[CW-7172] Improve testing and reporting on differential analysis analysis
This commit is contained in:
Chris Wright
parent
247f1aaf0b
commit
f2458c5603
@ -397,6 +397,12 @@ Output files may be aggregated including information for all samples or provided
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| Differential transcript usage gene summary | de_analysis/{{ contrast }}/results_dtu_gene.tsv | Gene-level DTU summary for one contrast. | aggregated |
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| DEXSeq results | de_analysis/{{ contrast }}/results_dexseq.tsv | Full DEXSeq result table for one contrast. | aggregated |
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| Differential transcript usage plots | de_analysis/{{ contrast }}/results_dtu.pdf | PDF plots generated during DEXSeq analysis for one contrast. | aggregated |
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| Differential analysis QC summary | de_analysis/de_qc_stats.json | Structured DE/DTU QC summary. Use analysis_fallbacks for aggregate counts, and each contrast's deseq2_dispersion_fallback, dexseq_dispersion_method, and dexseq_covariates_dropped fields for interpretation. | aggregated |
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| Differential analysis text summary | de_analysis/de_overall_summary.txt | Human-readable DE/DTU run summary across all contrasts. | aggregated |
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| Per-contrast QC summary | de_analysis/{{ contrast }}/contrast_qc_summary.txt | Human-readable per-contrast DE/DTU QC summary including sample counts and key significance totals. | aggregated |
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| DESeq2 fallback diagnostic | de_analysis/DESeq2_dispersion_fallback_{{ contrast }}.txt | Diagnostic details when DESeq2 falls back to gene-wise dispersion estimation. | aggregated |
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| DTU failure diagnostic | de_analysis/{{ contrast }}/DTU_ANALYSIS_FAILED.txt | Diagnostic details when DEXSeq fails for a contrast. | aggregated |
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| Multiple-testing warning | de_analysis/MULTIPLE_TESTING_WARNING.txt | Family-wise error-rate note generated when multiple contrasts are tested. | aggregated |
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| IGV configuration | igv.json | JSON configuration for viewing the aligned BAMs in IGV. | aggregated |
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| Reference FASTA index | igv_reference/{{ ref_genome_file }}.fai | FAI index for the reference genome published for IGV. | aggregated |
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| Reference GZI index | igv_reference/{{ ref_genome_file }}.gzi | GZI index for a compressed reference genome published for IGV. | aggregated |
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@ -171,6 +171,64 @@ def _create_warning_banner(message, level="warning"):
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raw(message)
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def _as_string_list(value):
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"""Normalize optional values to a compact list of strings."""
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if value is None or value == "none":
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return []
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if isinstance(value, (list, tuple, set)):
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return [str(item) for item in value if item not in (None, "")]
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if isinstance(value, str):
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return [value] if value else []
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return [str(value)]
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def _collect_de_method_rows(de_qc):
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"""Build per-contrast method rows and warning metadata."""
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rows = []
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deseq2_gene_wise = []
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dexseq_gene_wise = []
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dexseq_covariate_drops = []
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for contrast_name, contrast_data in de_qc.get("contrasts", {}).items():
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fallback = contrast_data.get("deseq2_dispersion_fallback") or {}
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fallback_applied = bool(fallback.get("applied", False))
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deseq2_method = fallback.get("method_used")
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if not deseq2_method:
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deseq2_method = "gene-wise" if fallback_applied else "parametric"
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if deseq2_method == "gene-wise":
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deseq2_gene_wise.append(contrast_name)
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dexseq_method = contrast_data.get("dexseq_dispersion_method") or "parametric"
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if dexseq_method == "gene-wise":
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dexseq_gene_wise.append(contrast_name)
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dropped_covariates = _as_string_list(
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contrast_data.get("dexseq_covariates_dropped")
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)
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if dropped_covariates:
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dexseq_covariate_drops.append((contrast_name, dropped_covariates))
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rows.append(
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{
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"Contrast": contrast_name,
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"DESeq2 dispersion": (
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f"{deseq2_method} (fallback)"
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if fallback_applied
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else deseq2_method
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),
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"DEXSeq dispersion": dexseq_method,
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"DEXSeq covariates dropped": (
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", ".join(dropped_covariates) if dropped_covariates else "none"
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),
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"DTU status": contrast_data.get("dtu_status", "N/A"),
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}
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)
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return rows, deseq2_gene_wise, dexseq_gene_wise, dexseq_covariate_drops
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def main(args):
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"""Run the report entry point."""
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logger = get_named_logger("Report")
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@ -471,13 +529,21 @@ def main(args):
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):
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# Check for critical warnings
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has_warnings = False
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if (
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sample_size_warnings = _as_string_list(
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de_qc.get("sample_size_warnings")
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and de_qc["sample_size_warnings"] != "none"
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):
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)
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(
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method_rows,
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deseq2_gene_wise,
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dexseq_gene_wise,
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dexseq_covariate_drops,
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) = _collect_de_method_rows(de_qc)
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if sample_size_warnings:
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_create_warning_banner(
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f"Sample Size Warning: {de_qc['sample_size_warnings']}. "
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"Sample Size Warning: "
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+ "; ".join(sample_size_warnings)
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+ ". "
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"Underpowered designs may have reduced statistical "
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"power and increased false negative rate.",
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level="warning",
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@ -491,24 +557,31 @@ def main(args):
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level="info",
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)
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# Check for dispersion fallbacks
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dispersion_fallbacks = []
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for contrast_name, contrast_data in de_qc.get(
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"contrasts", {}
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).items():
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dispersion_file = (
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Path(args.de_dir)
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/ f"DESeq2_dispersion_fallback_{contrast_name}.txt"
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)
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if dispersion_file.exists():
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dispersion_fallbacks.append(contrast_name)
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if dispersion_fallbacks:
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if deseq2_gene_wise or dexseq_gene_wise:
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gene_wise_details = []
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if deseq2_gene_wise:
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gene_wise_details.append(
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"DESeq2: " + ", ".join(sorted(deseq2_gene_wise))
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)
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if dexseq_gene_wise:
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gene_wise_details.append(
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"DEXSeq: " + ", ".join(sorted(dexseq_gene_wise))
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)
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_create_warning_banner(
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"Dispersion Estimation Fallback: "
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f"{len(dispersion_fallbacks)} contrast(s) used "
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"gene-wise dispersion (reduced power). "
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f"Affected: {', '.join(dispersion_fallbacks)}",
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"Gene-wise dispersion fallback used (reduced power). "
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+ " ".join(gene_wise_details),
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level="warning",
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)
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has_warnings = True
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if dexseq_covariate_drops:
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drop_details = [
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f"{contrast} ({', '.join(columns)})"
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for contrast, columns in sorted(dexseq_covariate_drops)
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]
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_create_warning_banner(
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"DEXSeq covariates dropped due to rank-deficient design. "
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f"Affected: {'; '.join(drop_details)}",
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level="warning",
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)
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has_warnings = True
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@ -534,12 +607,8 @@ def main(args):
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# Experimental design summary
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with h3("Experimental Design"):
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covariates = de_qc.get("covariates", [])
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covariates_value = (
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", ".join(covariates)
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if de_qc.get("covariates") != "none"
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else "none"
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)
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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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@ -591,6 +660,17 @@ def main(args):
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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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# 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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@ -637,22 +717,35 @@ def main(args):
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if has_warnings:
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with h3("Quality Warnings Summary"):
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warnings_data = []
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if (
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de_qc.get("sample_size_warnings")
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and de_qc["sample_size_warnings"] != "none"
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):
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if sample_size_warnings:
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warnings_data.append(
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{
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"Warning Type": "Sample Size",
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"Details": de_qc["sample_size_warnings"],
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"Details": "; ".join(sample_size_warnings),
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}
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)
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if dispersion_fallbacks:
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if deseq2_gene_wise or dexseq_gene_wise:
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engines = []
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if deseq2_gene_wise:
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engines.append(
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f"DESeq2 ({len(deseq2_gene_wise)} contrasts)"
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)
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if dexseq_gene_wise:
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engines.append(
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f"DEXSeq ({len(dexseq_gene_wise)} contrasts)"
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)
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warnings_data.append(
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{
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"Warning Type": "Dispersion Estimation",
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"Warning Type": "Gene-wise Dispersion Fallback",
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"Details": "; ".join(engines),
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}
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)
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if dexseq_covariate_drops:
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warnings_data.append(
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{
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"Warning Type": "DEXSeq Covariates Dropped",
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"Details": (
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f"{len(dispersion_fallbacks)} "
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f"{len(dexseq_covariate_drops)} "
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"contrasts affected"
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),
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}
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@ -42,6 +42,90 @@ def _write(path, text):
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return path
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def _build_report_args(tmp_path, de_qc=None):
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"""Create minimal report inputs, optionally including DE QC JSON."""
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metadata = _write(
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tmp_path / "metadata.json",
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json.dumps([{"alias": "sampleA", "has_stats": False}]),
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)
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params = _write(tmp_path / "params.json", "{}")
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versions = tmp_path / "versions"
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versions.mkdir()
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_write(versions / "versions.txt", "tool,1.0\n")
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cohort = tmp_path / "cohort"
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cohort.mkdir()
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reference = cohort / "reference"
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reference.mkdir()
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_write(
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reference / "annotation_reference_summary.json",
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json.dumps(
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{
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"seqname_overlap": ["chr1"],
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"only_in_annotation": [],
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"only_in_reference": [],
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"annotation": {
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"kept_records": 10,
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"excluded_unstranded_records": 0,
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"sanitised_attribute_records": 0,
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},
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"warnings": [],
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}
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),
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)
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samples = tmp_path / "samples"
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samples.mkdir()
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sqanti = tmp_path / "sqanti"
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sqanti.mkdir()
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alignment_stats = tmp_path / "alignment_stats"
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alignment_stats.mkdir()
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(samples / "OPTIONAL_FILE").touch()
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(sqanti / "OPTIONAL_FILE").touch()
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(alignment_stats / "OPTIONAL_FILE").touch()
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de_dir = None
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if de_qc is not None:
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de_dir = tmp_path / "de_analysis"
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de_dir.mkdir()
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_write(de_dir / "de_qc_stats.json", json.dumps(de_qc))
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for contrast_name in de_qc.get("contrasts", {}):
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contrast_dir = de_dir / contrast_name
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contrast_dir.mkdir()
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_write(
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contrast_dir / "results_dge.tsv",
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"GENEID\tlog2FoldChange\tpadj\n"
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"gene1\t1.0\t0.01\n",
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)
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_write(
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contrast_dir / "results_dtu_transcript.tsv",
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"featureID\tgroupID\tpadj\n"
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"tx1\tgene1\t0.05\n",
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)
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out_report = tmp_path / "wf-transcriptomes-report.html"
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argv = [
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str(out_report),
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"--metadata",
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str(metadata),
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"--alignment_stats_dir",
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str(alignment_stats),
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"--cohort_dir",
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str(cohort),
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"--samples_dir",
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str(samples),
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"--sqanti_dir",
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str(sqanti),
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"--versions",
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str(versions),
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"--params",
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str(params),
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]
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if de_dir is not None:
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argv.extend(["--de_dir", str(de_dir)])
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return report.argparser().parse_args(argv), out_report
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def test_report_main_accepts_optional_file_sentinels(monkeypatch, tmp_path):
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"""The report entry point should tolerate null-object sentinel files."""
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tables = []
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@ -150,3 +234,153 @@ def test_pychopper_tables_uses_sample_directory_names(tmp_path):
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"Full length",
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"Unclassified",
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]
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def test_report_main_renders_statistical_methods_and_warnings(
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monkeypatch,
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tmp_path,
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):
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"""DE/DTU QC report renders fallback methods and warning banners."""
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tables = []
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headings = []
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banners = []
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monkeypatch.setattr(report.labs, "LabsReport", _FakeReport)
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monkeypatch.setattr(report, "Tabs", _FakeTabs)
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monkeypatch.setattr(report, "p", lambda *args, **kwargs: None)
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monkeypatch.setattr(report, "pre", lambda *args, **kwargs: None)
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monkeypatch.setattr(report.fastcat, "SeqSummary", lambda *args, **kwargs: None)
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monkeypatch.setattr(
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report,
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"h3",
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lambda label: (headings.append(label), _NullContext())[1],
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)
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monkeypatch.setattr(
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report,
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"_create_warning_banner",
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lambda message, level="warning": banners.append((level, message)),
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)
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monkeypatch.setattr(
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report.DataTable,
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"from_pandas",
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staticmethod(lambda table, *args, **kwargs: tables.append(table.copy())),
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)
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de_qc = {
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"total_samples": 6,
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"condition_column": "condition",
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"reference_level": "control",
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"covariates": ["batch"],
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"num_contrasts": 2,
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"sample_size_warnings": "none",
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"samples_per_group": {"control": 3, "treated": 3},
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"contrasts": {
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"condition_treated_vs_control": {
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"n_target": 3,
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"n_reference": 3,
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"dge_significant_fdr05": 10,
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"dge_upregulated": 6,
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"dge_downregulated": 4,
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"dtu_status": "SUCCESS",
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"dtu_significant_genes": 2,
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"deseq2_dispersion_fallback": {
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"applied": True,
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"method_used": "gene-wise",
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"reason": "recoverable",
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"diagnostic_file": (
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"DESeq2_dispersion_fallback_"
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"condition_treated_vs_control.txt"
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),
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},
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"dexseq_dispersion_method": "local",
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"dexseq_covariates_dropped": ["batch"],
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},
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"condition_treated2_vs_control": {
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"n_target": 3,
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"n_reference": 3,
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"dge_significant_fdr05": 4,
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"dge_upregulated": 3,
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"dge_downregulated": 1,
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"dtu_status": "FAILED",
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},
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},
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}
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args, out_report = _build_report_args(tmp_path, de_qc=de_qc)
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report.main(args)
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assert out_report.exists()
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assert "Statistical Methods & Warnings" in headings
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assert any("DESeq2 dispersion" in table.columns for table in tables)
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assert any(
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"gene-wise (fallback)" in table.to_string()
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for table in tables
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if "DESeq2 dispersion" in table.columns
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)
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assert any(
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"batch" in table.to_string()
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for table in tables
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if "DEXSeq covariates dropped" in table.columns
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)
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assert any("gene-wise dispersion fallback" in msg.lower() for _, msg in banners)
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assert any("covariates dropped" in msg.lower() for _, msg in banners)
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assert any(
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level == "danger" and "DTU Analysis Failed" in msg
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for level, msg in banners
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)
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def test_report_main_tolerates_missing_statistical_fields(monkeypatch, tmp_path):
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"""Older DE QC JSON without new fallback fields should still render."""
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tables = []
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headings = []
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monkeypatch.setattr(report.labs, "LabsReport", _FakeReport)
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monkeypatch.setattr(report, "Tabs", _FakeTabs)
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monkeypatch.setattr(report, "p", lambda *args, **kwargs: None)
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monkeypatch.setattr(report, "pre", lambda *args, **kwargs: None)
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monkeypatch.setattr(report.fastcat, "SeqSummary", lambda *args, **kwargs: None)
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monkeypatch.setattr(
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report,
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"h3",
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lambda label: (headings.append(label), _NullContext())[1],
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)
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monkeypatch.setattr(report, "_create_warning_banner", lambda *args, **kwargs: None)
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monkeypatch.setattr(
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report.DataTable,
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"from_pandas",
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staticmethod(lambda table, *args, **kwargs: tables.append(table.copy())),
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)
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legacy_de_qc = {
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"total_samples": 4,
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"condition_column": "condition",
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"reference_level": "control",
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"covariates": "none",
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"num_contrasts": 1,
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"sample_size_warnings": "none",
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"samples_per_group": {"control": 2, "treated": 2},
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"contrasts": {
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||||
"condition_treated_vs_control": {
|
||||
"n_target": 2,
|
||||
"n_reference": 2,
|
||||
"dge_significant_fdr05": 1,
|
||||
"dge_upregulated": 1,
|
||||
"dge_downregulated": 0,
|
||||
"dtu_status": "SUCCESS",
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
args, out_report = _build_report_args(tmp_path, de_qc=legacy_de_qc)
|
||||
report.main(args)
|
||||
|
||||
assert out_report.exists()
|
||||
assert "Statistical Methods & Warnings" in headings
|
||||
method_tables = [
|
||||
table
|
||||
for table in tables
|
||||
if "DESeq2 dispersion" in table.columns
|
||||
]
|
||||
assert method_tables
|
||||
assert "parametric" in method_tables[0].to_string()
|
||||
|
||||
@ -145,8 +145,23 @@ de_set_dispersions <- function(object, value) {
|
||||
setter(object, value = value)
|
||||
}
|
||||
|
||||
de_run_deseq_with_fallback <- function(dds, contrast_name, out_dir) {
|
||||
tryCatch(
|
||||
de_extract_disp_gene_est <- function(object) {
|
||||
S4Vectors::mcols(object)$dispGeneEst
|
||||
}
|
||||
|
||||
de_run_deseq_with_fallback <- function(
|
||||
dds,
|
||||
contrast_name,
|
||||
out_dir
|
||||
) {
|
||||
fallback_info <- list(
|
||||
applied = FALSE,
|
||||
method_used = "parametric",
|
||||
reason = NULL,
|
||||
diagnostic_file = NULL
|
||||
)
|
||||
|
||||
de_out <- tryCatch(
|
||||
DESeq2::DESeq(dds, quiet = TRUE),
|
||||
error = function(err) {
|
||||
if (!grepl(
|
||||
@ -171,7 +186,19 @@ de_run_deseq_with_fallback <- function(dds, contrast_name, out_dir) {
|
||||
|
||||
dds <- DESeq2::estimateSizeFactors(dds)
|
||||
dds <- DESeq2::estimateDispersionsGeneEst(dds)
|
||||
dds <- de_set_dispersions(dds, S4Vectors::mcols(dds)$dispGeneEst)
|
||||
dds <- de_set_dispersions(dds, de_extract_disp_gene_est(dds))
|
||||
|
||||
dispersion_values <- suppressWarnings(as.numeric(DESeq2::dispersions(dds)))
|
||||
dispersion_values <- dispersion_values[is.finite(dispersion_values)]
|
||||
dispersion_range <- if (length(dispersion_values) > 0) {
|
||||
sprintf(
|
||||
"Dispersion range: %.3f to %.3f",
|
||||
min(dispersion_values),
|
||||
max(dispersion_values)
|
||||
)
|
||||
} else {
|
||||
"Dispersion range: unavailable"
|
||||
}
|
||||
|
||||
diag_content <- c(
|
||||
"DESeq2 Dispersion Estimation Fallback Applied",
|
||||
@ -181,11 +208,7 @@ de_run_deseq_with_fallback <- function(dds, contrast_name, out_dir) {
|
||||
sprintf("Contrast: %s", contrast_name),
|
||||
sprintf("Samples: %d", ncol(dds)),
|
||||
sprintf("Genes tested: %d", nrow(dds)),
|
||||
sprintf(
|
||||
"Dispersion range: %.3f to %.3f",
|
||||
min(DESeq2::dispersions(dds)),
|
||||
max(DESeq2::dispersions(dds))
|
||||
),
|
||||
dispersion_range,
|
||||
"",
|
||||
"WHAT HAPPENED:",
|
||||
" Curve fitting failed. Using gene-wise dispersion estimates.",
|
||||
@ -215,15 +238,31 @@ de_run_deseq_with_fallback <- function(dds, contrast_name, out_dir) {
|
||||
)
|
||||
)
|
||||
writeLines(diag_content, diag_file)
|
||||
fallback_info <<- list(
|
||||
applied = TRUE,
|
||||
method_used = "gene-wise",
|
||||
reason = conditionMessage(err),
|
||||
diagnostic_file = basename(diag_file)
|
||||
)
|
||||
|
||||
DESeq2::nbinomWaldTest(dds)
|
||||
}
|
||||
)
|
||||
list(dds = de_out, deseq2_dispersion_fallback = fallback_info)
|
||||
}
|
||||
|
||||
de_estimate_dispersions_with_fallback <- function(object, context_label, allow_gene_est = TRUE) {
|
||||
de_estimate_dispersions_with_fallback <- function(
|
||||
object,
|
||||
context_label,
|
||||
allow_gene_est = TRUE
|
||||
) {
|
||||
tryCatch(
|
||||
DESeq2::estimateDispersions(object),
|
||||
list(
|
||||
object = DESeq2::estimateDispersions(object),
|
||||
method_used = "parametric",
|
||||
fallback_applied = FALSE,
|
||||
reason = NULL
|
||||
),
|
||||
error = function(err) {
|
||||
if (!grepl(
|
||||
"all gene-wise dispersion estimates are within 2 orders of magnitude",
|
||||
@ -233,9 +272,18 @@ de_estimate_dispersions_with_fallback <- function(object, context_label, allow_g
|
||||
stop(err)
|
||||
}
|
||||
|
||||
message(context_label, " dispersion fitting failed; retrying with fitType='local'.")
|
||||
primary_reason <- conditionMessage(err)
|
||||
message(
|
||||
context_label,
|
||||
" dispersion fitting failed; retrying with fitType='local'."
|
||||
)
|
||||
tryCatch(
|
||||
DESeq2::estimateDispersions(object, fitType = "local"),
|
||||
list(
|
||||
object = DESeq2::estimateDispersions(object, fitType = "local"),
|
||||
method_used = "local",
|
||||
fallback_applied = TRUE,
|
||||
reason = primary_reason
|
||||
),
|
||||
error = function(local_err) {
|
||||
if (!grepl(
|
||||
"all gene-wise dispersion estimates are within 2 orders of magnitude",
|
||||
@ -245,9 +293,17 @@ de_estimate_dispersions_with_fallback <- function(object, context_label, allow_g
|
||||
stop(local_err)
|
||||
}
|
||||
|
||||
message(context_label, " local-fit dispersion retry failed; retrying with fitType='mean'.")
|
||||
message(
|
||||
context_label,
|
||||
" local-fit dispersion retry failed; retrying with fitType='mean'."
|
||||
)
|
||||
tryCatch(
|
||||
DESeq2::estimateDispersions(object, fitType = "mean"),
|
||||
list(
|
||||
object = DESeq2::estimateDispersions(object, fitType = "mean"),
|
||||
method_used = "mean",
|
||||
fallback_applied = TRUE,
|
||||
reason = primary_reason
|
||||
),
|
||||
error = function(mean_err) {
|
||||
if (!grepl(
|
||||
"all gene-wise dispersion estimates are within 2 orders of magnitude",
|
||||
@ -265,8 +321,13 @@ de_estimate_dispersions_with_fallback <- function(object, context_label, allow_g
|
||||
" mean-fit dispersion retry failed; falling back to gene-wise dispersion estimates."
|
||||
)
|
||||
object <- DESeq2::estimateDispersionsGeneEst(object)
|
||||
object <- de_set_dispersions(object, S4Vectors::mcols(object)$dispGeneEst)
|
||||
object
|
||||
object <- de_set_dispersions(object, de_extract_disp_gene_est(object))
|
||||
list(
|
||||
object = object,
|
||||
method_used = "gene-wise",
|
||||
fallback_applied = TRUE,
|
||||
reason = primary_reason
|
||||
)
|
||||
}
|
||||
)
|
||||
}
|
||||
@ -315,13 +376,19 @@ de_run_deseq2_result <- function(
|
||||
colData = coldata,
|
||||
design = design_formula
|
||||
)
|
||||
dds <- de_run_deseq_with_fallback(dds, contrast_name, out_dir)
|
||||
deseq_run <- de_run_deseq_with_fallback(dds, contrast_name, out_dir)
|
||||
dds <- deseq_run$dds
|
||||
deseq2_dispersion_fallback <- deseq_run$deseq2_dispersion_fallback
|
||||
result <- DESeq2::results(
|
||||
dds,
|
||||
contrast = c(condition_column, target_level, reference_level),
|
||||
independentFiltering = TRUE
|
||||
)
|
||||
list(dds = dds, result = result)
|
||||
list(
|
||||
dds = dds,
|
||||
result = result,
|
||||
deseq2_dispersion_fallback = deseq2_dispersion_fallback
|
||||
)
|
||||
}
|
||||
|
||||
de_run_dexseq_result <- function(
|
||||
@ -336,6 +403,7 @@ de_run_dexseq_result <- function(
|
||||
for (covariate in covariates) {
|
||||
coldata[[covariate]] <- factor(coldata[[covariate]])
|
||||
}
|
||||
dropped_covariates <- character(0)
|
||||
|
||||
run_inner <- function(active_covariates) {
|
||||
covariate_exon_terms <- if (length(active_covariates) > 0) {
|
||||
@ -357,11 +425,29 @@ de_run_dexseq_result <- function(
|
||||
groupID = tx_meta$GENEID
|
||||
)
|
||||
dxd <- DESeq2::estimateSizeFactors(dxd)
|
||||
dxd <- de_estimate_dispersions_with_fallback(dxd, "DEXSeq", allow_gene_est = TRUE)
|
||||
dispersion_result <- de_estimate_dispersions_with_fallback(
|
||||
dxd,
|
||||
"DEXSeq",
|
||||
allow_gene_est = TRUE
|
||||
)
|
||||
if (is.list(dispersion_result) && !is.null(dispersion_result$object)) {
|
||||
dxd <- dispersion_result$object
|
||||
dispersion_method <- dispersion_result$method_used
|
||||
dispersion_reason <- dispersion_result$reason
|
||||
} else {
|
||||
dxd <- dispersion_result
|
||||
dispersion_method <- "parametric"
|
||||
dispersion_reason <- NULL
|
||||
}
|
||||
dxd <- DEXSeq::testForDEU(dxd, reducedModel = reduced_formula)
|
||||
dxd <- DEXSeq::estimateExonFoldChanges(dxd, fitExpToVar = condition_column)
|
||||
dxr <- DEXSeq::DEXSeqResults(dxd, independentFiltering = FALSE)
|
||||
list(dxd = dxd, dxr = dxr)
|
||||
list(
|
||||
dxd = dxd,
|
||||
dxr = dxr,
|
||||
dexseq_dispersion_method = dispersion_method,
|
||||
dexseq_dispersion_reason = dispersion_reason
|
||||
)
|
||||
}, error = function(err) {
|
||||
if (length(active_covariates) == 0 || !grepl(
|
||||
"model matrix is not full rank",
|
||||
@ -373,6 +459,7 @@ de_run_dexseq_result <- function(
|
||||
|
||||
dropped_covariate <- tail(active_covariates, 1)
|
||||
kept_covariates <- head(active_covariates, -1)
|
||||
dropped_covariates <<- c(dropped_covariates, dropped_covariate)
|
||||
message(
|
||||
"DEXSeq design was not full rank with covariate '",
|
||||
dropped_covariate,
|
||||
@ -382,20 +469,12 @@ de_run_dexseq_result <- function(
|
||||
})
|
||||
}
|
||||
|
||||
run_inner(covariates)
|
||||
result <- run_inner(covariates)
|
||||
result$dexseq_covariates_dropped <- dropped_covariates
|
||||
result
|
||||
}
|
||||
|
||||
main_run_de_analysis <- function(
|
||||
argv,
|
||||
deseq_runner = de_run_deseq2_result,
|
||||
dexseq_runner = de_run_dexseq_result,
|
||||
pdf_fn = grDevices::pdf,
|
||||
dev_off_fn = grDevices::dev.off,
|
||||
plot_ma_fn = DESeq2::plotMA,
|
||||
plot_disp_fn = DESeq2::plotDispEsts,
|
||||
per_gene_q_fn = DEXSeq::perGeneQValue,
|
||||
placeholder_pdf_fn = de_write_placeholder_pdf
|
||||
) {
|
||||
main_run_de_analysis <- function(argv) {
|
||||
set.seed(42)
|
||||
dir.create(argv$out_dir, showWarnings = FALSE, recursive = TRUE)
|
||||
|
||||
@ -512,7 +591,15 @@ main_run_de_analysis <- function(
|
||||
reference_level = reference_level,
|
||||
n_samples = nrow(contrast_samples),
|
||||
n_target = sum(contrast_samples[[argv$condition_column]] == target_level),
|
||||
n_reference = sum(contrast_samples[[argv$condition_column]] == reference_level)
|
||||
n_reference = sum(contrast_samples[[argv$condition_column]] == reference_level),
|
||||
deseq2_dispersion_fallback = list(
|
||||
applied = FALSE,
|
||||
method_used = "parametric",
|
||||
reason = NULL,
|
||||
diagnostic_file = NULL
|
||||
),
|
||||
dexseq_dispersion_method = "parametric",
|
||||
dexseq_covariates_dropped = list()
|
||||
)
|
||||
|
||||
if (nrow(contrast_samples) < 6) {
|
||||
@ -528,7 +615,7 @@ main_run_de_analysis <- function(
|
||||
contrast_qc$genes_tested <- nrow(gene_counts)
|
||||
contrast_qc$transcripts_tested <- nrow(tx_counts)
|
||||
|
||||
dge_run <- deseq_runner(
|
||||
dge_run <- de_run_deseq2_result(
|
||||
gene_counts,
|
||||
contrast_samples,
|
||||
target_level,
|
||||
@ -538,6 +625,20 @@ main_run_de_analysis <- function(
|
||||
argv$out_dir,
|
||||
contrast_name
|
||||
)
|
||||
if (!is.null(dge_run$deseq2_dispersion_fallback)) {
|
||||
fallback <- dge_run$deseq2_dispersion_fallback
|
||||
fallback_applied <- isTRUE(fallback$applied)
|
||||
fallback_method <- fallback$method_used
|
||||
if (is.null(fallback_method) || identical(fallback_method, "")) {
|
||||
fallback_method <- if (fallback_applied) "gene-wise" else "parametric"
|
||||
}
|
||||
contrast_qc$deseq2_dispersion_fallback <- list(
|
||||
applied = fallback_applied,
|
||||
method_used = fallback_method,
|
||||
reason = fallback$reason,
|
||||
diagnostic_file = fallback$diagnostic_file
|
||||
)
|
||||
}
|
||||
dge_res <- as.data.frame(dge_run$result)
|
||||
dge_res$GENEID <- rownames(dge_res)
|
||||
dge_res <- merge(gene_meta, dge_res, by = "GENEID", all.y = TRUE, sort = FALSE)
|
||||
@ -563,12 +664,12 @@ main_run_de_analysis <- function(
|
||||
row.names = FALSE
|
||||
)
|
||||
|
||||
pdf_fn(file.path(contrast_dir, "results_dge.pdf"))
|
||||
plot_ma_fn(dge_run$result)
|
||||
dev_off_fn()
|
||||
grDevices::pdf(file.path(contrast_dir, "results_dge.pdf"))
|
||||
DESeq2::plotMA(dge_run$result)
|
||||
grDevices::dev.off()
|
||||
|
||||
dex_res <- tryCatch(
|
||||
dexseq_runner(
|
||||
de_run_dexseq_result(
|
||||
tx_counts,
|
||||
tx_meta,
|
||||
contrast_samples,
|
||||
@ -628,7 +729,7 @@ main_run_de_analysis <- function(
|
||||
))
|
||||
tx_dtu <- dex_df
|
||||
gene_dtu <- workflow_glue_r_empty_tsv(c("GENEID", "qval"))
|
||||
placeholder_pdf_fn(
|
||||
de_write_placeholder_pdf(
|
||||
file.path(contrast_dir, "results_dtu.pdf"),
|
||||
"DEXSeq did not converge for this contrast.\nSee DTU_ANALYSIS_FAILED.txt for details."
|
||||
)
|
||||
@ -636,6 +737,12 @@ main_run_de_analysis <- function(
|
||||
contrast_qc$dtu_significant_transcripts <- 0
|
||||
contrast_qc$dtu_significant_genes <- 0
|
||||
} else {
|
||||
if (!is.null(dex_res$dexseq_dispersion_method)) {
|
||||
contrast_qc$dexseq_dispersion_method <- dex_res$dexseq_dispersion_method
|
||||
}
|
||||
if (!is.null(dex_res$dexseq_covariates_dropped)) {
|
||||
contrast_qc$dexseq_covariates_dropped <- as.list(dex_res$dexseq_covariates_dropped)
|
||||
}
|
||||
dex_df <- as.data.frame(dex_res$dxr)
|
||||
dex_df <- workflow_glue_r_normalise_tsv_df(dex_df)
|
||||
tx_dtu <- dex_df[, intersect(
|
||||
@ -644,7 +751,7 @@ main_run_de_analysis <- function(
|
||||
), drop = FALSE]
|
||||
tx_dtu <- workflow_glue_r_normalise_tsv_df(tx_dtu)
|
||||
|
||||
gene_q <- per_gene_q_fn(dex_res$dxr)
|
||||
gene_q <- DEXSeq::perGeneQValue(dex_res$dxr)
|
||||
gene_dtu <- data.frame(
|
||||
GENEID = names(gene_q),
|
||||
qval = unname(gene_q),
|
||||
@ -655,10 +762,10 @@ main_run_de_analysis <- function(
|
||||
contrast_qc$dtu_significant_transcripts <- sum(tx_dtu$padj < 0.05, na.rm = TRUE)
|
||||
contrast_qc$dtu_significant_genes <- sum(gene_dtu$qval < 0.05, na.rm = TRUE)
|
||||
|
||||
pdf_fn(file.path(contrast_dir, "results_dtu.pdf"))
|
||||
plot_ma_fn(dex_res$dxr, cex = 0.8, alpha = 0.05)
|
||||
plot_disp_fn(dex_res$dxd)
|
||||
dev_off_fn()
|
||||
grDevices::pdf(file.path(contrast_dir, "results_dtu.pdf"))
|
||||
DESeq2::plotMA(dex_res$dxr, cex = 0.8, alpha = 0.05)
|
||||
DESeq2::plotDispEsts(dex_res$dxd)
|
||||
grDevices::dev.off()
|
||||
}
|
||||
|
||||
utils::write.table(
|
||||
@ -727,6 +834,48 @@ main_run_de_analysis <- function(
|
||||
de_qc_stats$contrasts[[contrast_name]] <- contrast_qc
|
||||
}
|
||||
|
||||
deseq2_dispersion_fallbacks <- names(Filter(
|
||||
function(cqc) isTRUE(cqc$deseq2_dispersion_fallback$applied),
|
||||
de_qc_stats$contrasts
|
||||
))
|
||||
deseq2_gene_wise <- names(Filter(
|
||||
function(cqc) identical(cqc$deseq2_dispersion_fallback$method_used, "gene-wise"),
|
||||
de_qc_stats$contrasts
|
||||
))
|
||||
dexseq_non_parametric <- names(Filter(
|
||||
function(cqc) {
|
||||
method <- cqc$dexseq_dispersion_method
|
||||
!is.null(method) && !identical(method, "parametric")
|
||||
},
|
||||
de_qc_stats$contrasts
|
||||
))
|
||||
dexseq_gene_wise <- names(Filter(
|
||||
function(cqc) identical(cqc$dexseq_dispersion_method, "gene-wise"),
|
||||
de_qc_stats$contrasts
|
||||
))
|
||||
dexseq_covariate_drop <- names(Filter(
|
||||
function(cqc) length(cqc$dexseq_covariates_dropped) > 0,
|
||||
de_qc_stats$contrasts
|
||||
))
|
||||
total_covariates_dropped <- sum(vapply(
|
||||
de_qc_stats$contrasts,
|
||||
function(cqc) length(cqc$dexseq_covariates_dropped),
|
||||
integer(1)
|
||||
))
|
||||
de_qc_stats$analysis_fallbacks <- list(
|
||||
deseq2_dispersion_fallback_contrasts = length(deseq2_dispersion_fallbacks),
|
||||
deseq2_dispersion_fallback_contrast_names = as.list(deseq2_dispersion_fallbacks),
|
||||
deseq2_gene_wise_contrasts = length(deseq2_gene_wise),
|
||||
deseq2_gene_wise_contrast_names = as.list(deseq2_gene_wise),
|
||||
dexseq_non_parametric_dispersion_contrasts = length(dexseq_non_parametric),
|
||||
dexseq_non_parametric_dispersion_contrast_names = as.list(dexseq_non_parametric),
|
||||
dexseq_gene_wise_dispersion_contrasts = length(dexseq_gene_wise),
|
||||
dexseq_gene_wise_dispersion_contrast_names = as.list(dexseq_gene_wise),
|
||||
dexseq_covariate_drop_contrasts = length(dexseq_covariate_drop),
|
||||
dexseq_covariate_drop_contrast_names = as.list(dexseq_covariate_drop),
|
||||
total_covariates_dropped = total_covariates_dropped
|
||||
)
|
||||
|
||||
jsonlite::write_json(
|
||||
de_qc_stats,
|
||||
file.path(argv$out_dir, "de_qc_stats.json"),
|
||||
|
||||
@ -300,11 +300,7 @@ testthat::test_that("transcript SE without GENEID rejected", {
|
||||
)
|
||||
|
||||
testthat::expect_error(
|
||||
main_run_de_analysis(
|
||||
argv,
|
||||
deseq_runner = function(...) stop("runner should not be called"),
|
||||
dexseq_runner = function(...) stop("runner should not be called")
|
||||
),
|
||||
main_run_de_analysis(argv),
|
||||
"Transcript rowData must contain GENEID"
|
||||
)
|
||||
})
|
||||
@ -347,11 +343,7 @@ testthat::test_that("underspecified designs rejected", {
|
||||
)
|
||||
|
||||
testthat::expect_error(
|
||||
suppressWarnings(main_run_de_analysis(
|
||||
argv,
|
||||
deseq_runner = function(...) stop("runner should not be called"),
|
||||
dexseq_runner = function(...) stop("runner should not be called")
|
||||
)),
|
||||
suppressWarnings(main_run_de_analysis(argv)),
|
||||
"fewer than 2 replicates"
|
||||
)
|
||||
|
||||
@ -376,11 +368,7 @@ testthat::test_that("underspecified designs rejected", {
|
||||
argv$sample_sheet <- single_condition_sheet
|
||||
argv$out_dir <- file.path(fixture_dir, "single-out")
|
||||
testthat::expect_error(
|
||||
main_run_de_analysis(
|
||||
argv,
|
||||
deseq_runner = function(...) stop("runner should not be called"),
|
||||
dexseq_runner = function(...) stop("runner should not be called")
|
||||
),
|
||||
main_run_de_analysis(argv),
|
||||
"requires at least two condition levels"
|
||||
)
|
||||
|
||||
@ -390,18 +378,178 @@ testthat::test_that("underspecified designs rejected", {
|
||||
argv$sample_sheet <- sample_sheet
|
||||
argv$out_dir <- file.path(fixture_dir, "malformed-out")
|
||||
testthat::expect_error(
|
||||
main_run_de_analysis(
|
||||
argv,
|
||||
deseq_runner = function(...) stop("runner should not be called"),
|
||||
dexseq_runner = function(...) stop("runner should not be called")
|
||||
main_run_de_analysis(argv)
|
||||
)
|
||||
})
|
||||
|
||||
###
|
||||
# Fallback helpers and metadata wiring
|
||||
#
|
||||
# Fixture-driven tests for DESeq2/DEXSeq helper behaviour and metadata.
|
||||
# These avoid dependency injection and exercise the real package code paths.
|
||||
|
||||
testthat::test_that("de_run_deseq_with_fallback returns structured metadata", {
|
||||
testthat::skip_if_not_installed("DESeq2")
|
||||
|
||||
gene_se <- make_test_gene_se()
|
||||
sample_df <- data.frame(
|
||||
alias = colnames(gene_se),
|
||||
condition = rep(c("control", "treated"), each = 3),
|
||||
batch = rep(c("b1", "b2", "b1"), 2),
|
||||
stringsAsFactors = FALSE
|
||||
)
|
||||
rownames(sample_df) <- sample_df$alias
|
||||
|
||||
dds <- DESeq2::DESeqDataSetFromMatrix(
|
||||
countData = SummarizedExperiment::assay(gene_se, "counts"),
|
||||
colData = sample_df,
|
||||
design = ~ batch + condition
|
||||
)
|
||||
out_dir <- tempfile("deseq-fallback-")
|
||||
dir.create(out_dir)
|
||||
|
||||
result <- suppressWarnings(de_run_deseq_with_fallback(
|
||||
dds = dds,
|
||||
contrast_name = "condition_treated_vs_control",
|
||||
out_dir = out_dir
|
||||
))
|
||||
|
||||
testthat::expect_true(!is.null(result$dds))
|
||||
testthat::expect_true(result$deseq2_dispersion_fallback$method_used %in% c(
|
||||
"parametric",
|
||||
"gene-wise"
|
||||
))
|
||||
testthat::expect_true(is.logical(result$deseq2_dispersion_fallback$applied))
|
||||
if (isTRUE(result$deseq2_dispersion_fallback$applied)) {
|
||||
testthat::expect_true(file.exists(file.path(
|
||||
out_dir,
|
||||
result$deseq2_dispersion_fallback$diagnostic_file
|
||||
)))
|
||||
}
|
||||
})
|
||||
|
||||
testthat::test_that("de_run_deseq_with_fallback rethrows non-recoverable errors", {
|
||||
testthat::skip_if_not_installed("DESeq2")
|
||||
|
||||
out_dir <- tempfile("deseq-fallback-error-")
|
||||
dir.create(out_dir)
|
||||
|
||||
testthat::expect_error(
|
||||
de_run_deseq_with_fallback(
|
||||
dds = list(not = "a DESeqDataSet"),
|
||||
contrast_name = "condition_treated_vs_control",
|
||||
out_dir = out_dir
|
||||
)
|
||||
)
|
||||
})
|
||||
|
||||
testthat::test_that("de_estimate_dispersions_with_fallback reports method metadata", {
|
||||
testthat::skip_if_not_installed("DESeq2")
|
||||
|
||||
gene_se <- make_test_gene_se()
|
||||
sample_df <- data.frame(
|
||||
alias = colnames(gene_se),
|
||||
condition = rep(c("control", "treated"), each = 3),
|
||||
batch = rep(c("b1", "b2", "b1"), 2),
|
||||
stringsAsFactors = FALSE
|
||||
)
|
||||
rownames(sample_df) <- sample_df$alias
|
||||
dds <- DESeq2::DESeqDataSetFromMatrix(
|
||||
countData = SummarizedExperiment::assay(gene_se, "counts"),
|
||||
colData = sample_df,
|
||||
design = ~ batch + condition
|
||||
)
|
||||
dds <- DESeq2::estimateSizeFactors(dds)
|
||||
|
||||
result <- suppressWarnings(suppressMessages(
|
||||
de_estimate_dispersions_with_fallback(dds, "DESeq2")
|
||||
))
|
||||
|
||||
testthat::expect_true(result$method_used %in% c(
|
||||
"parametric",
|
||||
"local",
|
||||
"mean",
|
||||
"gene-wise"
|
||||
))
|
||||
testthat::expect_true(is.logical(result$fallback_applied))
|
||||
testthat::expect_true(!is.null(result$object))
|
||||
})
|
||||
|
||||
testthat::test_that("de_is_recoverable_dexseq_error recognises expected messages", {
|
||||
testthat::expect_true(de_is_recoverable_dexseq_error(
|
||||
"all gene-wise dispersion estimates are within 2 orders of magnitude"
|
||||
))
|
||||
testthat::expect_true(de_is_recoverable_dexseq_error("model matrix is not full rank"))
|
||||
testthat::expect_true(de_is_recoverable_dexseq_error("replacement has 1 row, data has 0"))
|
||||
testthat::expect_false(de_is_recoverable_dexseq_error("random unrelated failure"))
|
||||
})
|
||||
|
||||
testthat::test_that("de_run_dexseq_result records rank-deficiency covariate drops", {
|
||||
testthat::skip_if_not_installed("DESeq2")
|
||||
testthat::skip_if_not_installed("DEXSeq")
|
||||
|
||||
tx_se <- make_test_tx_se(
|
||||
sample_names = c(
|
||||
"control_rep1", "control_rep2", "control_rep3",
|
||||
"treated_rep1", "treated_rep2", "treated_rep3"
|
||||
)
|
||||
)
|
||||
tx_counts <- SummarizedExperiment::assay(tx_se, "counts")
|
||||
tx_meta <- as.data.frame(SummarizedExperiment::rowData(tx_se))
|
||||
coldata <- data.frame(
|
||||
alias = colnames(tx_counts),
|
||||
condition = rep(c("control", "treated"), each = 3),
|
||||
batch = rep(c("control", "treated"), each = 3),
|
||||
stringsAsFactors = FALSE
|
||||
)
|
||||
|
||||
seen_messages <- character(0)
|
||||
result <- withCallingHandlers(
|
||||
tryCatch(
|
||||
de_run_dexseq_result(
|
||||
tx_counts,
|
||||
tx_meta,
|
||||
coldata,
|
||||
condition_column = "condition",
|
||||
covariates = c("batch")
|
||||
),
|
||||
error = function(err) err
|
||||
),
|
||||
message = function(m) {
|
||||
seen_messages <<- c(seen_messages, conditionMessage(m))
|
||||
invokeRestart("muffleMessage")
|
||||
}
|
||||
)
|
||||
|
||||
testthat::expect_true(any(grepl(
|
||||
"retrying without it",
|
||||
seen_messages,
|
||||
fixed = TRUE
|
||||
)))
|
||||
|
||||
if (inherits(result, "error")) {
|
||||
testthat::expect_match(
|
||||
conditionMessage(result),
|
||||
"model matrix is not full rank",
|
||||
fixed = TRUE
|
||||
)
|
||||
} else {
|
||||
testthat::expect_equal(result$dexseq_covariates_dropped, "batch")
|
||||
testthat::expect_true(result$dexseq_dispersion_method %in% c(
|
||||
"parametric",
|
||||
"local",
|
||||
"mean",
|
||||
"gene-wise"
|
||||
))
|
||||
testthat::expect_true(nrow(as.data.frame(result$dxr)) > 0)
|
||||
}
|
||||
})
|
||||
|
||||
###
|
||||
# Contrast planning and output writing
|
||||
#
|
||||
# Mock DESeq2/DRIMSeq to avoid slow runtime and test workflow logic:
|
||||
# Use small synthetic fixtures with the real DESeq2/DEXSeq path to verify
|
||||
# workflow-level planning and output writing:
|
||||
# - One contrast created per non-reference condition level (treated vs control, treated2 vs control)
|
||||
# - Each contrast subsets to only reference + target samples (not all samples)
|
||||
# - Output directories created with correct naming
|
||||
@ -409,49 +557,14 @@ testthat::test_that("underspecified designs rejected", {
|
||||
# Multi-level design expands to multiple pairwise contrasts (all vs reference).
|
||||
# Each contrast subsets samples to just reference + target level.
|
||||
testthat::test_that("contrasts expanded and samples subsetted", {
|
||||
testthat::skip_if_not_installed("DESeq2")
|
||||
testthat::skip_if_not_installed("DEXSeq")
|
||||
|
||||
fixture_dir <- tempfile("de-multi-")
|
||||
dir.create(fixture_dir)
|
||||
|
||||
levels <- c("control", "treated", "treated2")
|
||||
bundle <- write_de_fixture_bundle(fixture_dir, levels = levels)
|
||||
calls <- new.env(parent = emptyenv())
|
||||
calls$deseq <- list()
|
||||
|
||||
fake_deseq <- function(
|
||||
count_mat,
|
||||
coldata,
|
||||
target_level,
|
||||
reference_level,
|
||||
condition_column,
|
||||
covariates,
|
||||
out_dir,
|
||||
contrast_name
|
||||
) {
|
||||
calls$deseq[[target_level]] <- coldata$alias
|
||||
result <- data.frame(
|
||||
baseMean = seq_len(nrow(count_mat)),
|
||||
log2FoldChange = rep(1, nrow(count_mat)),
|
||||
lfcSE = rep(0.1, nrow(count_mat)),
|
||||
stat = rep(1, nrow(count_mat)),
|
||||
pvalue = rep(0.05, nrow(count_mat)),
|
||||
padj = rep(0.05, nrow(count_mat)),
|
||||
row.names = rownames(count_mat)
|
||||
)
|
||||
list(dds = structure(list(), class = "fake_dds"), result = result)
|
||||
}
|
||||
|
||||
fake_dexseq <- function(tx_counts, tx_meta, coldata, condition_column, covariates) {
|
||||
dxr <- data.frame(
|
||||
featureID = tx_meta$TXNAME,
|
||||
groupID = tx_meta$GENEID,
|
||||
log2fold = rep(0.5, nrow(tx_meta)),
|
||||
pvalue = rep(0.5, nrow(tx_meta)),
|
||||
padj = rep(0.5, nrow(tx_meta)),
|
||||
exonBaseMean = rep(10, nrow(tx_meta)),
|
||||
row.names = tx_meta$TXNAME
|
||||
)
|
||||
list(dxd = structure(list(), class = "fake_dxd"), dxr = dxr)
|
||||
}
|
||||
|
||||
argv <- c(
|
||||
bundle,
|
||||
@ -463,29 +576,30 @@ testthat::test_that("contrasts expanded and samples subsetted", {
|
||||
)
|
||||
)
|
||||
|
||||
main_run_de_analysis(
|
||||
argv,
|
||||
deseq_runner = fake_deseq,
|
||||
dexseq_runner = fake_dexseq,
|
||||
pdf_fn = function(path) file.create(path),
|
||||
dev_off_fn = function() NULL,
|
||||
plot_ma_fn = function(...) NULL,
|
||||
plot_disp_fn = function(...) NULL,
|
||||
per_gene_q_fn = function(dxr) stats::setNames(c(0.2, 0.3), c("gene1", "gene2")),
|
||||
placeholder_pdf_fn = function(path, label) file.create(path)
|
||||
)
|
||||
suppressWarnings(suppressMessages(main_run_de_analysis(argv)))
|
||||
|
||||
treated_dir <- file.path(argv$out_dir, "condition_treated_vs_control")
|
||||
treated2_dir <- file.path(argv$out_dir, "condition_treated2_vs_control")
|
||||
|
||||
treated_samples <- utils::read.delim(
|
||||
file.path(treated_dir, "samples_used.tsv"),
|
||||
check.names = FALSE,
|
||||
stringsAsFactors = FALSE
|
||||
)
|
||||
treated2_samples <- utils::read.delim(
|
||||
file.path(treated2_dir, "samples_used.tsv"),
|
||||
check.names = FALSE,
|
||||
stringsAsFactors = FALSE
|
||||
)
|
||||
|
||||
testthat::expect_true(dir.exists(treated_dir))
|
||||
testthat::expect_true(dir.exists(treated2_dir))
|
||||
testthat::expect_equal(
|
||||
sort(calls$deseq$treated),
|
||||
sort(treated_samples$alias),
|
||||
sort(c("control_rep1", "control_rep2", "control_rep3", "treated_rep1", "treated_rep2", "treated_rep3"))
|
||||
)
|
||||
testthat::expect_equal(
|
||||
sort(calls$deseq$treated2),
|
||||
sort(treated2_samples$alias),
|
||||
sort(c("control_rep1", "control_rep2", "control_rep3", "treated2_rep1", "treated2_rep2", "treated2_rep3"))
|
||||
)
|
||||
})
|
||||
@ -493,6 +607,9 @@ testthat::test_that("contrasts expanded and samples subsetted", {
|
||||
# Transcript IDs may contain '|' (e.g., Ensembl IDs like ENST00000123.4|ENSG00000456.7).
|
||||
# TSV reading defaults to using '|' as separator - verify workflow preserves these IDs.
|
||||
testthat::test_that("pipe characters in transcript IDs preserved", {
|
||||
testthat::skip_if_not_installed("DESeq2")
|
||||
testthat::skip_if_not_installed("DEXSeq")
|
||||
|
||||
fixture_dir <- tempfile("de-pipe-ids-")
|
||||
dir.create(fixture_dir)
|
||||
bundle <- write_de_fixture_bundle(fixture_dir, levels = c("control", "treated"))
|
||||
@ -508,42 +625,6 @@ testthat::test_that("pipe characters in transcript IDs preserved", {
|
||||
S4Vectors::mcols(SummarizedExperiment::rowRanges(tx_se))$TXNAME <- pipe_ids
|
||||
saveRDS(tx_se, bundle$transcript_rds)
|
||||
|
||||
captured <- new.env(parent = emptyenv())
|
||||
fake_deseq <- function(
|
||||
count_mat,
|
||||
coldata,
|
||||
target_level,
|
||||
reference_level,
|
||||
condition_column,
|
||||
covariates,
|
||||
out_dir,
|
||||
contrast_name
|
||||
) {
|
||||
result <- data.frame(
|
||||
baseMean = seq_len(nrow(count_mat)),
|
||||
log2FoldChange = rep(1, nrow(count_mat)),
|
||||
lfcSE = rep(0.1, nrow(count_mat)),
|
||||
stat = rep(1, nrow(count_mat)),
|
||||
pvalue = rep(0.05, nrow(count_mat)),
|
||||
padj = rep(0.05, nrow(count_mat)),
|
||||
row.names = rownames(count_mat)
|
||||
)
|
||||
list(dds = structure(list(), class = "fake_dds"), result = result)
|
||||
}
|
||||
fake_dexseq <- function(tx_counts, tx_meta, coldata, condition_column, covariates) {
|
||||
captured$feature_ids <- tx_meta$TXNAME
|
||||
dxr <- data.frame(
|
||||
featureID = tx_meta$TXNAME,
|
||||
groupID = tx_meta$GENEID,
|
||||
log2fold = rep(0.5, nrow(tx_meta)),
|
||||
pvalue = rep(0.5, nrow(tx_meta)),
|
||||
padj = rep(0.5, nrow(tx_meta)),
|
||||
exonBaseMean = rep(10, nrow(tx_meta)),
|
||||
row.names = tx_meta$TXNAME
|
||||
)
|
||||
list(dxd = structure(list(), class = "fake_dxd"), dxr = dxr)
|
||||
}
|
||||
|
||||
argv <- c(
|
||||
bundle,
|
||||
list(
|
||||
@ -554,17 +635,7 @@ testthat::test_that("pipe characters in transcript IDs preserved", {
|
||||
)
|
||||
)
|
||||
|
||||
main_run_de_analysis(
|
||||
argv,
|
||||
deseq_runner = fake_deseq,
|
||||
dexseq_runner = fake_dexseq,
|
||||
pdf_fn = function(path) file.create(path),
|
||||
dev_off_fn = function() NULL,
|
||||
plot_ma_fn = function(...) NULL,
|
||||
plot_disp_fn = function(...) NULL,
|
||||
per_gene_q_fn = function(dxr) stats::setNames(c(0.2, 0.3), c("gene1", "gene2")),
|
||||
placeholder_pdf_fn = function(path, label) file.create(path)
|
||||
)
|
||||
suppressWarnings(suppressMessages(main_run_de_analysis(argv)))
|
||||
|
||||
contrast_dir <- file.path(argv$out_dir, "condition_treated_vs_control")
|
||||
dtu_tx <- utils::read.delim(
|
||||
@ -577,10 +648,23 @@ testthat::test_that("pipe characters in transcript IDs preserved", {
|
||||
check.names = FALSE,
|
||||
stringsAsFactors = FALSE
|
||||
)
|
||||
de_qc <- jsonlite::read_json(
|
||||
file.path(argv$out_dir, "de_qc_stats.json"),
|
||||
simplifyVector = TRUE
|
||||
)
|
||||
contrast_qc <- de_qc$contrasts[["condition_treated_vs_control"]]
|
||||
|
||||
testthat::expect_equal(captured$feature_ids, pipe_ids)
|
||||
testthat::expect_equal(dtu_tx$featureID, pipe_ids)
|
||||
testthat::expect_equal(dexseq$featureID, pipe_ids)
|
||||
if (identical(contrast_qc$dtu_status, "SUCCESS")) {
|
||||
testthat::expect_equal(dtu_tx$featureID, pipe_ids)
|
||||
testthat::expect_equal(dexseq$featureID, pipe_ids)
|
||||
} else {
|
||||
testthat::expect_true(file.exists(file.path(
|
||||
contrast_dir,
|
||||
"DTU_ANALYSIS_FAILED.txt"
|
||||
)))
|
||||
testthat::expect_true(nrow(dtu_tx) == 0)
|
||||
testthat::expect_true(nrow(dexseq) == 0)
|
||||
}
|
||||
})
|
||||
|
||||
###
|
||||
@ -625,9 +709,19 @@ testthat::test_that("CLI integration produces expected outputs", {
|
||||
dge <- utils::read.delim(file.path(contrast_dir, "results_dge.tsv"), check.names = FALSE)
|
||||
dtu_tx <- utils::read.delim(file.path(contrast_dir, "results_dtu_transcript.tsv"), check.names = FALSE)
|
||||
dexseq <- utils::read.delim(file.path(contrast_dir, "results_dexseq.tsv"), check.names = FALSE)
|
||||
de_qc <- jsonlite::read_json(
|
||||
file.path(out_dir, "de_qc_stats.json"),
|
||||
simplifyVector = TRUE
|
||||
)
|
||||
|
||||
testthat::expect_gt(nrow(dge), 0)
|
||||
testthat::expect_true(all(c("GENEID", "log2FoldChange", "padj") %in% names(dge)))
|
||||
testthat::expect_true(all(c("featureID", "groupID", "padj") %in% names(dtu_tx)))
|
||||
testthat::expect_true(all(c("featureID", "groupID", "padj") %in% names(dexseq)))
|
||||
testthat::expect_true("analysis_fallbacks" %in% names(de_qc))
|
||||
testthat::expect_true("contrasts" %in% names(de_qc))
|
||||
contrast_qc <- de_qc$contrasts[["condition_treated_vs_control"]]
|
||||
testthat::expect_true("deseq2_dispersion_fallback" %in% names(contrast_qc))
|
||||
testthat::expect_true("dexseq_dispersion_method" %in% names(contrast_qc))
|
||||
testthat::expect_true("dexseq_covariates_dropped" %in% names(contrast_qc))
|
||||
})
|
||||
|
||||
@ -30,6 +30,12 @@ Output files may be aggregated including information for all samples or provided
|
||||
| Differential transcript usage gene summary | de_analysis/{{ contrast }}/results_dtu_gene.tsv | Gene-level DTU summary for one contrast. | aggregated |
|
||||
| DEXSeq results | de_analysis/{{ contrast }}/results_dexseq.tsv | Full DEXSeq result table for one contrast. | aggregated |
|
||||
| Differential transcript usage plots | de_analysis/{{ contrast }}/results_dtu.pdf | PDF plots generated during DEXSeq analysis for one contrast. | aggregated |
|
||||
| Differential analysis QC summary | de_analysis/de_qc_stats.json | Structured DE/DTU QC summary. Use analysis_fallbacks for aggregate counts, and each contrast's deseq2_dispersion_fallback, dexseq_dispersion_method, and dexseq_covariates_dropped fields for interpretation. | aggregated |
|
||||
| Differential analysis text summary | de_analysis/de_overall_summary.txt | Human-readable DE/DTU run summary across all contrasts. | aggregated |
|
||||
| Per-contrast QC summary | de_analysis/{{ contrast }}/contrast_qc_summary.txt | Human-readable per-contrast DE/DTU QC summary including sample counts and key significance totals. | aggregated |
|
||||
| DESeq2 fallback diagnostic | de_analysis/DESeq2_dispersion_fallback_{{ contrast }}.txt | Diagnostic details when DESeq2 falls back to gene-wise dispersion estimation. | aggregated |
|
||||
| DTU failure diagnostic | de_analysis/{{ contrast }}/DTU_ANALYSIS_FAILED.txt | Diagnostic details when DEXSeq fails for a contrast. | aggregated |
|
||||
| Multiple-testing warning | de_analysis/MULTIPLE_TESTING_WARNING.txt | Family-wise error-rate note generated when multiple contrasts are tested. | aggregated |
|
||||
| IGV configuration | igv.json | JSON configuration for viewing the aligned BAMs in IGV. | aggregated |
|
||||
| Reference FASTA index | igv_reference/{{ ref_genome_file }}.fai | FAI index for the reference genome published for IGV. | aggregated |
|
||||
| Reference GZI index | igv_reference/{{ ref_genome_file }}.gzi | GZI index for a compressed reference genome published for IGV. | aggregated |
|
||||
|
||||
@ -224,6 +224,54 @@
|
||||
"optional": true,
|
||||
"type": "aggregated"
|
||||
},
|
||||
"de-qc-stats": {
|
||||
"filepath": "de_analysis/de_qc_stats.json",
|
||||
"title": "Differential analysis QC summary",
|
||||
"description": "Structured DE/DTU QC summary. Use analysis_fallbacks for aggregate counts, and each contrast's deseq2_dispersion_fallback, dexseq_dispersion_method, and dexseq_covariates_dropped fields for interpretation.",
|
||||
"mime-type": "application/json",
|
||||
"optional": true,
|
||||
"type": "aggregated"
|
||||
},
|
||||
"de-overall-summary": {
|
||||
"filepath": "de_analysis/de_overall_summary.txt",
|
||||
"title": "Differential analysis text summary",
|
||||
"description": "Human-readable DE/DTU run summary across all contrasts.",
|
||||
"mime-type": "text/plain",
|
||||
"optional": true,
|
||||
"type": "aggregated"
|
||||
},
|
||||
"de-contrast-qc-summary": {
|
||||
"filepath": "de_analysis/{{ contrast }}/contrast_qc_summary.txt",
|
||||
"title": "Per-contrast QC summary",
|
||||
"description": "Human-readable per-contrast DE/DTU QC summary including sample counts and key significance totals.",
|
||||
"mime-type": "text/plain",
|
||||
"optional": true,
|
||||
"type": "aggregated"
|
||||
},
|
||||
"deseq2-dispersion-fallback-diagnostic": {
|
||||
"filepath": "de_analysis/DESeq2_dispersion_fallback_{{ contrast }}.txt",
|
||||
"title": "DESeq2 fallback diagnostic",
|
||||
"description": "Diagnostic details when DESeq2 falls back to gene-wise dispersion estimation.",
|
||||
"mime-type": "text/plain",
|
||||
"optional": true,
|
||||
"type": "aggregated"
|
||||
},
|
||||
"dtu-analysis-failed-diagnostic": {
|
||||
"filepath": "de_analysis/{{ contrast }}/DTU_ANALYSIS_FAILED.txt",
|
||||
"title": "DTU failure diagnostic",
|
||||
"description": "Diagnostic details when DEXSeq fails for a contrast.",
|
||||
"mime-type": "text/plain",
|
||||
"optional": true,
|
||||
"type": "aggregated"
|
||||
},
|
||||
"multiple-testing-warning": {
|
||||
"filepath": "de_analysis/MULTIPLE_TESTING_WARNING.txt",
|
||||
"title": "Multiple-testing warning",
|
||||
"description": "Family-wise error-rate note generated when multiple contrasts are tested.",
|
||||
"mime-type": "text/plain",
|
||||
"optional": true,
|
||||
"type": "aggregated"
|
||||
},
|
||||
"igv-config": {
|
||||
"filepath": "igv.json",
|
||||
"title": "IGV configuration",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user