bioinformatics-init-analysis

id	bioinformatics-init-analysis
name	bioinformatics-init-analysis
version	1.0.0
description	# bioinformatics-init-analysis
stages	["experiment"]
tools	["read_file","search_project","write_file","run_terminal"]
summary	# bioinformatics-init-analysis
primaryIntent	research
intents	["research"]
capabilities	["research-planning","data-processing"]
domains	["bioinformatics"]
keywords	["bioinformatics-init-analysis","experiment"]
source	builtin
status	verified
upstream	{"repo":"dr-claw","path":"skills/bioinformatics-init-analysis","revision":"8322dc4ef575affaa374aa7922c0a0971c6db7d7"}
resourceFlags	{"hasReferences":true,"hasScripts":true,"hasTemplates":false,"hasAssets":false,"referenceCount":4,"scriptCount":10,"templateCount":0,"assetCount":0,"optionalScripts":true}

bioinformatics-init-analysis

Canonical Summary

bioinformatics-init-analysis

Trigger Rules

Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.

Resource Use Rules

• Read from references/ only when the current task needs the extra detail.
• Treat scripts/ as optional helpers. Run them only when their dependencies are available, keep outputs in the project workspace, and explain a manual fallback if execution is blocked.

Execution Contract

• Resolve every relative path from this skill directory first.
• Prefer inspection before mutation when invoking bundled scripts.
• If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
• Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

Upstream Instructions

bioinformatics-init-analysis

A Claude Code plugin that automates initial data analysis for high-dimensional single-cell biology data. Supports CyTOF (mass cytometry), scRNA-seq, and flow cytometry with automatic data type detection and plain-language clinical report generation.

Features

• 7-step pipeline: Load → QC → Normalize → PCA/UMAP → Cluster → Marker Analysis → Report
• Auto-detection: Identifies CyTOF, scRNA-seq, or flow cytometry from file format and marker patterns
• Clinical reports: HTML reports with plain-language explanations for medical doctors and non-bioinformaticians
• Data-type-aware: QC, normalization, and interpretation adapt to data type
• Modular: Run the full pipeline or import individual steps

Installation

Clone into your Claude Code plugins directory:

git clone https://github.com/<your-username>/bioinformatics-init-analysis.git \
    ~/.claude/plugins/bioinformatics-init-analysis

Dependencies

pip install scanpy anndata matplotlib seaborn scipy scikit-learn pandas numpy
# Optional: fcsparser (for .fcs flow cytometry files)

Usage

As a Claude Code Plugin

Once installed, trigger the skill in Claude Code with phrases like:

• "Run initial analysis on my CyTOF data"
• "QC my single-cell data"
• "Analyze and generate a report for my dataset"

Command Line

python3 scripts/run_pipeline.py <input_path> \
    [--data-type auto|cytof|scrnaseq|flow] \
    [--subsample 500] \
    [--output-dir ./analysis_output] \
    [--report-style clinical|technical]

Examples

# CyTOF directory of CSVs (auto-detected)
python3 scripts/run_pipeline.py /path/to/cytof_csvs/

# scRNA-seq h5ad file with technical report
python3 scripts/run_pipeline.py /path/to/data.h5ad --report-style technical

# Flow cytometry with more cells per sample
python3 scripts/run_pipeline.py /path/to/data.fcs --subsample 2000

Output

analysis_output/
├── figures/                    # All generated plots (PNG)
├── processed/
│   └── adata_processed.h5ad   # Processed AnnData object
├── report.html                 # HTML report with embedded figures
└── analysis_summary.json       # Machine-readable summary statistics

Plugin Structure

bioinformatics-init-analysis/
├── .claude-plugin/
│   └── plugin.json             # Plugin manifest
├── skills/
│   └── init-analysis/
│       └── SKILL.md            # Skill definition (triggers, usage)
├── scripts/
│   ├── run_pipeline.py         # Main CLI entry point
│   ├── detect_data_type.py     # Auto-detection logic
│   ├── utils.py                # Shared utilities
│   ├── step1_load_data.py      # Universal data loader
│   ├── step2_qc.py             # Data-type-aware QC
│   ├── step3_normalize.py      # Normalization (arcsinh/CPM+log1p)
│   ├── step4_dim_reduction.py  # PCA + UMAP
│   ├── step5_clustering.py     # Leiden clustering + evaluation
│   ├── step6_marker_analysis.py# DE, correlation, treatment response
│   └── step7_report.py         # HTML report generator
├── references/
│   ├── plot_interpretation_guide.md  # How to read each plot type
│   ├── cytof_specifics.md            # CyTOF data handling
│   ├── scrnaseq_specifics.md         # scRNA-seq data handling
│   └── statistical_methods.md        # Stats glossary for non-experts
└── assets/                     # (reserved for future templates)

License

MIT