fix decoupler

Author: flying-sheep

docs/notebooks/basic-scrna-tutorial.ipynb

@@ -1173,15 +1173,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
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     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
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      ]
     },
@@ -1204,39 +1204,39 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
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     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
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     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
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     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
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     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
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     },
@@ -1398,7 +1398,7 @@
    ],
    "source": [
     "# Query Omnipath and get PanglaoDB\n",
-    "markers = dc.get_resource(name=\"PanglaoDB\", organism=\"human\")\n",
+    "markers = dc.op.resource(name=\"PanglaoDB\", organism=\"human\")\n",
     "# Keep canonical cell type markers alone\n",
     "markers = markers[markers[\"canonical_marker\"]]\n",
     "\n",
@@ -1424,31 +1424,31 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
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     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "\r",
+      "\r\n",
       " 50%|█████     | 1/2 [00:05<00:05,  5.44s/it]"
      ]
     },
     {
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      "output_type": "stream",
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+      "\r\n",
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      ]
     },
     {
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      "output_type": "stream",
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+      "\r\n",
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     },
@@ -1461,15 +1461,15 @@
     }
    ],
    "source": [
-    "dc.run_mlm(mat=adata, net=markers, weight=None, source=\"cell_type\", target=\"genesymbol\", verbose=True, use_raw=False)"
+    "dc.mt.mlm(adata, net=markers.rename(columns=dict(cell_type=\"source\", genesymbol=\"target\")), verbose=True)"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "2e01e50a-238f-459f-a9f6-f25880b78a2c",
    "metadata": {},
    "source": [
-    "The obtained results are stored in the .obsm key, with `mlm_estimate` representing coefficient t-values:"
+    "The obtained results are stored in `.obsm`, with `\"score_mlm\"` representing coefficient t-values, and `\"padj_mlm\"` representing adjusted p-values.:"
    ]
   },
   {
@@ -1714,7 +1714,7 @@
     }
    ],
    "source": [
-    "adata.obsm[\"mlm_estimate\"].head()"
+    "adata.obsm[\"score_mlm\"].head()"
    ]
   },
   {
@@ -1749,7 +1749,7 @@
     }
    ],
    "source": [
-    "acts = dc.get_acts(adata=adata, obsm_key=\"mlm_estimate\")\n",
+    "acts = dc.pp.get_obsm(adata, \"score_mlm\")\n",
     "sc.pl.umap(\n",
     "    acts,\n",
     "    color=[\n",
@@ -1780,8 +1780,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mean_enr = dc.summarize_acts(acts, groupby=\"leiden_res0_5\", min_std=1.0)\n",
-    "annotation_dict = dc.assign_groups(mean_enr)\n",
+    "enr = dc.tl.rankby_group(acts, groupby=\"leiden_res0_5\")\n",
+    "annotation_dict = enr[enr[\"stat\"] > 0].groupby(\"group\").head(1).set_index(\"group\")[\"name\"].to_dict()\n",
     "adata.obs[\"dc_anno\"] = [annotation_dict[clust] for clust in adata.obs[\"leiden_res0_5\"]]"
    ]
   },

pyproject.toml

@@ -92,6 +92,7 @@ lint.select = [
 ]
 lint.ignore = [
   "B008", # Errors from function calls in argument defaults. These are fine when the result is immutable.
+  "C408", # dict() is nice for some use cases
   "D100", # Missing docstring in public module
   "D104", # Missing docstring in public package
   "D105", # __magic__ methods are often self-explanatory, allow missing docstrings