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fix bugs perturbation notebook #94

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fix bugs perturbation notebook #94

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4cdb6d0
fix bugs perturbation nb
chelseabright96 Jun 18, 2024
27a2d58
change sphinx version
chelseabright96 Jun 18, 2024
485fb53
remove deprecated code
chelseabright96 Jun 18, 2024
0573377
reformat with black
chelseabright96 Jun 18, 2024
4841a08
reformat other file
chelseabright96 Jun 19, 2024
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2 changes: 1 addition & 1 deletion docs/conf.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,6 @@
"sphinx.ext.autosummary",
"scanpydoc.elegant_typehints",
"scanpydoc.definition_list_typed_field",
"scanpydoc.autosummary_generate_imported",
*[p.stem for p in (HERE / "extensions").glob("*.py")],
]

Expand Down Expand Up @@ -75,6 +74,7 @@
todo_include_todos = False
numpydoc_show_class_members = False
annotate_defaults = True
autosummary_generate_imported = True
# The master toctree document.
master_doc = "index"

Expand Down
60 changes: 36 additions & 24 deletions docs/tutorials/scgen_perturbation_prediction.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,8 @@
"outputs": [],
"source": [
"import sys\n",
"#if branch is stable, will install via pypi, else will install from source\n",
"\n",
"# if branch is stable, will install via pypi, else will install from source\n",
"branch = \"stable\"\n",
"IN_COLAB = \"google.colab\" in sys.modules\n",
"\n",
Expand Down Expand Up @@ -78,8 +79,10 @@
}
],
"source": [
"train = sc.read(\"./tests/data/train_kang.h5ad\",\n",
" backup_url='https://drive.google.com/uc?id=1r87vhoLLq6PXAYdmyyd89zG90eJOFYLk')"
"train = sc.read(\n",
" \"./tests/data/train_kang.h5ad\",\n",
" backup_url=\"https://drive.google.com/uc?id=1r87vhoLLq6PXAYdmyyd89zG90eJOFYLk\",\n",
")"
]
},
{
Expand All @@ -95,8 +98,9 @@
"metadata": {},
"outputs": [],
"source": [
"train_new = train[~((train.obs[\"cell_type\"] == \"CD4T\") &\n",
" (train.obs[\"condition\"] == \"stimulated\"))]"
"train_new = train[\n",
" ~((train.obs[\"cell_type\"] == \"CD4T\") & (train.obs[\"condition\"] == \"stimulated\"))\n",
"].copy()"
]
},
{
Expand Down Expand Up @@ -198,10 +202,7 @@
],
"source": [
"model.train(\n",
" max_epochs=100,\n",
" batch_size=32,\n",
" early_stopping=True,\n",
" early_stopping_patience=25\n",
" max_epochs=100, batch_size=32, early_stopping=True, early_stopping_patience=25\n",
")"
]
},
Expand Down Expand Up @@ -258,8 +259,13 @@
"source": [
"sc.pp.neighbors(latent_adata)\n",
"sc.tl.umap(latent_adata)\n",
"sc.pl.umap(latent_adata, color=['condition', 'cell_type'], wspace=0.4, frameon=False,\n",
" save='latentspace_batch32_klw000005_z100__100e.pdf')"
"sc.pl.umap(\n",
" latent_adata,\n",
" color=[\"condition\", \"cell_type\"],\n",
" wspace=0.4,\n",
" frameon=False,\n",
" save=\"latentspace_batch32_klw000005_z100__100e.pdf\",\n",
")"
]
},
{
Expand Down Expand Up @@ -339,11 +345,9 @@
],
"source": [
"pred, delta = model.predict(\n",
" ctrl_key='control',\n",
" stim_key='stimulated',\n",
" celltype_to_predict='CD4T'\n",
" ctrl_key=\"control\", stim_key=\"stimulated\", celltype_to_predict=\"CD4T\"\n",
")\n",
"pred.obs['condition'] = 'pred'"
"pred.obs[\"condition\"] = \"pred\""
]
},
{
Expand Down Expand Up @@ -380,8 +384,12 @@
"metadata": {},
"outputs": [],
"source": [
"ctrl_adata = train[((train.obs['cell_type'] == 'CD4T') & (train.obs['condition'] == 'control'))]\n",
"stim_adata = train[((train.obs['cell_type'] == 'CD4T') & (train.obs['condition'] == 'stimulated'))]"
"ctrl_adata = train[\n",
" ((train.obs[\"cell_type\"] == \"CD4T\") & (train.obs[\"condition\"] == \"control\"))\n",
"]\n",
"stim_adata = train[\n",
" ((train.obs[\"cell_type\"] == \"CD4T\") & (train.obs[\"condition\"] == \"stimulated\"))\n",
"]"
]
},
{
Expand Down Expand Up @@ -433,8 +441,12 @@
],
"source": [
"sc.tl.pca(eval_adata)\n",
"sc.pl.pca(eval_adata, color=\"condition\", frameon=False,\n",
" save='pred_stim_b32_klw000005_z100__100e.pdf')"
"sc.pl.pca(\n",
" eval_adata,\n",
" color=\"condition\",\n",
" frameon=False,\n",
" save=\"pred_stim_b32_klw000005_z100__100e.pdf\",\n",
")"
]
},
{
Expand Down Expand Up @@ -466,7 +478,7 @@
}
],
"source": [
"CD4T = train[train.obs[\"cell_type\"] ==\"CD4T\"]"
"CD4T = train[train.obs[\"cell_type\"] == \"CD4T\"]"
]
},
{
Expand Down Expand Up @@ -527,7 +539,7 @@
" labels={\"x\": \"predicted\", \"y\": \"ground truth\"},\n",
" path_to_save=\"./reg_mean1.pdf\",\n",
" show=True,\n",
" legend=False\n",
" legend=False,\n",
")"
]
},
Expand Down Expand Up @@ -567,11 +579,11 @@
" eval_adata,\n",
" axis_keys={\"x\": \"pred\", \"y\": \"stimulated\"},\n",
" gene_list=diff_genes[:10],\n",
" top_100_genes= diff_genes,\n",
" labels={\"x\": \"predicted\",\"y\": \"ground truth\"},\n",
" top_100_genes=diff_genes,\n",
" labels={\"x\": \"predicted\", \"y\": \"ground truth\"},\n",
" path_to_save=\"./reg_mean1.pdf\",\n",
" show=True,\n",
" legend=False\n",
" legend=False,\n",
")"
]
},
Expand Down
3 changes: 2 additions & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,8 @@ scanpydoc = {version = ">=0.5", optional = true}
scikit-misc = {version = ">=0.1.3", optional = true}
scvi-tools = ">=0.20.0"
seaborn = ">=0.11"
sphinx = {version = ">=4.1,<4.4", optional = true}
numpy = "<2.0.0"
sphinx = {version = ">=5.0", optional = true}
sphinx-autodoc-typehints = {version = "*", optional = true}
sphinx-material = {version = "*", optional = true}
typing_extensions = {version = "*", python = "<3.8"}
Expand Down
6 changes: 3 additions & 3 deletions scgen/_scgen.py
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Original file line number Diff line number Diff line change
Expand Up @@ -284,7 +284,7 @@ def batch_removal(self, adata: Optional[AnnData] = None) -> AnnData:
adata_raw = AnnData(X=adata.raw.X, var=adata.raw.var)
adata_raw.obs_names = adata.obs_names
corrected.raw = adata_raw
corrected.obsm["latent"] = all_shared_ann[corrected.obs_names,:].X
corrected.obsm["latent"] = all_shared_ann[corrected.obs_names, :].X
corrected.obsm["corrected_latent"] = self.get_latent_representation(
corrected
)
Expand Down Expand Up @@ -314,7 +314,7 @@ def batch_removal(self, adata: Optional[AnnData] = None) -> AnnData:
adata_raw = AnnData(X=adata.raw.X, var=adata.raw.var)
adata_raw.obs_names = adata.obs_names
corrected.raw = adata_raw
corrected.obsm["latent"] = all_corrected_data[corrected.obs_names,:].X
corrected.obsm["latent"] = all_corrected_data[corrected.obs_names, :].X
corrected.obsm["corrected_latent"] = self.get_latent_representation(
corrected
)
Expand Down Expand Up @@ -447,7 +447,7 @@ def reg_mean_plot(
x=x,
y=y,
arrowprops=dict(arrowstyle="->", color="grey", lw=0.5),
force_points=(0.0, 0.0),
force_static=(0.0, 0.0),
)
if legend:
pyplot.legend(loc="center left", bbox_to_anchor=(1, 0.5))
Expand Down
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