Skip cohort processes when n=1 [CW-7208]

.gitlab-ci.yml

@@ -134,15 +134,14 @@ docker-run:
                   jq -e '.contrasts["condition_treated_vs_control"].dge_status | IN("SUCCESS", "FAILED")' ${CI_PROJECT_NAME}/de_analysis/de_qc_stats.json >/dev/null &&
                   jq -e '.contrasts["condition_treated_vs_control"].dtu_status | IN("SUCCESS", "FAILED")' ${CI_PROJECT_NAME}/de_analysis/de_qc_stats.json >/dev/null
 
-        # Smoke: quick discover-mode sanity check for core cohort and per-sample outputs.
+        # Smoke: quick discover-mode sanity check for core cohort outputs.
         - if: $MATRIX_NAME == "smoke_discover"
           variables:
               NF_BEFORE_SCRIPT: ":"
               NF_WORKFLOW_OPTS: "--fastq test_data/smoke/reads.fastq --sample sampleA --ref_genome test_data/smoke/reference.fa --ref_annotation test_data/smoke/annotation.gtf"
               AFTER_NEXTFLOW_CMD: >
-                  test -f ${CI_PROJECT_NAME}/cohort/transcripts.gtf &&
+                  test -f ${CI_PROJECT_NAME}/samples/sampleA/transcripts.gtf &&
-                  test -f ${CI_PROJECT_NAME}/cohort/cohort.transcriptome.fa &&
+                  test -f ${CI_PROJECT_NAME}/samples/sampleA/sampleA.transcriptome.fa
-                  test -f ${CI_PROJECT_NAME}/samples/sampleA/transcripts.gtf
 
         # Smoke: fixed-annotation path sanity check for quantification outputs.
         - if: $MATRIX_NAME == "smoke_fixed"
@@ -150,8 +149,8 @@ docker-run:
               NF_BEFORE_SCRIPT: ":"
               NF_WORKFLOW_OPTS: "--fastq test_data/smoke/reads.fastq --sample sampleA --ref_genome test_data/smoke/reference.fa --ref_annotation test_data/smoke/annotation.gtf --transcriptome_mode fixed_annotation"
               AFTER_NEXTFLOW_CMD: >
-                  test -f ${CI_PROJECT_NAME}/cohort/transcripts.gtf &&
+                  test -f ${CI_PROJECT_NAME}/samples/sampleA/transcripts.gtf &&
-                  test -f ${CI_PROJECT_NAME}/cohort/transcript_counts.tsv
+                  test -f ${CI_PROJECT_NAME}/samples/sampleA/transcript_counts.tsv
 
         # Smoke: direct-RNA alignment profile and downstream SQANTI output presence.
         - if: $MATRIX_NAME == "smoke_direct_rna"
@@ -160,7 +159,7 @@ docker-run:
               NF_WORKFLOW_OPTS: "--fastq test_data/smoke/reads.fastq --sample sampleA --ref_genome test_data/smoke/reference.fa --ref_annotation test_data/smoke/annotation.gtf --direct_rna"
               AFTER_NEXTFLOW_CMD: >
                   test -f ${CI_PROJECT_NAME}/cohort/alignments/sampleA/reads.bam &&
-                  test -f ${CI_PROJECT_NAME}/cohort/sqanti_cohort/classification_summary.tsv
+                  test -f ${CI_PROJECT_NAME}/samples/sampleA/sampleA_sqanti/classification_summary.tsv
 
         # Smoke: end-to-end DE/DTU wiring and expected contrast output files.
         - if: $MATRIX_NAME == "smoke_de"

bin/workflow_glue/report.py

@@ -68,9 +68,9 @@ def _format_ratio_value(value):
     return f"{numeric:.2f}x"
 
 
-def _cohort_summary(cohort_dir):
+def _transcriptome_summary(transcriptome_dir):
-    """Return cohort-level metrics and transcript class counts DataFrames."""
+    """Return transcriptome metrics and transcript class counts DataFrames."""
-    tx_meta = _read_table(Path(cohort_dir) / "transcript_metadata.tsv")
+    tx_meta = _read_table(Path(transcriptome_dir) / "transcript_metadata.tsv")
     if tx_meta is None:
         return None, None
 
@@ -95,7 +95,10 @@ def _cohort_summary(cohort_dir):
 def _sample_summaries(samples_dir):
     """Return a dict of per-sample metrics DataFrames keyed by sample name."""
     summaries = {}
-    for sample_dir in sorted(Path(samples_dir).iterdir()):
+    samples_path = Path(samples_dir)
+    if not samples_path.exists() or not samples_path.is_dir():
+        return summaries
+    for sample_dir in sorted(samples_path.iterdir()):
         if not sample_dir.is_dir():
             continue
         tx_meta = _read_table(sample_dir / "transcript_metadata.tsv")
@@ -143,9 +146,9 @@ def _contrast_results(de_dir, filename, n=None):
     return tables
 
 
-def _load_bambu_qc(cohort_dir):
+def _load_bambu_qc(bambu_dir):
     """Load bambu QC statistics JSON."""
-    qc_file = Path(cohort_dir) / "bambu_qc_stats.json"
+    qc_file = Path(bambu_dir) / "bambu_qc_stats.json"
     if qc_file.exists():
         with open(qc_file) as f:
             return json.load(f)
@@ -345,8 +348,6 @@ def main(args):
                     use_index=False,
                 )
 
-    # Load bambu QC statistics
-    bambu_qc = _load_bambu_qc(args.cohort_dir)
     annotation_reference_summary = _load_annotation_reference_summary(args.cohort_dir)
 
     if annotation_reference_summary:
@@ -437,6 +438,16 @@ def main(args):
                 h4("Unstranded Annotation Examples")
                 pre("\n".join(examples))
 
+    # Setup for using cohort or single sample bambu results
+    is_single_sample = len(metadata) == 1
+    primary_label = metadata[0]["alias"] if is_single_sample else "Cohort"
+
+    # Load bambu QC statistics
+    bambu_dir = (
+        Path(args.samples_dir) / metadata[0]["alias"] if is_single_sample
+        else args.cohort_dir
+    )
+    bambu_qc = _load_bambu_qc(bambu_dir)
     # Add Bambu QC section with warnings
     if bambu_qc:
         with report.add_section("Bambu Quality Control", "Bambu QC"):
@@ -556,29 +567,39 @@ def main(args):
                     use_index=False,
                 )
 
-    with report.add_section("Cohort transcriptome", "Cohort"):
+    with report.add_section(
-        cohort_metrics, cohort_classes = _cohort_summary(args.cohort_dir)
+        f"{primary_label} transcriptome",
-        if cohort_metrics is not None:
+        f"{primary_label} transcriptome"
+    ):
+        transcriptome_metrics, transcriptome_classes = _transcriptome_summary(bambu_dir)
+        if transcriptome_metrics is not None:
             DataTable.from_pandas(
-                cohort_metrics,
+                transcriptome_metrics,
                 paging=False,
                 searchable=False,
                 use_index=False,
             )
-        if cohort_classes is not None:
+        if transcriptome_classes is not None:
             DataTable.from_pandas(
-                cohort_classes,
+                transcriptome_classes,
                 paging=False,
                 searchable=False,
                 use_index=False,
             )
 
-        tx_counts = _read_table(Path(args.cohort_dir) / "transcript_counts.tsv")
+        tx_counts = _read_table(Path(bambu_dir) / "transcript_counts.tsv")
         if tx_counts is not None and not tx_counts.empty:
-            p("Top transcript rows from the cohort abundance table.")
+            p(
+                "Top transcript rows from the "
+                f"{'sample' if is_single_sample else 'cohort'} abundance table."
+            )
             DataTable.from_pandas(tx_counts.head(20), use_index=False)
 
-    with report.add_section("Per-sample transcriptomes", "Per sample"):
+    if not is_single_sample:
+        with report.add_section(
+            "Per-sample transcriptomes",
+            "Per-sample transcriptomes"
+        ):
             tabs = Tabs()
             for sample, summary_df in _sample_summaries(args.samples_dir).items():
                 with tabs.add_tab(sample):

bin/workflow_glue/tests/common/test_report.py

@@ -277,8 +277,12 @@ def test_report_main_handles_degenerate_bambu_qc_and_read_summary(
             }
         ),
     )
+    samples = tmp_path / "samples"
+    samples.mkdir()
+    sample_a = samples / "sampleA"
+    sample_a.mkdir()
     _write(
-        cohort / "bambu_qc_stats.json",
+        sample_a / "bambu_qc_stats.json",
         json.dumps(
             {
                 "samples": 1,
@@ -298,10 +302,6 @@ def test_report_main_handles_degenerate_bambu_qc_and_read_summary(
         ),
     )
 
-    samples = tmp_path / "samples"
-    samples.mkdir()
-    (samples / "OPTIONAL_FILE").touch()
-
     sqanti = tmp_path / "sqanti"
     sqanti.mkdir()
     (sqanti / "OPTIONAL_FILE").touch()
@@ -346,6 +346,161 @@ def test_report_main_handles_degenerate_bambu_qc_and_read_summary(
     )
 
 
+def test_report_main_uses_cohort_bambu_qc_for_multi_sample_inputs(
+    monkeypatch,
+    tmp_path,
+):
+    """Multi-sample runs should render bambu QC from cohort-level outputs."""
+    tables = []
+
+    monkeypatch.setattr(report.labs, "LabsReport", _FakeReport)
+    monkeypatch.setattr(report, "Tabs", _FakeTabs)
+    monkeypatch.setattr(report, "p", lambda *args, **kwargs: None)
+    monkeypatch.setattr(report, "pre", lambda *args, **kwargs: None)
+    monkeypatch.setattr(report, "_create_warning_banner", lambda *args, **kwargs: None)
+    monkeypatch.setattr(report.fastcat, "SeqSummary", lambda *args, **kwargs: None)
+    monkeypatch.setattr(
+        report.DataTable,
+        "from_pandas",
+        staticmethod(lambda table, *args, **kwargs: tables.append(table.copy())),
+    )
+
+    metadata = _write(
+        tmp_path / "metadata.json",
+        json.dumps(
+            [
+                {"alias": "sampleA", "has_stats": False},
+                {"alias": "sampleB", "has_stats": False},
+            ]
+        ),
+    )
+    params = _write(tmp_path / "params.json", "{}")
+    versions = tmp_path / "versions"
+    versions.mkdir()
+    _write(versions / "versions.txt", "tool,1.0\n")
+
+    cohort = tmp_path / "cohort"
+    cohort.mkdir()
+    _write(
+        cohort / "bambu_qc_stats.json",
+        json.dumps(
+            {
+                "samples": 2,
+                "library_sizes": {"sampleA": 1200, "sampleB": 900},
+                "min_library_size": 900,
+                "max_library_size": 1200,
+                "median_library_size": 1050,
+                "library_size_ratio": 1.3333,
+                "total_transcripts_before_filter": 100,
+                "total_transcripts_after_filter": 80,
+                "transcripts_filtered": 20,
+                "median_transcripts_detected": 70,
+                "total_genes_after_filter": 60,
+                "transcriptome_mode": "discover",
+                "ndr_used": 0.1,
+            }
+        ),
+    )
+
+    samples = tmp_path / "samples"
+    samples.mkdir()
+    sample_a = samples / "sampleA"
+    sample_a.mkdir()
+    _write(
+        sample_a / "bambu_qc_stats.json",
+        json.dumps({"samples": 1, "library_sizes": {"sampleA": 5}}),
+    )
+    _write(
+        sample_a / "transcript_metadata.tsv",
+        "TXNAME\tGENEID\n"
+        "tx1\tgene1\n"
+        "tx2\tgene1\n"
+        "tx3\tgene2\n",
+    )
+    sample_b = samples / "sampleB"
+    sample_b.mkdir()
+    _write(
+        sample_b / "bambu_qc_stats.json",
+        json.dumps({"samples": 1, "library_sizes": {"sampleB": 7}}),
+    )
+    _write(
+        sample_b / "transcript_metadata.tsv",
+        "TXNAME\tGENEID\n"
+        "txA\tgeneA\n"
+        "txB\tgeneB\n",
+    )
+
+    sqanti = tmp_path / "sqanti"
+    sqanti.mkdir()
+    (sqanti / "OPTIONAL_FILE").touch()
+
+    alignment_stats = tmp_path / "alignment_stats"
+    alignment_stats.mkdir()
+    (alignment_stats / "OPTIONAL_FILE").touch()
+
+    out_report = tmp_path / "wf-transcriptomes-report.html"
+    args = report.argparser().parse_args(
+        [
+            str(out_report),
+            "--metadata",
+            str(metadata),
+            "--alignment_stats_dir",
+            str(alignment_stats),
+            "--cohort_dir",
+            str(cohort),
+            "--samples_dir",
+            str(samples),
+            "--sqanti_dir",
+            str(sqanti),
+            "--versions",
+            str(versions),
+            "--params",
+            str(params),
+        ]
+    )
+
+    report.main(args)
+
+    assert out_report.exists()
+    assert any(
+        "Samples analyzed" in table.to_string() and "2" in table.to_string()
+        for table in tables
+    )
+    assert any(
+        "Library size ratio (max/min)" in table.to_string()
+        and "1.33x" in table.to_string()
+        for table in tables
+    )
+    assert any(
+        "Sample" in table.columns
+        and "Library Size" in table.columns
+        and {"sampleA", "sampleB"}.issubset(set(table["Sample"].tolist()))
+        and "1,200" in table.to_string()
+        and "900" in table.to_string()
+        for table in tables
+    )
+    per_sample_metric_tables = [
+        table
+        for table in tables
+        if list(table.columns) == ["Metric", "Value"]
+        and set(table["Metric"].tolist()) == {"Transcripts", "Genes"}
+    ]
+    assert any(
+        set(zip(table["Metric"], table["Value"])) == {
+            ("Transcripts", 3),
+            ("Genes", 2),
+        }
+        for table in per_sample_metric_tables
+    )
+    assert any(
+        set(zip(table["Metric"], table["Value"])) == {
+            ("Transcripts", 2),
+            ("Genes", 2),
+        }
+        for table in per_sample_metric_tables
+    )
+
+
 def test_report_main_renders_statistical_methods_and_warnings(
     monkeypatch,
     tmp_path,

main.nf

@@ -39,7 +39,7 @@ process makeReport {
         tuple val(metadata), path(stats, stageAs: "stats_*")
         path "versions/*"
         path "params.json"
-        path cohort_dir, stageAs: "cohort"
+        path cohort_dir, stageAs: "cohort/*"
         path sample_dirs, stageAs: "samples/*"
         path sqanti_dirs, stageAs: "sqanti/*"
         path de_files
@@ -198,7 +198,7 @@ workflow pipeline {
             report_input,
             software_versions,
             workflow_params,
-            transcriptome.joint_dir,
+            transcriptome.joint_dir.ifEmpty(OPTIONAL_FILE),
             sample_dirs_for_report,
             sqanti_dirs_for_report,
             de_dir,

subworkflows/transcriptome.nf

@@ -222,6 +222,7 @@ workflow transcriptome_analysis {
         joint_discover = runJointBambuDiscover(
             alignments
                 .toSortedList { a, b -> a[0].alias <=> b[0].alias }
+                .filter { rows -> rows.size() > 1 }
                 .map { rows ->
                     // transform [meta, bam, bai] rows to 
                     // [meta, [alias1...aliasN], [bam1...bamN], [bai1...baiN], sample_sheet]