluster, find_marker_genes, make_marker_genes_df,
+                         perform_celltypist, annotate_by_markers,
+                         print_marker_info)
 from .composition import analyze_composition
-from .communication import analyze_receptor_ligand, analyze_causal_network
+from .communication import (analyze_receptor_ligand, analyze_causal_network)
+from .quantification import (classify_gex_cells, classify_coex_cells,
+                             classify_tx)
 
 __all__ = [
     "perform_mixscape", "perform_augur",
     "perform_differential_prioritization", "compute_distance",
     "perform_gsea", "perform_gsea_pt", "perform_pathway_interference",
-    "perform_dea", "calculate_dea_deseq2", "cluster", "find_marker_genes",
-    "perform_celltypist", "analyze_composition",
-    "analyze_receptor_ligand", "analyze_causal_network"
+    "perform_dea", "calculate_dea_deseq2", "cluster",
+    "find_marker_genes", "make_marker_genes_df", "print_marker_info",
+    "perform_celltypist", "annotate_by_markers", "analyze_composition",
+    "analyze_receptor_ligand", "analyze_causal_network",
+    "classify_gex_cells", "classify_coex_cells", "classify_tx"
 ]