kfukuta/wf-transcriptomes-v202
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Merge branch 'cw-7250' into 'dev'

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[CW-7250] fix up typography

See merge request epi2melabs/workflows/wf-transcriptomes!270
This commit is contained in:
Chris Wright 2026-05-18 15:14:53 +00:00
commit 27f475d21a
2 changed files with 329 additions and 297 deletions
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70
bin/workflow_glue/report.py
View File

@ -4,7 +4,7 @@ import json
import math
from pathlib import Path
from dominate.tags import div, h3, p, pre, strong
from dominate.tags import div, h4, p, pre, strong
from dominate.util import raw
from ezcharts.components import fastcat
from ezcharts.components.reports import labs
@ -317,7 +317,8 @@ def main(args):
DataTable.from_pandas(
pd.DataFrame.from_dict(item, orient="index", columns=["Value"])
.reset_index()
.rename(columns={"index": "Field"})
.rename(columns={"index": "Field"}),
use_index=False,
)
# Load bambu QC statistics
@ -362,9 +363,10 @@ def main(args):
pd.DataFrame(seqname_rows, columns=["Check", "Value"]),
paging=False,
searchable=False,
use_index=False,
)
with h3("Build and Provider Hints"):
h4("Build and Provider Hints")
hint_rows = [
(
"Reference build",
@ -403,11 +405,12 @@ def main(args):
pd.DataFrame(hint_rows, columns=["Evidence", "Hints"]),
paging=False,
searchable=False,
use_index=False,
)
examples = annotation_summary.get("unstranded_examples") or []
if examples:
with h3("Unstranded Annotation Examples"):
h4("Unstranded Annotation Examples")
pre("\n".join(examples))
# Add Bambu QC section with warnings
@ -422,8 +425,7 @@ def main(args):
level="warning",
)
# Library size statistics
with h3("Library Size Statistics"):
h4("Library Size Statistics")
lib_stats = pd.DataFrame(
[
("Samples analyzed", bambu_qc.get("samples", "N/A")),
@ -460,10 +462,15 @@ def main(args):
],
columns=["Metric", "Value"],
)
DataTable.from_pandas(lib_stats, paging=False, searchable=False)
DataTable.from_pandas(
lib_stats,
paging=False,
searchable=False,
use_index=False,
)
# Transcript discovery statistics
with h3("Transcript Discovery"):
h4("Transcript Discovery")
discovery_stats = pd.DataFrame(
[
(
@ -494,11 +501,16 @@ def main(args):
],
columns=["Metric", "Value"],
)
DataTable.from_pandas(discovery_stats, paging=False, searchable=False)
DataTable.from_pandas(
discovery_stats,
paging=False,
searchable=False,
use_index=False,
)
# Per-sample library sizes
if "library_sizes" in bambu_qc and bambu_qc["library_sizes"]:
with h3("Per-Sample Library Sizes"):
h4("Per-Sample Library Sizes")
lib_size_data = []
for sample, size in bambu_qc["library_sizes"].items():
numeric_size = _coerce_float(size)
@ -523,9 +535,19 @@ def main(args):
with report.add_section("Cohort transcriptome", "Cohort"):
cohort_metrics, cohort_classes = _cohort_summary(args.cohort_dir)
if cohort_metrics is not None:
DataTable.from_pandas(cohort_metrics, paging=False, searchable=False)
DataTable.from_pandas(
cohort_metrics,
paging=False,
searchable=False,
use_index=False,
)
if cohort_classes is not None:
DataTable.from_pandas(cohort_classes, paging=False, searchable=False)
DataTable.from_pandas(
cohort_classes,
paging=False,
searchable=False,
use_index=False,
)
tx_counts = _read_table(Path(args.cohort_dir) / "transcript_counts.tsv")
if tx_counts is not None and not tx_counts.empty:
@ -536,7 +558,12 @@ def main(args):
tabs = Tabs()
for sample, summary_df in _sample_summaries(args.samples_dir).items():
with tabs.add_tab(sample):
DataTable.from_pandas(summary_df, paging=False, searchable=False)
DataTable.from_pandas(
summary_df,
paging=False,
searchable=False,
use_index=False,
)
if args.alignment_stats_dir and Path(args.alignment_stats_dir).exists():
with report.add_section("Alignment statistics", "Alignments"):
@ -656,7 +683,7 @@ def main(args):
has_warnings = True
# Experimental design summary
with h3("Experimental Design"):
h4("Experimental Design")
covariates = _as_string_list(de_qc.get("covariates"))
covariates_value = ", ".join(covariates) if covariates else "none"
design_stats = pd.DataFrame(
@ -678,11 +705,16 @@ def main(args):
],
columns=["Parameter", "Value"],
)
DataTable.from_pandas(design_stats, paging=False, searchable=False)
DataTable.from_pandas(
design_stats,
paging=False,
searchable=False,
use_index=False,
)
# Sample sizes per group
if "samples_per_group" in de_qc:
with h3("Sample Sizes per Group"):
h4("Sample Sizes per Group")
sample_size_data = []
for group, count in de_qc["samples_per_group"].items():
status = (
@ -710,7 +742,7 @@ def main(args):
use_index=False,
)
with h3("Statistical Methods & Warnings"):
h4("Statistical Methods & Warnings")
if method_rows:
method_df = pd.DataFrame(method_rows)
DataTable.from_pandas(
@ -723,7 +755,7 @@ def main(args):
# Per-contrast summary
if "contrasts" in de_qc:
with h3("Results Summary by Contrast"):
h4("Results Summary by Contrast")
contrast_summary_data = []
for contrast_name, contrast_data in de_qc["contrasts"].items():
dtu_genes = (
@ -774,7 +806,7 @@ def main(args):
# Warnings summary table
if has_warnings:
with h3("Quality Warnings Summary"):
h4("Quality Warnings Summary")
warnings_data = []
if sample_size_warnings:
warnings_data.append(

4
bin/workflow_glue/tests/common/test_report.py
View File

@ -359,7 +359,7 @@ def test_report_main_renders_statistical_methods_and_warnings(
monkeypatch.setattr(report.fastcat, "SeqSummary", lambda *args, **kwargs: None)
monkeypatch.setattr(
report,
"h3",
"h4",
lambda label: (headings.append(label), _NullContext())[1],
)
monkeypatch.setattr(
@ -449,7 +449,7 @@ def test_report_main_tolerates_missing_statistical_fields(monkeypatch, tmp_path)
monkeypatch.setattr(report.fastcat, "SeqSummary", lambda *args, **kwargs: None)
monkeypatch.setattr(
report,
"h3",
"h4",
lambda label: (headings.append(label), _NullContext())[1],
)
monkeypatch.setattr(report, "_create_warning_banner", lambda *args, **kwargs: None)