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refactor: extract burden-summary renderer from burden_test (PR-5) #183

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Merged
enriquea merged 1 commit into from
Jun 2, 2026
Merged

refactor: extract burden-summary renderer from burden_test (PR-5) #183

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206 changes: 107 additions & 99 deletions hvantk/tools/enrichex/burden_cli.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,112 @@ def register_burden_commands(group):
group.add_command(burden_pipeline_cmd)


def _render_burden_summary(result_df, alpha, phenotype_type: str) -> None:
"""Print the single-class burden analysis summary and top-significant table.

Pure presentation extracted from ``burden_test``: counts significant gene
sets and renders an ASCII table of the top hits, with columns chosen by
phenotype type (odds ratio for binary, standard error for continuous).
"""
n_significant = result_df["significant"].sum()
click.echo("\n" + "=" * 60)
click.echo("BURDEN ANALYSIS SUMMARY")
click.echo("=" * 60)
click.echo(f"Total gene sets tested: {len(result_df)}")
click.echo(f"Significant gene sets (p_adj < {alpha}): {n_significant}")

if n_significant > 0:
click.echo(f"\nTop significant gene sets:")
click.echo("-" * 60)

# Select columns based on phenotype type
if phenotype_type == "binary":
display_cols = [
"gene_set_name",
"beta",
"odds_ratio",
"p_value",
"p_adjusted",
]
else:
display_cols = [
"gene_set_name",
"beta",
"standard_error",
"p_value",
"p_adjusted",
]

# Filter to available columns
available_cols = [c for c in display_cols if c in result_df.columns]
top = result_df[result_df["significant"]].head(5)[available_cols]

# Format for display
top_display = top.copy()
if "beta" in top_display.columns:
top_display["beta"] = top_display["beta"].apply(lambda x: f"{x:.4f}")
if "odds_ratio" in top_display.columns:
top_display["odds_ratio"] = top_display["odds_ratio"].apply(
lambda x: f"{x:.2f}"
)
if "standard_error" in top_display.columns:
top_display["standard_error"] = top_display["standard_error"].apply(
lambda x: f"{x:.4f}"
)
top_display["p_value"] = top_display["p_value"].apply(lambda x: f"{x:.2e}")
top_display["p_adjusted"] = top_display["p_adjusted"].apply(lambda x: f"{x:.2e}")

# Print table
if phenotype_type == "binary":
header = " ".join(
[
"Gene Set".ljust(20),
"Beta".ljust(8),
"OR".ljust(8),
"P-value".ljust(10),
"P-adj".ljust(10),
]
)
else:
header = " ".join(
[
"Gene Set".ljust(20),
"Beta".ljust(8),
"SE".ljust(8),
"P-value".ljust(10),
"P-adj".ljust(10),
]
)

click.echo(header)
click.echo("-" * 60)

for _, row in top_display.iterrows():
if phenotype_type == "binary":
line = " ".join(
[
str(row["gene_set_name"])[:20].ljust(20),
str(row["beta"]).ljust(8),
str(row.get("odds_ratio", "N/A")).ljust(8),
str(row["p_value"]).ljust(10),
str(row["p_adjusted"]).ljust(10),
]
)
else:
line = " ".join(
[
str(row["gene_set_name"])[:20].ljust(20),
str(row["beta"]).ljust(8),
str(row.get("standard_error", "N/A")).ljust(8),
str(row["p_value"]).ljust(10),
str(row["p_adjusted"]).ljust(10),
]
)
click.echo(line)

click.echo("=" * 60 + "\n")


@click.command(name="burden")
@click.option(
"-m",
Expand Down Expand Up @@ -621,105 +727,7 @@ def burden_test(
click.echo(f"\n Results written to: {results_path}")

# Summary statistics
n_significant = result_df["significant"].sum()
click.echo("\n" + "=" * 60)
click.echo("BURDEN ANALYSIS SUMMARY")
click.echo("=" * 60)
click.echo(f"Total gene sets tested: {len(result_df)}")
click.echo(f"Significant gene sets (p_adj < {alpha}): {n_significant}")

if n_significant > 0:
click.echo(f"\nTop significant gene sets:")
click.echo("-" * 60)

# Select columns based on phenotype type
if phenotype_type == "binary":
display_cols = [
"gene_set_name",
"beta",
"odds_ratio",
"p_value",
"p_adjusted",
]
else:
display_cols = [
"gene_set_name",
"beta",
"standard_error",
"p_value",
"p_adjusted",
]

# Filter to available columns
available_cols = [c for c in display_cols if c in result_df.columns]
top = result_df[result_df["significant"]].head(5)[available_cols]

# Format for display
top_display = top.copy()
if "beta" in top_display.columns:
top_display["beta"] = top_display["beta"].apply(lambda x: f"{x:.4f}")
if "odds_ratio" in top_display.columns:
top_display["odds_ratio"] = top_display["odds_ratio"].apply(
lambda x: f"{x:.2f}"
)
if "standard_error" in top_display.columns:
top_display["standard_error"] = top_display["standard_error"].apply(
lambda x: f"{x:.4f}"
)
top_display["p_value"] = top_display["p_value"].apply(lambda x: f"{x:.2e}")
top_display["p_adjusted"] = top_display["p_adjusted"].apply(
lambda x: f"{x:.2e}"
)

# Print table
if phenotype_type == "binary":
header = " ".join(
[
"Gene Set".ljust(20),
"Beta".ljust(8),
"OR".ljust(8),
"P-value".ljust(10),
"P-adj".ljust(10),
]
)
else:
header = " ".join(
[
"Gene Set".ljust(20),
"Beta".ljust(8),
"SE".ljust(8),
"P-value".ljust(10),
"P-adj".ljust(10),
]
)

click.echo(header)
click.echo("-" * 60)

for _, row in top_display.iterrows():
if phenotype_type == "binary":
line = " ".join(
[
str(row["gene_set_name"])[:20].ljust(20),
str(row["beta"]).ljust(8),
str(row.get("odds_ratio", "N/A")).ljust(8),
str(row["p_value"]).ljust(10),
str(row["p_adjusted"]).ljust(10),
]
)
else:
line = " ".join(
[
str(row["gene_set_name"])[:20].ljust(20),
str(row["beta"]).ljust(8),
str(row.get("standard_error", "N/A")).ljust(8),
str(row["p_value"]).ljust(10),
str(row["p_adjusted"]).ljust(10),
]
)
click.echo(line)

click.echo("=" * 60 + "\n")
_render_burden_summary(result_df, alpha, phenotype_type)

# Run permutation test if requested
if permutation:
Expand Down
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