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Close census connection in extract_czi_markers #8

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168 changes: 86 additions & 82 deletions spatialagent/tool/databases.py
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Original file line number Diff line number Diff line change
Expand Up @@ -447,88 +447,92 @@ def get_cellguide_file(relpth, snapshot=LATEST_SNAPSHOT):
# Read from CZI Census
census = cellxgene_census.open_soma(census_version="latest")

all_results = []

for did in dataset_ids:
# Query dataset
query = f'dataset_id == "{did}"'
adata = cellxgene_census.get_anndata(census, organism, obs_value_filter=query)

# Get cell types
cell_types = adata.obs["cell_type"].value_counts()

for cell_type, count in cell_types.items():
# Get cell_type_id with error handling for empty results
cell_type_mask = adata.obs["cell_type"] == cell_type
cell_type_ids = adata.obs[cell_type_mask]["cell_type_ontology_term_id"].values

if len(cell_type_ids) == 0:
continue # Skip if no cell_type_ontology_term_id found

cell_type_id = cell_type_ids[0]

# Get marker genes from CellGuide
# Note: CellGuide uses underscore format (CL_0000182) not colon format (CL:0000182)
cellguide_id = cell_type_id.replace(":", "_")

comp_genes = []
cano_genes = []

# Limit marker genes to top N for readability (CellGuide can return 500+ genes)
MAX_MARKER_GENES = 100

# Helper to convert gene symbols based on organism
# CellGuide returns mouse-format symbols (title case like 'Grin2b')
# Human genes should be uppercase (GRIN2B), mouse stays title case
def normalize_gene_symbol(gene: str) -> str:
if organism == "Homo sapiens":
return gene.upper()
return gene # Keep mouse format as-is

try:
comp_markers = get_cellguide_file(f"computational_marker_genes/{cellguide_id}.json")
if comp_markers.status_code == 200 and comp_markers.text:
comp_markers_df = pd.DataFrame.from_records(comp_markers.json())
# Gene symbol is in 'symbol' column, not 'marker_gene'
if "symbol" in comp_markers_df.columns:
comp_genes = [normalize_gene_symbol(g) for g in comp_markers_df["symbol"].tolist()[:MAX_MARKER_GENES]]
elif "marker_gene" in comp_markers_df.columns:
comp_genes = [normalize_gene_symbol(g) for g in comp_markers_df["marker_gene"].tolist()[:MAX_MARKER_GENES]]
except Exception:
pass # CellGuide may not have data for all cell types

try:
cano_markers = get_cellguide_file(f"canonical_marker_genes/{cellguide_id}.json")
if cano_markers.status_code == 200 and cano_markers.text:
cano_markers_df = pd.DataFrame.from_records(cano_markers.json())
if "symbol" in cano_markers_df.columns:
cano_genes = [normalize_gene_symbol(g) for g in cano_markers_df["symbol"].tolist()[:MAX_MARKER_GENES]]
elif "marker_gene" in cano_markers_df.columns:
cano_genes = [normalize_gene_symbol(g) for g in cano_markers_df["marker_gene"].tolist()[:MAX_MARKER_GENES]]
except Exception:
pass

all_results.append({
"cell_type": cell_type,
"cell_type_id": cell_type_id,
"n_cells": count,
"marker_genes": comp_genes,
"cano_marker_genes": cano_genes,
})

# Save
df_results = pd.DataFrame(all_results)
df_results.to_csv(save_csv, index=False)

# Count how many cell types have marker genes
n_with_markers = sum(1 for r in all_results if r["marker_genes"] or r["cano_marker_genes"])
n_without_markers = len(all_results) - n_with_markers

msg = f"Successfully processed {len(dataset_ids)} CZI dataset(s) with {len(all_results)} cell types. Saved to {save_csv}"
if n_without_markers > 0:
msg += f"\nNote: {n_with_markers} cell types have marker genes from CellGuide, {n_without_markers} do not (may need PanglaoDB/CellMarker2 lookup)."

return msg
try:
all_results = []

for did in dataset_ids:
# Query dataset
query = f'dataset_id == "{did}"'
adata = cellxgene_census.get_anndata(census, organism, obs_value_filter=query)

# Get cell types
cell_types = adata.obs["cell_type"].value_counts()

for cell_type, count in cell_types.items():
# Get cell_type_id with error handling for empty results
cell_type_mask = adata.obs["cell_type"] == cell_type
cell_type_ids = adata.obs[cell_type_mask]["cell_type_ontology_term_id"].values

if len(cell_type_ids) == 0:
continue # Skip if no cell_type_ontology_term_id found

cell_type_id = cell_type_ids[0]

# Get marker genes from CellGuide
# Note: CellGuide uses underscore format (CL_0000182) not colon format (CL:0000182)
cellguide_id = cell_type_id.replace(":", "_")

comp_genes = []
cano_genes = []

# Limit marker genes to top N for readability (CellGuide can return 500+ genes)
MAX_MARKER_GENES = 100

# Helper to convert gene symbols based on organism
# CellGuide returns mouse-format symbols (title case like 'Grin2b')
# Human genes should be uppercase (GRIN2B), mouse stays title case
def normalize_gene_symbol(gene: str) -> str:
if organism == "Homo sapiens":
return gene.upper()
return gene # Keep mouse format as-is

try:
comp_markers = get_cellguide_file(f"computational_marker_genes/{cellguide_id}.json")
if comp_markers.status_code == 200 and comp_markers.text:
comp_markers_df = pd.DataFrame.from_records(comp_markers.json())
# Gene symbol is in 'symbol' column, not 'marker_gene'
if "symbol" in comp_markers_df.columns:
comp_genes = [normalize_gene_symbol(g) for g in comp_markers_df["symbol"].tolist()[:MAX_MARKER_GENES]]
elif "marker_gene" in comp_markers_df.columns:
comp_genes = [normalize_gene_symbol(g) for g in comp_markers_df["marker_gene"].tolist()[:MAX_MARKER_GENES]]
except Exception:
pass # CellGuide may not have data for all cell types

try:
cano_markers = get_cellguide_file(f"canonical_marker_genes/{cellguide_id}.json")
if cano_markers.status_code == 200 and cano_markers.text:
cano_markers_df = pd.DataFrame.from_records(cano_markers.json())
if "symbol" in cano_markers_df.columns:
cano_genes = [normalize_gene_symbol(g) for g in cano_markers_df["symbol"].tolist()[:MAX_MARKER_GENES]]
elif "marker_gene" in cano_markers_df.columns:
cano_genes = [normalize_gene_symbol(g) for g in cano_markers_df["marker_gene"].tolist()[:MAX_MARKER_GENES]]
except Exception:
pass

all_results.append({
"cell_type": cell_type,
"cell_type_id": cell_type_id,
"n_cells": count,
"marker_genes": comp_genes,
"cano_marker_genes": cano_genes,
})

# Save
df_results = pd.DataFrame(all_results)
df_results.to_csv(save_csv, index=False)

# Count how many cell types have marker genes
n_with_markers = sum(1 for r in all_results if r["marker_genes"] or r["cano_marker_genes"])
n_without_markers = len(all_results) - n_with_markers

msg = f"Successfully processed {len(dataset_ids)} CZI dataset(s) with {len(all_results)} cell types. Saved to {save_csv}"
if n_without_markers > 0:
msg += f"\nNote: {n_with_markers} cell types have marker genes from CellGuide, {n_without_markers} do not (may need PanglaoDB/CellMarker2 lookup)."

return msg

finally:
census.close()


# =============================================================================
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