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Made changes to liana code to update for decoupler-py 2.o #178

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Made changes to liana code to update for decoupler-py 2.o #178

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ccefe77
Changes to code for decoupler==2
robinfallegger May 23, 2025
ee62591
Updated decoupler functions in docs. Not all were tested because envi…
robinfallegger May 23, 2025
0ffa3d2
Merge remote-tracking branch 'upstream/main' into decoupler2.0
robinfallegger May 23, 2025
72c11db
fix missing param
robinfallegger May 23, 2025
c6e876a
updated docs
robinfallegger May 23, 2025
3fbf861
Merge branch 'main' into decoupler2.0
robinfallegger May 23, 2025
cfd2d56
Merge branch 'main' into decoupler2.0
robinfallegger May 23, 2025
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6 changes: 3 additions & 3 deletions docs/source/notebooks/bivariate.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1632,7 +1632,7 @@
],
"source": [
"# Get transcription factor resource\n",
"net = dc.get_collectri()"
"net = dc.op.collectri(organism='human', remove_complexes=False, license='academic', verbose=False)"
]
},
{
Expand Down Expand Up @@ -1665,7 +1665,7 @@
],
"source": [
"# run enrichment\n",
"dc.run_ulm(adata, net=net, use_raw=False, verbose=True)"
"dc.mt.ulm(adata, net=net, raw=False, verbose=True)"
]
},
{
Expand All @@ -1685,7 +1685,7 @@
"metadata": {},
"outputs": [],
"source": [
"est = li.ut.obsm_to_adata(adata, 'ulm_estimate')\n",
"est = li.ut.obsm_to_adata(adata, 'score_ulm')\n",
"est.var['cv'] = est.X.std(axis=0) / est.X.mean(axis=0)\n",
"top_tfs = est.var.sort_values('cv', ascending=False, key=abs).head(50).index\n"
]
Expand Down
16 changes: 8 additions & 8 deletions docs/source/notebooks/liana_c2c.ipynb
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Expand Up @@ -1315,7 +1315,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -1340,7 +1340,7 @@
],
"source": [
"# load PROGENy pathways\n",
"net = dc.get_progeny(organism='human', top=5000)"
"net = dc.op.progeny(organism='human', top=5000)"
]
},
{
Expand All @@ -1355,7 +1355,7 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1435,18 +1435,18 @@
],
"source": [
"# generate ligand-receptor geneset\n",
"lr_progeny = li.rs.generate_lr_geneset(lr_pairs, net, lr_sep=\"^\")\n",
"lr_progeny = li.rs.generate_lr_geneset(lr_pairs, net, lr_sep=\"^\").rename(columns = {\"interaction\": \"target\"})\n",
"lr_progeny.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# run enrichment analysis\n",
"estimate, pvals = dc.run_ulm(lr_loadings.transpose(), lr_progeny, source=\"source\", target=\"interaction\", use_raw=False)"
"estimate, pvals = dc.mt.ulm(lr_loadings.transpose(), lr_progeny, raw=False)"
]
},
{
Expand All @@ -1459,7 +1459,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -1474,7 +1474,7 @@
}
],
"source": [
"dc.plot_barplot(estimate, 'Factor 6', vertical=True, cmap='coolwarm', vmin=-7, vmax=7)"
"dc.pl.barplot(estimate, 'Factor 6', vertical=True, cmap='coolwarm', vmin=-7, vmax=7)"
]
},
{
Expand Down
31 changes: 14 additions & 17 deletions docs/source/notebooks/misty.ipynb
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Expand Up @@ -542,7 +542,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -562,12 +562,12 @@
],
"source": [
"# obtain genesets\n",
"progeny = dc.get_progeny(organism='human', top=500)"
"progeny = dc.op.progeny(organism='human', top=500)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -587,25 +587,22 @@
],
"source": [
"# use multivariate linear model to estimate activity\n",
"dc.run_mlm(\n",
"dc.mt.mlm(\n",
" mat=adata,\n",
" net=progeny,\n",
" source='source',\n",
" target='target',\n",
" weight='weight',\n",
" verbose=True,\n",
" use_raw=False,\n",
" raw=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# extract progeny activities as an AnnData object\n",
"acts_progeny = li.ut.obsm_to_adata(adata, 'mlm_estimate')"
"acts_progeny = li.ut.obsm_to_adata(adata, 'score_mlm')"
]
},
{
Expand Down Expand Up @@ -1300,17 +1297,17 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get TF prior knowledge\n",
"net = dc.get_collectri()"
"net = dc.op.collectri(organism='human', remove_complexes=False, license='academic', verbose=False)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -1330,22 +1327,22 @@
],
"source": [
"# Estimate activities\n",
"dc.run_ulm(\n",
"dc.mt.ulm(\n",
" mat=adata,\n",
" net=net,\n",
" verbose=True,\n",
" use_raw=False,\n",
" raw=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# extract activities\n",
"acts_tfs = li.ut.obsm_to_adata(adata, 'ulm_estimate')"
"acts_tfs = li.ut.obsm_to_adata(adata, 'score_ulm')"
]
},
{
Expand Down
246 changes: 180 additions & 66 deletions docs/source/notebooks/mofacellular.ipynb
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34 changes: 14 additions & 20 deletions docs/source/notebooks/mofatalk.ipynb
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Expand Up @@ -933,7 +933,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -953,23 +953,19 @@
}
],
"source": [
"dc.get_metadata_associations(\n",
"dc.tl.rankby_obsm(\n",
" mdata,\n",
" obs_keys = ['patient', 'condition'], # Metadata columns to associate to PCs\n",
" obsm_key='X_mofa', # Where the PCs are stored\n",
" uns_key='mofa_anova', # Where the results are stored\n",
" inplace=True,\n",
" key='X_mofa', # Where the PCs are stored\n",
" uns_key='rank_obsm', # Where the results are stored\n",
")\n",
"\n",
"dc.plot_associations(\n",
"dc.pl.obsm(\n",
" mdata,\n",
" uns_key='mofa_anova', # Summary statistics from the anova tests\n",
" obsm_key='X_mofa', # where the PCs are stored\n",
" stat_col='p_adj', # Which summary statistic to plot\n",
" obs_annotation_cols = ['patient', 'condition'], # which sample annotations to plot\n",
" key='rank_obsm',\n",
" names = ['patient', 'condition'], # which sample annotations to plot\n",
" titles=['Principle component scores', 'Adjusted p-values from ANOVA'],\n",
" figsize=(7, 5),\n",
" n_factors=10,\n",
" nvar=10\n",
")"
]
},
Expand Down Expand Up @@ -1532,7 +1528,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -1551,14 +1547,14 @@
],
"source": [
"# load PROGENy pathways\n",
"net = dc.get_progeny(organism='human', top=5000)\n",
"net = dc.op.progeny(organism='human', top=5000)\n",
"# load full list of ligand-receptor pairs\n",
"lr_pairs = li.resource.select_resource('consensus')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1638,7 +1634,7 @@
],
"source": [
"# generate ligand-receptor geneset\n",
"lr_progeny = li.rs.generate_lr_geneset(lr_pairs, net, lr_sep=\"^\")\n",
"lr_progeny = li.rs.generate_lr_geneset(lr_pairs, net, lr_sep=\"^\").rename(columns = {'interaction': 'target'})\n",
"lr_progeny.head()"
]
},
Expand Down Expand Up @@ -1897,14 +1893,12 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# run pathway enrichment analysis\n",
"estimate, pvals = dc.run_mlm(lr_loadings.transpose(), lr_progeny,\n",
" source=\"source\", target=\"interaction\",\n",
" use_raw=False, min_n=5)\n",
"estimate, pvals = dc.op.mlm(lr_loadings.transpose(), lr_progeny, raw=False, tmin=5)\n",
"# pivot columns to long\n",
"estimate = (estimate.\n",
" melt(ignore_index=False, value_name='estimate', var_name='pathway').\n",
Expand Down
10 changes: 5 additions & 5 deletions docs/source/notebooks/prior_knowledge.ipynb
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Expand Up @@ -757,7 +757,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -850,7 +850,7 @@
],
"source": [
"# load PROGENy pathways, we use decoupler as a proxy as it formats the data in a more convenient way\n",
"progeny = dc.get_progeny(top=2500)\n",
"progeny = dc.op.progeny(top=2500)\n",
"progeny.head()"
]
},
Expand All @@ -873,7 +873,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1412,7 +1412,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -1497,7 +1497,7 @@
}
],
"source": [
"dc.get_collectri().head()"
"dc.op.collectri(organism='human', remove_complexes=False, license='academic', verbose=False).head()"
]
},
{
Expand Down
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