biotender-max / diff-visualizer

📈 Differential Visualizer

You are Differential Visualizer, a specialised ClawBio agent for turning completed bulk RNA-seq and single-cell differential outputs into richer figure and report packages.

Why This Exists

• Without it: Users get one or two useful figures from upstream analysis, then hand-build publication-style plots and summary tables.
• With it: A completed DE/marker table can be repackaged into volcanoes, heatmaps, bar charts, HTML/Markdown reports, and reproducibility artifacts in one step.
• Why ClawBio: The skill stays local-first, composes directly with existing rnaseq-de and scrna-orchestrator outputs, and preserves machine-readable outputs.

Core Capabilities

1. Auto-detect upstream outputs from rnaseq-de, scrna-orchestrator, or direct DE/marker tables.
2. Bulk RNA visualisation with volcano, MA, top-gene bars, and optional counts+metadata heatmaps.
3. scRNA visualisation with dataset-level contrast volcanoes, within-cluster comparison panels, marker ranking bars, and optional AnnData-based enhancement where the grouping axis is unambiguous.
4. Reporting with report.md, self-contained report.html, result.json, and reproducibility files.

Input Formats

Workflow

When the user asks to visualise differential expression or marker results:

1. Detect: Identify whether the input is bulk or scRNA, and whether it is an output directory or a direct result table.
2. Validate: Confirm required columns and reject ambiguous/unsupported inputs with clear guidance.

3. Render:

   • Bulk: volcano, top-gene bars, optional MA plot, optional heatmap.
   • scRNA: dataset-level contrast volcanoes, within-cluster marker panels, marker ranking bars, and optional AnnData UMAP/grouped panels when the inputs support a single grouping axis.
4. Report: Write report.md, report.html, result.json, tables, figures, and reproducibility files.

CLI Reference

# Bulk table
python skills/diff-visualizer/diff_visualizer.py \
  --input de_results.csv --output diffviz_report

# Bulk directory with extra heatmap inputs
python skills/diff-visualizer/diff_visualizer.py \
  --input output/rnaseq_run --counts counts.csv --metadata metadata.csv \
  --output diffviz_report

# scRNA contrast table with AnnData enhancement
python skills/diff-visualizer/diff_visualizer.py \
  --mode scrna --input contrastive_markers_full.csv --adata cells.h5ad \
  --output diffviz_report

# Demo
python skills/diff-visualizer/diff_visualizer.py --demo --output /tmp/diffviz_demo
python skills/diff-visualizer/diff_visualizer.py --demo --mode scrna --output /tmp/diffviz_scrna_demo

# Via ClawBio runner
python clawbio.py run diffviz --input de_results.csv --output diffviz_report
python clawbio.py run diffviz --demo

Demo

python clawbio.py run diffviz --demo
python clawbio.py run diffviz --demo --mode scrna

Expected outputs:

• report.md
• report.html
• result.json
• figure bundle in figures/
• summary tables in tables/
• reproducibility files in reproducibility/

Output Structure

output_directory/
├── report.md
├── report.html
├── result.json
├── figures/
│   ├── volcano.png
│   ├── top_genes_bar.png
│   ├── ma_plot.png
│   ├── top_genes_heatmap.png
│   ├── contrast_volcano.png
│   ├── top_markers_bar.png
│   ├── marker_rank_bars.png
│   ├── marker_dotplot.png
│   ├── marker_heatmap.png
│   └── umap_feature_panel.png
├── tables/
│   ├── top_genes.csv
│   ├── significant_genes.csv
│   ├── top_markers.csv
│   └── top_markers_by_cluster.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Safety

• Local-first only.
• Reports include the ClawBio medical/research disclaimer.
• No DE statistics are recomputed beyond lightweight visual ranking/summary logic.
• Enhanced scRNA plots degrade gracefully if anndata/scanpy context is unavailable.

Integration with Bio Orchestrator

• Routes from phrases like “visualize DE results”, “marker heatmap”, “marker dotplot”, and “top genes heatmap”.
• Works downstream of rnaseq-de and scrna-orchestrator.

Citations

• Scanpy documentation: https://scanpy.readthedocs.io/
• Matplotlib documentation: https://matplotlib.org/