major changes to the report style

src/pyprideap/cli.py

@@ -92,6 +92,7 @@ def _generate_report(
     split: bool = False,
     sdrf_path: Path | None = None,
     no_border: bool = True,
+    two_sides: bool = True,
 ) -> Path:
     """Read a data file and generate a QC report."""
     import pyprideap as pp
@@ -114,7 +115,7 @@ def _generate_report(
 
         click.echo("Generating individual plot files...")
         logger.debug("Output directory: %s", output_path)
-        result = qc_report_split(ds, output_path, no_border=no_border)
+        result = qc_report_split(ds, output_path, no_border=no_border, two_sides=two_sides)
         n_files = len(list(result.glob("*.html")))
         click.echo(f"  {n_files} HTML files saved to {result}/")
         return result
@@ -161,6 +162,11 @@ def main() -> None:
 @click.option(
     "--no-border/--border", default=True, help="Remove card borders from split plot files (default: no border)."
 )
+@click.option(
+    "--two-sides/--no-two-sides",
+    default=True,
+    help="Generate combined plot files for side-by-side panel layouts (default: enabled).",
+)
 @click.option("-v", "--verbose", is_flag=True, default=False, help="Enable verbose logging output.")
 def report(
     input_file: str | None,
@@ -170,6 +176,7 @@ def report(
     split: bool,
     sdrf: str | None,
     no_border: bool,
+    two_sides: bool,
     verbose: bool,
 ) -> None:
     """Generate a QC report from a data file or PAD accession."""
@@ -203,7 +210,15 @@ def report(
                         out = Path(f"{output_path}/{stem}")
                     else:
                         out = Path(f"{output_path}/{stem}.html")
-                    _generate_report(f, out, platform=platform, split=split, sdrf_path=sdrf_path, no_border=no_border)
+                    _generate_report(
+                        f,
+                        out,
+                        platform=platform,
+                        split=split,
+                        sdrf_path=sdrf_path,
+                        no_border=no_border,
+                        two_sides=two_sides,
+                    )
                 except Exception as e:
                     logger.debug("Error processing %s: %s", f.name, e, exc_info=True)
                     click.echo(f"  Skipping {f.name}: {e}", err=True)
@@ -219,6 +234,7 @@ def report(
             split=split,
             sdrf_path=sdrf_path,
             no_border=no_border,
+            two_sides=two_sides,
         )
 
 

src/pyprideap/viz/qc/compute.py

@@ -88,7 +88,7 @@ class DataCompletenessData:
     below_lod_rate: list[float]  # per-sample fraction below LOD (0-1)
     protein_ids: list[str] = field(default_factory=list)  # per-protein identifiers
     missing_freq: list[float] = field(default_factory=list)  # per-protein fraction below LOD (0-1)
-    title: str = "Data Completeness"
+    title: str = "Sample Completeness"
 
 
 @dataclass

src/pyprideap/viz/qc/render.py

@@ -580,7 +580,7 @@ def render_data_completeness(data: DataCompletenessData) -> Figure:
     fig = make_subplots(
         rows=2,
         cols=1,
-        subplot_titles=["Per-Sample Data Completeness", "Missing Frequency Distribution"],
+        subplot_titles=["Sample Completeness", "Missing Frequency"],
         vertical_spacing=0.35,
     )
 
@@ -670,6 +670,94 @@ def render_data_completeness(data: DataCompletenessData) -> Figure:
     return fig
 
 
+def render_sample_completeness(data: DataCompletenessData) -> Figure:
+    """Per-sample stacked bar showing above/below LOD as standalone plot."""
+    go, _ = _import_plotly()
+
+    _MAX_LABEL = 20
+    short_ids = [s if len(s) <= _MAX_LABEL else s[:_MAX_LABEL] + "\u2026" for s in data.sample_ids]
+
+    fig = go.Figure()
+    fig.add_trace(
+        go.Bar(
+            x=short_ids,
+            y=[r * 100 for r in data.above_lod_rate],
+            name="Above LOD",
+            marker_color="#2ecc71",
+            customdata=data.sample_ids,
+            hovertemplate="%{customdata}<br>Above LOD: %{y:.1f}%<extra></extra>",
+        ),
+    )
+    fig.add_trace(
+        go.Bar(
+            x=short_ids,
+            y=[r * 100 for r in data.below_lod_rate],
+            name="Below LOD",
+            marker_color="#f39c12",
+            customdata=data.sample_ids,
+            hovertemplate="%{customdata}<br>Below LOD: %{y:.1f}%<extra></extra>",
+        ),
+    )
+    fig.update_xaxes(title_text="", tickangle=-45)
+    fig.update_yaxes(title_text="% of Proteins", range=[0, 100], ticksuffix="%")
+    fig.update_layout(
+        title="Sample Completeness",
+        barmode="stack",
+        height=500,
+        legend=dict(orientation="h", yanchor="top", y=-0.18),
+        margin=dict(b=100),
+    )
+    return fig
+
+
+def render_missing_frequency(data: DataCompletenessData) -> Figure:
+    """Per-protein missing frequency histogram as standalone plot."""
+    go, _ = _import_plotly()
+
+    fig = go.Figure()
+    if data.missing_freq:
+        missing_pct = [f * 100 for f in data.missing_freq]
+        fig.add_trace(
+            go.Histogram(
+                x=missing_pct,
+                nbinsx=25,
+                marker_color="#e74c3c",
+                showlegend=False,
+            ),
+        )
+        n_below_30 = sum(1 for f in data.missing_freq if f < 0.30)
+        pct_below_30 = n_below_30 / len(data.missing_freq) * 100 if data.missing_freq else 0
+        fig.add_annotation(
+            text=f"{pct_below_30:.1f}% of proteins below 30% missing",
+            xref="paper",
+            yref="paper",
+            x=0.98,
+            y=0.98,
+            xanchor="right",
+            yanchor="top",
+            showarrow=False,
+            font=dict(size=12),
+            bgcolor="rgba(255,255,255,0.8)",
+        )
+        fig.add_vline(
+            x=30,
+            line_dash="dash",
+            line_color="#e67e22",
+            line_width=2,
+            annotation_text="30% threshold",
+            annotation_position="bottom right",
+            annotation_font_color="#e67e22",
+        )
+    fig.update_xaxes(title_text="Missing Frequency (% Samples Below LOD)", range=[0, 100])
+    fig.update_yaxes(title_text="Number of Proteins")
+    fig.update_layout(
+        title="Missing Frequency Distribution",
+        height=500,
+        margin=dict(b=100),
+    )
+    return fig
+
+
 def render_cv_distribution(data: CvDistributionData) -> Figure:
     go, _ = _import_plotly()
     fig = go.Figure(data=[go.Histogram(x=data.cv_values, nbinsx=50)])