[CW-7250] fix up typography
This commit is contained in:
Chris Wright
parent
0bd6f118b5
commit
68809a0147
@ -4,7 +4,7 @@ import json
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import math
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from pathlib import Path
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from dominate.tags import div, h3, p, pre, strong
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from dominate.tags import div, h4, p, pre, strong
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from dominate.util import raw
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from ezcharts.components import fastcat
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from ezcharts.components.reports import labs
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@ -317,7 +317,8 @@ def main(args):
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DataTable.from_pandas(
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pd.DataFrame.from_dict(item, orient="index", columns=["Value"])
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.reset_index()
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.rename(columns={"index": "Field"})
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.rename(columns={"index": "Field"}),
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use_index=False,
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)
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# Load bambu QC statistics
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@ -362,53 +363,55 @@ def main(args):
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pd.DataFrame(seqname_rows, columns=["Check", "Value"]),
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paging=False,
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searchable=False,
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use_index=False,
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)
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with h3("Build and Provider Hints"):
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hint_rows = [
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(
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"Reference build",
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_format_hint_values(
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annotation_reference_summary.get(
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"reference_build_hints", []
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)
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),
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h4("Build and Provider Hints")
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hint_rows = [
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(
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"Reference build",
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_format_hint_values(
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annotation_reference_summary.get(
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"reference_build_hints", []
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)
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),
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(
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"Annotation build",
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_format_hint_values(
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annotation_reference_summary.get(
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"annotation_build_hints", []
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)
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),
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),
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(
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"Annotation build",
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_format_hint_values(
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annotation_reference_summary.get(
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"annotation_build_hints", []
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)
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),
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(
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"Reference provider",
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_format_hint_values(
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annotation_reference_summary.get(
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"reference_provider_hints", []
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)
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),
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),
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(
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"Reference provider",
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_format_hint_values(
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annotation_reference_summary.get(
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"reference_provider_hints", []
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)
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),
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(
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"Annotation provider",
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_format_hint_values(
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annotation_reference_summary.get(
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"annotation_provider_hints", []
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)
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),
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),
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(
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"Annotation provider",
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_format_hint_values(
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annotation_reference_summary.get(
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"annotation_provider_hints", []
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)
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),
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]
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DataTable.from_pandas(
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pd.DataFrame(hint_rows, columns=["Evidence", "Hints"]),
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paging=False,
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searchable=False,
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)
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),
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]
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DataTable.from_pandas(
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pd.DataFrame(hint_rows, columns=["Evidence", "Hints"]),
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paging=False,
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searchable=False,
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use_index=False,
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)
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examples = annotation_summary.get("unstranded_examples") or []
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if examples:
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with h3("Unstranded Annotation Examples"):
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pre("\n".join(examples))
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h4("Unstranded Annotation Examples")
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pre("\n".join(examples))
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# Add Bambu QC section with warnings
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if bambu_qc:
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@ -422,110 +425,129 @@ def main(args):
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level="warning",
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)
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# Library size statistics
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with h3("Library Size Statistics"):
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lib_stats = pd.DataFrame(
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[
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("Samples analyzed", bambu_qc.get("samples", "N/A")),
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(
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"Median library size",
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"{} reads".format(
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_format_count_value(
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bambu_qc.get("median_library_size", 0)
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)
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),
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h4("Library Size Statistics")
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lib_stats = pd.DataFrame(
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[
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("Samples analyzed", bambu_qc.get("samples", "N/A")),
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(
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"Median library size",
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"{} reads".format(
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_format_count_value(
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bambu_qc.get("median_library_size", 0)
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)
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),
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(
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"Min library size",
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"{} reads".format(
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_format_count_value(
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bambu_qc.get("min_library_size", 0)
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)
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),
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),
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(
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"Min library size",
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"{} reads".format(
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_format_count_value(
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bambu_qc.get("min_library_size", 0)
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)
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),
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(
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"Max library size",
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"{} reads".format(
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_format_count_value(
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bambu_qc.get("max_library_size", 0)
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)
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),
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),
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(
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"Max library size",
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"{} reads".format(
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_format_count_value(
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bambu_qc.get("max_library_size", 0)
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)
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),
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(
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"Library size ratio (max/min)",
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_format_ratio_value(
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bambu_qc.get("library_size_ratio", 1.0)
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),
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),
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(
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"Library size ratio (max/min)",
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_format_ratio_value(
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bambu_qc.get("library_size_ratio", 1.0)
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),
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],
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columns=["Metric", "Value"],
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)
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DataTable.from_pandas(lib_stats, paging=False, searchable=False)
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),
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],
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columns=["Metric", "Value"],
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)
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DataTable.from_pandas(
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lib_stats,
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paging=False,
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searchable=False,
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use_index=False,
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)
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# Transcript discovery statistics
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with h3("Transcript Discovery"):
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discovery_stats = pd.DataFrame(
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[
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(
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"Transcriptome mode",
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bambu_qc.get("transcriptome_mode", "N/A"),
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),
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("NDR used", str(bambu_qc.get("ndr_used", "N/A"))),
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(
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"Transcripts before filtering",
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bambu_qc.get("total_transcripts_before_filter", 0),
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),
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(
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"Transcripts after filtering",
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bambu_qc.get("total_transcripts_after_filter", 0),
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),
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(
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"Transcripts removed",
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bambu_qc.get("transcripts_filtered", 0),
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),
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(
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"Median transcripts per sample",
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bambu_qc.get("median_transcripts_detected", 0),
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),
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(
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"Unique genes (after filter)",
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bambu_qc.get("total_genes_after_filter", 0),
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),
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],
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columns=["Metric", "Value"],
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)
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DataTable.from_pandas(discovery_stats, paging=False, searchable=False)
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h4("Transcript Discovery")
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discovery_stats = pd.DataFrame(
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[
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(
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"Transcriptome mode",
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bambu_qc.get("transcriptome_mode", "N/A"),
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),
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("NDR used", str(bambu_qc.get("ndr_used", "N/A"))),
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(
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"Transcripts before filtering",
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bambu_qc.get("total_transcripts_before_filter", 0),
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),
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(
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"Transcripts after filtering",
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bambu_qc.get("total_transcripts_after_filter", 0),
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),
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(
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"Transcripts removed",
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bambu_qc.get("transcripts_filtered", 0),
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),
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(
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"Median transcripts per sample",
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bambu_qc.get("median_transcripts_detected", 0),
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),
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(
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"Unique genes (after filter)",
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bambu_qc.get("total_genes_after_filter", 0),
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),
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],
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columns=["Metric", "Value"],
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)
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DataTable.from_pandas(
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discovery_stats,
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paging=False,
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searchable=False,
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use_index=False,
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)
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# Per-sample library sizes
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if "library_sizes" in bambu_qc and bambu_qc["library_sizes"]:
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with h3("Per-Sample Library Sizes"):
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lib_size_data = []
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for sample, size in bambu_qc["library_sizes"].items():
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numeric_size = _coerce_float(size)
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lib_size_data.append(
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{
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"Sample": sample,
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"Library Size": _format_count_value(size),
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"Reads": (
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numeric_size if numeric_size is not None else -1
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),
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}
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)
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lib_df = pd.DataFrame(lib_size_data).sort_values(
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"Reads", ascending=False
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)
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DataTable.from_pandas(
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lib_df[["Sample", "Library Size"]],
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paging=False,
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use_index=False,
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h4("Per-Sample Library Sizes")
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lib_size_data = []
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for sample, size in bambu_qc["library_sizes"].items():
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numeric_size = _coerce_float(size)
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lib_size_data.append(
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{
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"Sample": sample,
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"Library Size": _format_count_value(size),
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"Reads": (
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numeric_size if numeric_size is not None else -1
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),
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}
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)
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lib_df = pd.DataFrame(lib_size_data).sort_values(
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"Reads", ascending=False
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)
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DataTable.from_pandas(
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lib_df[["Sample", "Library Size"]],
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paging=False,
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use_index=False,
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)
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with report.add_section("Cohort transcriptome", "Cohort"):
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cohort_metrics, cohort_classes = _cohort_summary(args.cohort_dir)
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if cohort_metrics is not None:
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DataTable.from_pandas(cohort_metrics, paging=False, searchable=False)
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DataTable.from_pandas(
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cohort_metrics,
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paging=False,
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searchable=False,
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use_index=False,
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)
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if cohort_classes is not None:
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DataTable.from_pandas(cohort_classes, paging=False, searchable=False)
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DataTable.from_pandas(
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cohort_classes,
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paging=False,
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searchable=False,
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use_index=False,
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)
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tx_counts = _read_table(Path(args.cohort_dir) / "transcript_counts.tsv")
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if tx_counts is not None and not tx_counts.empty:
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@ -536,7 +558,12 @@ def main(args):
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tabs = Tabs()
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for sample, summary_df in _sample_summaries(args.samples_dir).items():
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with tabs.add_tab(sample):
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DataTable.from_pandas(summary_df, paging=False, searchable=False)
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DataTable.from_pandas(
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summary_df,
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paging=False,
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searchable=False,
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use_index=False,
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)
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if args.alignment_stats_dir and Path(args.alignment_stats_dir).exists():
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with report.add_section("Alignment statistics", "Alignments"):
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@ -656,184 +683,189 @@ def main(args):
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has_warnings = True
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# Experimental design summary
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with h3("Experimental Design"):
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covariates = _as_string_list(de_qc.get("covariates"))
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covariates_value = ", ".join(covariates) if covariates else "none"
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design_stats = pd.DataFrame(
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[
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("Total samples", de_qc.get("total_samples", 0)),
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(
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"Condition column",
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de_qc.get("condition_column", "N/A"),
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),
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(
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"Reference level",
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de_qc.get("reference_level", "N/A"),
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),
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("Covariates", covariates_value),
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(
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"Number of contrasts",
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de_qc.get("num_contrasts", 0),
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),
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],
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columns=["Parameter", "Value"],
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)
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DataTable.from_pandas(design_stats, paging=False, searchable=False)
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h4("Experimental Design")
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covariates = _as_string_list(de_qc.get("covariates"))
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covariates_value = ", ".join(covariates) if covariates else "none"
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design_stats = pd.DataFrame(
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[
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("Total samples", de_qc.get("total_samples", 0)),
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(
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"Condition column",
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de_qc.get("condition_column", "N/A"),
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),
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(
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"Reference level",
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de_qc.get("reference_level", "N/A"),
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),
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("Covariates", covariates_value),
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(
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"Number of contrasts",
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de_qc.get("num_contrasts", 0),
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),
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],
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columns=["Parameter", "Value"],
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)
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DataTable.from_pandas(
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design_stats,
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paging=False,
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searchable=False,
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use_index=False,
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)
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# Sample sizes per group
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if "samples_per_group" in de_qc:
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with h3("Sample Sizes per Group"):
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sample_size_data = []
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for group, count in de_qc["samples_per_group"].items():
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status = (
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"✓" if count >= 3 else "⚠️" if count >= 2 else "❌"
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)
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note = (
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"OK"
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if count >= 3
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else "Low power"
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if count >= 2
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else "Too few"
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)
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sample_size_data.append(
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{
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"Group": group,
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"Samples": count,
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"Status": status,
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"Note": note,
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}
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)
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sample_df = pd.DataFrame(sample_size_data)
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DataTable.from_pandas(
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sample_df,
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paging=False,
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use_index=False,
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h4("Sample Sizes per Group")
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sample_size_data = []
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for group, count in de_qc["samples_per_group"].items():
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status = (
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"✓" if count >= 3 else "⚠️" if count >= 2 else "❌"
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)
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note = (
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"OK"
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if count >= 3
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else "Low power"
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if count >= 2
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else "Too few"
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)
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sample_size_data.append(
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{
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"Group": group,
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"Samples": count,
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"Status": status,
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"Note": note,
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}
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)
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sample_df = pd.DataFrame(sample_size_data)
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DataTable.from_pandas(
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sample_df,
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paging=False,
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use_index=False,
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)
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with h3("Statistical Methods & Warnings"):
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if method_rows:
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method_df = pd.DataFrame(method_rows)
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DataTable.from_pandas(
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method_df,
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paging=False,
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use_index=False,
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)
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else:
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p("No contrast-level QC metadata was found.")
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h4("Statistical Methods & Warnings")
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if method_rows:
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method_df = pd.DataFrame(method_rows)
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DataTable.from_pandas(
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method_df,
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paging=False,
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use_index=False,
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)
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else:
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p("No contrast-level QC metadata was found.")
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# Per-contrast summary
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if "contrasts" in de_qc:
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with h3("Results Summary by Contrast"):
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contrast_summary_data = []
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for contrast_name, contrast_data in de_qc["contrasts"].items():
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dtu_genes = (
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contrast_data.get("dtu_significant_genes", 0)
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if contrast_data.get("dtu_status") == "SUCCESS"
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else "N/A"
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)
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contrast_summary_data.append(
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{
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"Contrast": contrast_name,
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"Samples": (
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f"{contrast_data.get('n_target', 0)} "
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f"vs "
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f"{contrast_data.get('n_reference', 0)}"
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),
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"DGE Status": contrast_data.get(
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"dge_status", "N/A"
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),
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"DGE Significant (FDR<0.05)": (
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contrast_data.get(
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"dge_significant_fdr05", 0
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)
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if contrast_data.get("dge_status") == "SUCCESS"
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else "N/A"
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),
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"DGE Up": (
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contrast_data.get("dge_upregulated", 0)
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if contrast_data.get("dge_status") == "SUCCESS"
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else "N/A"
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),
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"DGE Down": (
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contrast_data.get("dge_downregulated", 0)
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if contrast_data.get("dge_status") == "SUCCESS"
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else "N/A"
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),
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"DTU Status": contrast_data.get(
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"dtu_status", "N/A"
|
||||
),
|
||||
"DTU Genes (q<0.05)": dtu_genes,
|
||||
}
|
||||
)
|
||||
contrast_summary_df = pd.DataFrame(contrast_summary_data)
|
||||
DataTable.from_pandas(
|
||||
contrast_summary_df,
|
||||
paging=False,
|
||||
use_index=False,
|
||||
h4("Results Summary by Contrast")
|
||||
contrast_summary_data = []
|
||||
for contrast_name, contrast_data in de_qc["contrasts"].items():
|
||||
dtu_genes = (
|
||||
contrast_data.get("dtu_significant_genes", 0)
|
||||
if contrast_data.get("dtu_status") == "SUCCESS"
|
||||
else "N/A"
|
||||
)
|
||||
contrast_summary_data.append(
|
||||
{
|
||||
"Contrast": contrast_name,
|
||||
"Samples": (
|
||||
f"{contrast_data.get('n_target', 0)} "
|
||||
f"vs "
|
||||
f"{contrast_data.get('n_reference', 0)}"
|
||||
),
|
||||
"DGE Status": contrast_data.get(
|
||||
"dge_status", "N/A"
|
||||
),
|
||||
"DGE Significant (FDR<0.05)": (
|
||||
contrast_data.get(
|
||||
"dge_significant_fdr05", 0
|
||||
)
|
||||
if contrast_data.get("dge_status") == "SUCCESS"
|
||||
else "N/A"
|
||||
),
|
||||
"DGE Up": (
|
||||
contrast_data.get("dge_upregulated", 0)
|
||||
if contrast_data.get("dge_status") == "SUCCESS"
|
||||
else "N/A"
|
||||
),
|
||||
"DGE Down": (
|
||||
contrast_data.get("dge_downregulated", 0)
|
||||
if contrast_data.get("dge_status") == "SUCCESS"
|
||||
else "N/A"
|
||||
),
|
||||
"DTU Status": contrast_data.get(
|
||||
"dtu_status", "N/A"
|
||||
),
|
||||
"DTU Genes (q<0.05)": dtu_genes,
|
||||
}
|
||||
)
|
||||
contrast_summary_df = pd.DataFrame(contrast_summary_data)
|
||||
DataTable.from_pandas(
|
||||
contrast_summary_df,
|
||||
paging=False,
|
||||
use_index=False,
|
||||
)
|
||||
|
||||
# Warnings summary table
|
||||
if has_warnings:
|
||||
with h3("Quality Warnings Summary"):
|
||||
warnings_data = []
|
||||
if sample_size_warnings:
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "Sample Size",
|
||||
"Details": "; ".join(sample_size_warnings),
|
||||
}
|
||||
)
|
||||
if deseq2_gene_wise or dexseq_gene_wise:
|
||||
engines = []
|
||||
if deseq2_gene_wise:
|
||||
engines.append(
|
||||
f"DESeq2 ({len(deseq2_gene_wise)} contrasts)"
|
||||
)
|
||||
if dexseq_gene_wise:
|
||||
engines.append(
|
||||
f"DEXSeq ({len(dexseq_gene_wise)} contrasts)"
|
||||
)
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "Gene-wise Dispersion Fallback",
|
||||
"Details": "; ".join(engines),
|
||||
}
|
||||
)
|
||||
if dexseq_covariate_drops:
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "DEXSeq Covariates Dropped",
|
||||
"Details": (
|
||||
f"{len(dexseq_covariate_drops)} "
|
||||
"contrasts affected"
|
||||
),
|
||||
}
|
||||
)
|
||||
if failed_dtu:
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "DTU Failure",
|
||||
"Details": (
|
||||
f"{len(failed_dtu)} contrasts failed"
|
||||
),
|
||||
}
|
||||
)
|
||||
if failed_dge:
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "DGE Failure",
|
||||
"Details": (
|
||||
f"{len(failed_dge)} contrasts failed"
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
warnings_df = pd.DataFrame(warnings_data)
|
||||
DataTable.from_pandas(
|
||||
warnings_df,
|
||||
paging=False,
|
||||
use_index=False,
|
||||
h4("Quality Warnings Summary")
|
||||
warnings_data = []
|
||||
if sample_size_warnings:
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "Sample Size",
|
||||
"Details": "; ".join(sample_size_warnings),
|
||||
}
|
||||
)
|
||||
if deseq2_gene_wise or dexseq_gene_wise:
|
||||
engines = []
|
||||
if deseq2_gene_wise:
|
||||
engines.append(
|
||||
f"DESeq2 ({len(deseq2_gene_wise)} contrasts)"
|
||||
)
|
||||
if dexseq_gene_wise:
|
||||
engines.append(
|
||||
f"DEXSeq ({len(dexseq_gene_wise)} contrasts)"
|
||||
)
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "Gene-wise Dispersion Fallback",
|
||||
"Details": "; ".join(engines),
|
||||
}
|
||||
)
|
||||
if dexseq_covariate_drops:
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "DEXSeq Covariates Dropped",
|
||||
"Details": (
|
||||
f"{len(dexseq_covariate_drops)} "
|
||||
"contrasts affected"
|
||||
),
|
||||
}
|
||||
)
|
||||
if failed_dtu:
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "DTU Failure",
|
||||
"Details": (
|
||||
f"{len(failed_dtu)} contrasts failed"
|
||||
),
|
||||
}
|
||||
)
|
||||
if failed_dge:
|
||||
warnings_data.append(
|
||||
{
|
||||
"Warning Type": "DGE Failure",
|
||||
"Details": (
|
||||
f"{len(failed_dge)} contrasts failed"
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
warnings_df = pd.DataFrame(warnings_data)
|
||||
DataTable.from_pandas(
|
||||
warnings_df,
|
||||
paging=False,
|
||||
use_index=False,
|
||||
)
|
||||
|
||||
with report.add_section("Differential gene expression", "DGE"):
|
||||
tabs = Tabs()
|
||||
|
||||
@ -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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user