📊 Proteomics MS-QC

Mass spectrometry data quality control. Computes basic QC statistics for protein/peptide abundance tables.

CLI Reference

python omicsclaw.py run proteomics-ms-qc --demo
python omicsclaw.py run proteomics-ms-qc --input <data.csv> --output <dir>

Why This Exists

• Without it: Instrument drift, missed cleavages, or poor LC gradients ruin quantitative integrity
• With it: Identifies bad samples early before costly downstream statistical processing
• Why OmicsClaw: Provides a unified mass-spectrometer agnostic report dashboard

Workflow

1. Calculate: Extract basic peptide features and contaminant ratios.
2. Execute: Run descriptive statistics across raw files.
3. Assess: Flag outliers outside expected robust median ranges.
4. Generate: Output normalized QC matrices.
5. Report: Synthesize multiple metric traces across runs.

Example Queries

• "Run mass spec QC on this data using PTXQC"
• "Assess proteomics instrument performance"

Output Structure

output_directory/
├── report.md
├── result.json
├── metrics.csv
├── figures/
│   └── qc_dashboard.pdf
├── tables/
│   └── qc_summary.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Safety

• Local-first: Strict offline processing without external upload.
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• data-import

  — Upstream format parsing

• quantification

  — Downstream normalized feature tables

Citations

• PTXQC