Merge branch 'transcriptome_table_order' into 'dev'

Report minor fixes [CW-7281]

See merge request epi2melabs/workflows/wf-transcriptomes!309

bin/workflow_glue/report.py

@@ -112,6 +112,24 @@ def _format_ratio_value(value):
     return f"{numeric:.2f}x"
 
 
+def _sorted_transcript_abundance_table(tx_counts_file, sample_aliases):
+    """Get transcript abundance rows by descending total abundance of sample columns."""
+    tx_counts = _read_table(tx_counts_file)
+    if tx_counts is None or tx_counts.empty:
+        return tx_counts
+
+    count_columns = [
+        column for column in tx_counts.columns if column in set(sample_aliases)
+    ]
+    if not count_columns:
+        return tx_counts
+
+    numeric_counts = tx_counts[count_columns].apply(pd.to_numeric, errors="coerce")
+    abundance = numeric_counts.sum(axis=1, min_count=1)
+    order = abundance.fillna(float("-inf")).sort_values(ascending=False).index
+    return tx_counts.loc[order].reset_index(drop=True)
+
+
 def _format_classification_label(name):
     """Return canonical report label for a summary classification value."""
     return str(name).strip().replace("-", "_").replace("_", " ").capitalize()
@@ -380,12 +398,38 @@ def _contrast_results(de_dir, filename, n=None):
         data = table
         if n is not None:
             data = data.head(n)
+        if "padj" in data.columns:
             data.sort_values("padj", ascending=True, inplace=True)
         tables[contrast_dir.name] = data
 
     return tables
 
 
+def _round_de_table(table):
+    """Return a display-formatted copy of a DE/DTU result table."""
+    rounded_columns = [
+        "baseMean", "exonBaseMean", "log2FoldChange", "lfcSE", "stat", "pvalue", "padj"
+    ]
+
+    def _format_numeric(value):
+        numeric_value = pd.to_numeric(value, errors="coerce")
+        if pd.isna(numeric_value):
+            return value
+        fixed_decimal = f"{numeric_value:.3f}"
+        if numeric_value != 0 and (
+            abs(numeric_value) < 0.0001 or fixed_decimal in {"0.000", "-0.000"}
+        ):
+            return f"{numeric_value:.3e}"
+        return fixed_decimal
+
+    rounded = table.copy()
+    for column in rounded_columns:
+        if column not in rounded.columns:
+            continue
+        rounded[column] = rounded[column].map(_format_numeric)
+    return rounded
+
+
 def _load_bambu_qc(bambu_dir):
     """Load bambu QC statistics JSON."""
     qc_file = Path(bambu_dir) / "bambu_qc_stats.json"
@@ -911,13 +955,17 @@ def main(args):
                 use_index=False,
             )
 
-        tx_counts = _read_table(Path(bambu_dir) / "transcript_counts.tsv")
+        tx_counts = _sorted_transcript_abundance_table(
+            Path(bambu_dir) / "transcript_counts.tsv",
+            [item["alias"] for item in metadata]
+        )
         if tx_counts is not None and not tx_counts.empty:
             p(
-                "Top transcript rows from the "
+                f"Top {args.de_table_size} most abundant transcripts"
-                f"{'sample' if is_single_sample else 'cohort'} abundance table."
             )
-            DataTable.from_pandas(tx_counts.head(20), use_index=False)
+            DataTable.from_pandas(tx_counts.head(args.de_table_size), use_index=False)
+        else:
+            _create_warning_banner("No trancrips discovered")
 
     if not is_single_sample:
         with report.add_section(
@@ -934,15 +982,6 @@ def main(args):
                         use_index=False,
                     )
 
-    if args.alignment_stats_dir and Path(args.alignment_stats_dir).exists():
-        with report.add_section("Alignment statistics", "Alignments"):
-            tabs = Tabs()
-            for stats_file in sorted(
-                Path(args.alignment_stats_dir).glob("*.flagstat.txt")
-            ):
-                with tabs.add_tab(stats_file.stem.replace(".flagstat", "")):
-                    pre(stats_file.read_text())
-
     sqanti_table = _sqanti_table(args.sqanti_dir)
     if sqanti_table is not None and not sqanti_table.empty:
         with report.add_section("SQANTI3 classification", "SQANTI3"):
@@ -1296,7 +1335,8 @@ def main(args):
                                     strong("Note: ")
                                     raw(contrast_data["dtu_power_warning"])
                     DataTable.from_pandas(
-                        table.head(args.de_table_size), use_index=False
+                        _round_de_table(table.head(args.de_table_size)),
+                        use_index=False
                     )
                     with div(cls="clustering-info"):
                         raw(
@@ -1373,7 +1413,8 @@ def main(args):
                     if contrast_name in dtu_tables:
                         dtu_table = dtu_tables[contrast_name]
                         DataTable.from_pandas(
-                            dtu_table.head(args.de_table_size), use_index=False
+                            _round_de_table(dtu_table.head(args.de_table_size)),
+                            use_index=False
                         )
                         with div(cls="clustering-info"):
                             raw(
@@ -1400,11 +1441,6 @@ def argparser():
     parser.add_argument("report", help="Report output file.")
     parser.add_argument("--metadata", required=True, help="Sample metadata JSON.")
     parser.add_argument("--stats", nargs="+", help="Per-read stats paths.")
-    parser.add_argument(
-        "--alignment_stats_dir",
-        default=None,
-        help="Alignment stats directory.",
-    )
     parser.add_argument(
         "--cohort_dir",
         required=True,

bin/workflow_glue/tests/common/test_report.py

@@ -3,6 +3,7 @@
 import json
 from pathlib import Path
 
+import pandas as pd
 from workflow_glue import report
 
 
@@ -110,11 +111,6 @@ def _build_report_args(tmp_path, de_qc=None):
     mod_summaries.mkdir()
     sqanti = tmp_path / "sqanti"
     sqanti.mkdir()
-    alignment_stats = tmp_path / "alignment_stats"
-    alignment_stats.mkdir()
-    (samples / "OPTIONAL_FILE").touch()
-    (sqanti / "OPTIONAL_FILE").touch()
-    (alignment_stats / "OPTIONAL_FILE").touch()
 
     de_dir = None
     if de_qc is not None:
@@ -143,8 +139,6 @@ def _build_report_args(tmp_path, de_qc=None):
         str(out_report),
         "--metadata",
         str(metadata),
-        "--alignment_stats_dir",
-        str(alignment_stats),
         "--cohort_dir",
         str(cohort),
         "--ref_summary",
@@ -225,18 +219,12 @@ def test_report_main_accepts_optional_file_sentinels(monkeypatch, tmp_path):
     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),
             "--ref_summary",
@@ -417,8 +405,6 @@ def test_report_main_handles_degenerate_bambu_qc_and_read_summary(
             str(out_report),
             "--metadata",
             str(metadata),
-            "--alignment_stats_dir",
-            str(alignment_stats),
             "--stats",
             str(alignment_stats),
             "--cohort_dir",
@@ -556,18 +542,12 @@ def test_report_main_uses_cohort_bambu_qc_for_multi_sample_inputs(
     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),
             "--ref_summary",
@@ -773,3 +753,64 @@ def test_report_main_tolerates_missing_statistical_fields(monkeypatch, tmp_path)
     ]
     assert method_tables
     assert "parametric" in method_tables[0].to_string()
+
+
+def test_round_de_table_formats_supported_numeric_columns():
+    """DE/DTU preview tables should round known numeric columns for display."""
+    table = pd.DataFrame(
+        {
+            "GENEID": ["gene1"],
+            "baseMean": [123.4567],
+            "log2FoldChange": [0.00001234],
+            "lfcSE": [0.98765],
+            "stat": [-45.6789],
+            "pvalue": [0.00001234],
+            "padj": [0.123456],
+            "other": [7.89123],
+        }
+    )
+
+    rounded = report._round_de_table(table)
+
+    assert rounded.loc[0, "baseMean"] == "123.457"
+    assert rounded.loc[0, "log2FoldChange"] == "1.234e-05"
+    assert rounded.loc[0, "lfcSE"] == "0.988"
+    assert rounded.loc[0, "stat"] == "-45.679"
+    assert rounded.loc[0, "pvalue"] == "1.234e-05"
+    assert rounded.loc[0, "padj"] == "0.123"
+    assert rounded.loc[0, "other"] == 7.89123
+
+
+def test_round_de_table_uses_scientific_notation_when_fixed_decimal_would_zero():
+    """Tiny non-zero values should not display as 0.000 in DE/DTU tables."""
+    table = pd.DataFrame(
+        {
+            "GENEID": ["gene1"],
+            "pvalue": [0.00012],
+            "padj": [0.00049],
+        }
+    )
+
+    rounded = report._round_de_table(table)
+
+    assert rounded.loc[0, "pvalue"] == "1.200e-04"
+    assert rounded.loc[0, "padj"] == "4.900e-04"
+
+
+def test_sorted_transcript_abundance_table_uses_sample_alias_columns(tmp_path):
+    """Transcript abundance sorting should use only real sample alias columns."""
+    tx_counts_file = _write(
+        tmp_path / "transcript_counts.tsv",
+        (
+            "TXNAME\tsampleA\tsampleB\tgene_length\tannotation_score\n"
+            "tx_low\t1\t1\t10000\t999\n"
+            "tx_high\t5\t5\t10\t1\n"
+            "tx_mid\t2\t2\t5000\t500\n"
+        ),
+    )
+
+    sorted_table = report._sorted_transcript_abundance_table(
+        tx_counts_file, ["sampleA", "sampleB"]
+    )
+
+    assert sorted_table["TXNAME"].tolist() == ["tx_high", "tx_mid", "tx_low"]