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chore: modernize get_version and remove types-pkg_resources #77

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12 changes: 3 additions & 9 deletions efaar_benchmarking/core.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,6 @@
from importlib.metadata import version


def get_version() -> str:
"""Returns a string representation of the version of efaar_benchmarking currently in use

Expand All @@ -7,15 +10,6 @@ def get_version() -> str:
the version number installed of this package
"""
try:
from importlib.metadata import version # type: ignore

return version("efaar_benchmarking")
except ImportError:
try:
import pkg_resources

return pkg_resources.get_distribution("efaar_benchmarking").version
except pkg_resources.DistributionNotFound:
return "set_version_placeholder"
except ModuleNotFoundError:
return "set_version_placeholder"
135 changes: 108 additions & 27 deletions notebooks/map_building_benchmarking.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -21,9 +21,11 @@
"import pickle\n",
"\n",
"pc_count = 128\n",
"save_results = False # Results already uploaded to the notebooks/data folder in the repo. If True, will replace these files.\n",
"save_results = (\n",
" False # Results already uploaded to the notebooks/data folder in the repo. If True, will replace these files.\n",
")\n",
"pert_signal_pval_cutoff = 0.05\n",
"recall_thr_pairs = [(.05, .95)]"
"recall_thr_pairs = [(0.05, 0.95)]"
]
},
{
Expand Down Expand Up @@ -53,17 +55,43 @@
"# Run EFAAR pipelines\n",
"all_embeddings_pre_agg = {}\n",
"print(\"Running for embedding size\", pc_count)\n",
"all_embeddings_pre_agg[f\"scVI{pc_count}\"] = embed_by_scvi_anndata(adata_raw, batch_col=gem_group_colname, n_latent=pc_count, n_hidden=pc_count*2)\n",
"all_embeddings_pre_agg[f\"scVI{pc_count}\"] = embed_by_scvi_anndata(\n",
" adata_raw, batch_col=gem_group_colname, n_latent=pc_count, n_hidden=pc_count * 2\n",
")\n",
"print(\"embed_by_scvi_anndata completed\")\n",
"all_embeddings_pre_agg[f\"scVI{pc_count}-CS\"] = centerscale_on_controls(all_embeddings_pre_agg[f\"scVI{pc_count}\"], metadata, pert_col=pert_colname, control_key=control_key, batch_col=gem_group_colname)\n",
"all_embeddings_pre_agg[f\"scVI{pc_count}-CS\"] = centerscale_on_controls(\n",
" all_embeddings_pre_agg[f\"scVI{pc_count}\"],\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" control_key=control_key,\n",
" batch_col=gem_group_colname,\n",
")\n",
"print(\"centerscale completed\")\n",
"all_embeddings_pre_agg[f\"scVI{pc_count}-TVN\"] = tvn_on_controls(all_embeddings_pre_agg[f\"scVI{pc_count}\"], metadata, pert_col=pert_colname, control_key=control_key, batch_col=gem_group_colname)\n",
"all_embeddings_pre_agg[f\"scVI{pc_count}-TVN\"] = tvn_on_controls(\n",
" all_embeddings_pre_agg[f\"scVI{pc_count}\"],\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" control_key=control_key,\n",
" batch_col=gem_group_colname,\n",
")\n",
"print(\"tvn completed\")\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}\"] = embed_by_pca_anndata(adata_raw, gem_group_colname, pc_count)\n",
"print(\"embed_by_pca_anndata completed\")\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-CS\"] = centerscale_on_controls(all_embeddings_pre_agg[f\"PCA{pc_count}\"], metadata, pert_col=pert_colname, control_key=control_key, batch_col=gem_group_colname)\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-CS\"] = centerscale_on_controls(\n",
" all_embeddings_pre_agg[f\"PCA{pc_count}\"],\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" control_key=control_key,\n",
" batch_col=gem_group_colname,\n",
")\n",
"print(\"centerscale completed\")\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-TVN\"] = tvn_on_controls(all_embeddings_pre_agg[f\"PCA{pc_count}\"], metadata, pert_col=pert_colname, control_key=control_key, batch_col=gem_group_colname)\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-TVN\"] = tvn_on_controls(\n",
" all_embeddings_pre_agg[f\"PCA{pc_count}\"],\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" control_key=control_key,\n",
" batch_col=gem_group_colname,\n",
")\n",
"print(\"tvn completed\")\n",
"\n",
"# Run biological relationship benchmarks\n",
Expand All @@ -77,9 +105,9 @@
"\n",
"# Save results\n",
"if save_results:\n",
" with open(f'data/{dataset}_map_cache.pkl', 'wb') as f:\n",
" with open(f\"data/{dataset}_map_cache.pkl\", \"wb\") as f:\n",
" pickle.dump(map_data, f) # storing the PCA-TVN map data for downstream analysis\n",
" with open(f'data/{dataset}_metadata.pkl', 'wb') as f:\n",
" with open(f\"data/{dataset}_metadata.pkl\", \"wb\") as f:\n",
" pickle.dump(metadata, f) # storing the metadata for downstream analysis"
]
},
Expand Down Expand Up @@ -109,26 +137,54 @@
"features, metadata = filter_cell_profiler_features(features, metadata)\n",
"\n",
"expression_data_folder = \"../efaar_benchmarking/expression_data\"\n",
"expr = pd.read_csv(f\"{expression_data_folder}/U2OS_expression.csv\", index_col=0).groupby(\"gene\").zfpkm.agg(\"median\").reset_index()\n",
"expr = (\n",
" pd.read_csv(f\"{expression_data_folder}/U2OS_expression.csv\", index_col=0)\n",
" .groupby(\"gene\")\n",
" .zfpkm.agg(\"median\")\n",
" .reset_index()\n",
")\n",
"unexpr_genes = list(expr.loc[expr.zfpkm < -3, \"gene\"])\n",
"expr_genes = list(expr.loc[expr.zfpkm >= -3, \"gene\"])\n",
"expr_ind = metadata[pert_colname].isin(expr_genes + [control_key])\n",
"\n",
"# Run EFAAR pipelines\n",
"all_embeddings_pre_agg = {}\n",
"print(\"Computing PCA embedding for\", pc_count, \"dimensions...\")\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}\"] = embed_by_pca(features.values, metadata, variance_or_ncomp=pc_count, batch_col=plate_colname)\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}\"] = embed_by_pca(\n",
" features.values, metadata, variance_or_ncomp=pc_count, batch_col=plate_colname\n",
")\n",
"print(\"Computing centerscale...\")\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-CS\"] = centerscale_on_controls(all_embeddings_pre_agg[f\"PCA{pc_count}\"], metadata, pert_col=pert_colname, control_key=control_key, batch_col=run_colname)\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-CS\"] = centerscale_on_controls(\n",
" all_embeddings_pre_agg[f\"PCA{pc_count}\"],\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" control_key=control_key,\n",
" batch_col=run_colname,\n",
")\n",
"print(\"Computing TVN...\")\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-TVN\"] = tvn_on_controls(all_embeddings_pre_agg[f\"PCA{pc_count}\"], metadata, pert_col=pert_colname, control_key=control_key, batch_col=run_colname)\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-TVN\"] = tvn_on_controls(\n",
" all_embeddings_pre_agg[f\"PCA{pc_count}\"],\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" control_key=control_key,\n",
" batch_col=run_colname,\n",
")\n",
"\n",
"# Run perturbation signal benchmarks\n",
"for k, emb in all_embeddings_pre_agg.items():\n",
" cons_res = pert_signal_consistency_benchmark(emb, metadata, pert_col=pert_colname, neg_ctrl_perts=unexpr_genes, keys_to_drop=all_controls)\n",
" cons_res = pert_signal_consistency_benchmark(\n",
" emb, metadata, pert_col=pert_colname, neg_ctrl_perts=unexpr_genes, keys_to_drop=all_controls\n",
" )\n",
" print(k, round(sum(cons_res.pval <= pert_signal_pval_cutoff) / sum(~pd.isna(cons_res.pval)) * 100, 1))\n",
"\n",
" magn_res = pert_signal_distance_benchmark(emb, metadata, pert_col=pert_colname, neg_ctrl_perts=unexpr_genes, control_key=control_key, keys_to_drop=[x for x in all_controls if x!=control_key])\n",
" magn_res = pert_signal_distance_benchmark(\n",
" emb,\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" neg_ctrl_perts=unexpr_genes,\n",
" control_key=control_key,\n",
" keys_to_drop=[x for x in all_controls if x != control_key],\n",
" )\n",
" print(k, round(sum(magn_res.pval <= pert_signal_pval_cutoff) / sum(~pd.isna(magn_res.pval)) * 100, 1))\n",
"\n",
"# Run biological relationship benchmarks\n",
Expand All @@ -137,14 +193,14 @@
" print(\"Aggregating...\")\n",
" map_data = aggregate(emb[expr_ind], metadata[expr_ind], pert_col=pert_colname, keys_to_remove=all_controls)\n",
" print(\"Computing recall...\")\n",
" metrics = known_relationship_benchmark(map_data, recall_thr_pairs=[(.05, .95)], pert_col=pert_colname)\n",
" metrics = known_relationship_benchmark(map_data, recall_thr_pairs=[(0.05, 0.95)], pert_col=pert_colname)\n",
" print(metrics[list(metrics.columns)[::-1]])\n",
"\n",
"# Save results\n",
"if save_results:\n",
" with open(f'data/{dataset}_map_cache.pkl', 'wb') as f:\n",
" with open(f\"data/{dataset}_map_cache.pkl\", \"wb\") as f:\n",
" pickle.dump(map_data, f) # storing the PCA-TVN map data for downstream analysis\n",
" with open(f'data/{dataset}_metadata.pkl', 'wb') as f:\n",
" with open(f\"data/{dataset}_metadata.pkl\", \"wb\") as f:\n",
" pickle.dump(metadata, f) # storing the metadata for downstream analysis"
]
},
Expand Down Expand Up @@ -172,7 +228,9 @@
"print(\"Perturbation dataset loaded\")\n",
"\n",
"expression_data_folder = \"../efaar_benchmarking/expression_data\"\n",
"expr = pd.read_csv(f\"{expression_data_folder}/HeLa_expression.csv\") # note that we assume the HeLa expression data was used for PERISCOPE which is the default option in load_periscope()\n",
"expr = pd.read_csv(\n",
" f\"{expression_data_folder}/HeLa_expression.csv\"\n",
") # note that we assume the HeLa expression data was used for PERISCOPE which is the default option in load_periscope()\n",
"expr.columns = [\"gene\", \"tpm\"]\n",
"expr.gene = expr.gene.apply(lambda x: x.split(\" \")[0])\n",
"unexpr_genes = list(expr.loc[expr.tpm == 0, \"gene\"])\n",
Expand All @@ -182,18 +240,41 @@
"# Run EFAAR pipelines\n",
"all_embeddings_pre_agg = {}\n",
"print(\"Computing PCA embedding for\", pc_count, \"dimensions...\")\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}\"] = embed_by_pca(features.values, metadata, variance_or_ncomp=pc_count, batch_col=plate_colname)\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}\"] = embed_by_pca(\n",
" features.values, metadata, variance_or_ncomp=pc_count, batch_col=plate_colname\n",
")\n",
"print(\"Computing centerscale...\")\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-CS\"] = centerscale_on_controls(all_embeddings_pre_agg[f\"PCA{pc_count}\"], metadata, pert_col=pert_colname, control_key=control_key, batch_col=plate_colname)\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-CS\"] = centerscale_on_controls(\n",
" all_embeddings_pre_agg[f\"PCA{pc_count}\"],\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" control_key=control_key,\n",
" batch_col=plate_colname,\n",
")\n",
"print(\"Computing TVN...\")\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-TVN\"] = tvn_on_controls(all_embeddings_pre_agg[f\"PCA{pc_count}\"], metadata, pert_col=pert_colname, control_key=control_key, batch_col=plate_colname)\n",
"all_embeddings_pre_agg[f\"PCA{pc_count}-TVN\"] = tvn_on_controls(\n",
" all_embeddings_pre_agg[f\"PCA{pc_count}\"],\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" control_key=control_key,\n",
" batch_col=plate_colname,\n",
")\n",
"\n",
"# Run perturbation signal benchmarks\n",
"for k, emb in all_embeddings_pre_agg.items():\n",
" cons_res = pert_signal_consistency_benchmark(emb, metadata, pert_col=pert_colname, neg_ctrl_perts=unexpr_genes, keys_to_drop=all_controls)\n",
" cons_res = pert_signal_consistency_benchmark(\n",
" emb, metadata, pert_col=pert_colname, neg_ctrl_perts=unexpr_genes, keys_to_drop=all_controls\n",
" )\n",
" print(k, round(sum(cons_res.pval <= pert_signal_pval_cutoff) / sum(~pd.isna(cons_res.pval)) * 100, 1))\n",
"\n",
" magn_res = pert_signal_distance_benchmark(emb, metadata, pert_col=pert_colname, neg_ctrl_perts=unexpr_genes, control_key=control_key, keys_to_drop=[x for x in all_controls if x!=control_key])\n",
" magn_res = pert_signal_distance_benchmark(\n",
" emb,\n",
" metadata,\n",
" pert_col=pert_colname,\n",
" neg_ctrl_perts=unexpr_genes,\n",
" control_key=control_key,\n",
" keys_to_drop=[x for x in all_controls if x != control_key],\n",
" )\n",
" print(k, round(sum(magn_res.pval <= pert_signal_pval_cutoff) / sum(~pd.isna(magn_res.pval)) * 100, 1))\n",
"\n",
"# Run biological relationship benchmarks\n",
Expand All @@ -202,14 +283,14 @@
" print(\"Aggregating...\")\n",
" map_data = aggregate(emb[expr_ind], metadata[expr_ind], pert_col=pert_colname, keys_to_remove=all_controls)\n",
" print(\"Computing recall...\")\n",
" metrics = known_relationship_benchmark(map_data, recall_thr_pairs=[(.05, .95)], pert_col=pert_colname)\n",
" metrics = known_relationship_benchmark(map_data, recall_thr_pairs=[(0.05, 0.95)], pert_col=pert_colname)\n",
" print(metrics[list(metrics.columns)[::-1]])\n",
"\n",
"# Save results\n",
"if save_results:\n",
" with open(f'data/{dataset}_map_cache.pkl', 'wb') as f:\n",
" with open(f\"data/{dataset}_map_cache.pkl\", \"wb\") as f:\n",
" pickle.dump(map_data, f) # storing the PCA-TVN map data for downstream analysis\n",
" with open(f'data/{dataset}_metadata.pkl', 'wb') as f:\n",
" with open(f\"data/{dataset}_metadata.pkl\", \"wb\") as f:\n",
" pickle.dump(metadata, f) # storing the metadata for downstream analysis"
]
}
Expand Down
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