diff-visualizer
Rich downstream visualisation and reporting for bulk RNA-seq differential expression and scRNA marker/contrast outputs.
Best use case
diff-visualizer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Rich downstream visualisation and reporting for bulk RNA-seq differential expression and scRNA marker/contrast outputs.
Teams using diff-visualizer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/diff-visualizer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How diff-visualizer Compares
| Feature / Agent | diff-visualizer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Rich downstream visualisation and reporting for bulk RNA-seq differential expression and scRNA marker/contrast outputs.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 📈 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
| Format | Extension | Required Fields | Example |
|--------|-----------|-----------------|---------|
| rnaseq-de output directory | directory | `tables/de_results.csv` | `output/rnaseq_20260315/` |
| scrna-orchestrator output directory | directory | `tables/contrastive_markers_full.csv`, `tables/within_cluster_contrastive_markers_full.csv`, or `tables/markers_top.csv` | `output/scrna_20260315/` |
| Bulk DE table | `.csv`, `.tsv` | `gene`, `log2FoldChange`, plus `padj` or `pvalue` | `de_results.csv` |
| scRNA contrast table | `.csv`, `.tsv` | `names`, `scores` | `contrastive_markers_full.csv` |
| scRNA within-cluster contrast table | `.csv`, `.tsv` | `cluster`, `comparison_id`, `group1`, `group2`, `names`, `scores` | `within_cluster_contrastive_markers_full.csv` |
| scRNA markers table | `.csv`, `.tsv` | `cluster`, `names`, `scores` | `markers_top.csv` |
| Optional bulk counts | `.csv`, `.tsv` | gene rows, sample columns, first column gene id | `counts.csv` |
| Optional bulk metadata | `.csv`, `.tsv` | `sample_id` | `metadata.csv` |
| Optional AnnData | `.h5ad` | expression matrix plus gene names in `var_names` | `subset.h5ad` |
## 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
```bash
# 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
```bash
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
```text
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/Related Skills
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