| name | proteomics-ms-qc |
| description | Load when computing protein-table QC — proteins × samples count, missing-value rate, intensity CV (median + mean) — from a MaxQuant / FragPipe / DIA-NN protein-quantification CSV. Skip when raw mzML / RAW spectra are the input (run a search engine first) or when peptide-level QC is needed (use `proteomics-identification`). |
| version | 0.5.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["proteomics","qc","ms","maxquant","intensity","missing-values","cv"] |
| requires | ["pandas","numpy"] |
proteomics-ms-qc
When to use
The user has a protein-quantification CSV (typically the output of
proteomics-data-import, with rows = proteins and columns =
samples + metadata) and wants QC summary statistics: protein count,
sample count, fraction of missing intensities, per-protein
coefficient of variation (CV) — median and mean. Auto-detects
intensity columns by select_dtypes(include=[np.number]).
This skill does NOT process raw spectra. For peptide / PSM-level
identification stats use proteomics-identification.
Inputs & Outputs
| Input | Format | Required |
|---|
| Protein table | .csv with at least one numeric (intensity) column | yes (unless --demo) |
| Output | Path | Notes |
|---|
| QC metrics | tables/qc_metrics.csv | one-row table — n_proteins / n_samples / missing_rate / median_cv / mean_cv |
| Report | report.md + result.json | always |
Flow
- Load CSV (
--input <file.csv>) or generate a demo at output_dir/demo_proteomics.csv (proteomics_ms_qc.py:223).
- Detect numeric (intensity) columns via
select_dtypes(include=[np.number]) (proteomics_ms_qc.py:47); raise ValueError("No intensity/sample columns detected in input data") at :74 if none found.
- Compute n_proteins / n_samples / missing_rate / per-protein CV.
- Write
tables/qc_metrics.csv (proteomics_ms_qc.py:241) + report.md + result.json.
Gotchas
- Sample columns must be NUMERIC. Intensity-column auto-detection (
proteomics_ms_qc.py:47) uses select_dtypes(include=[np.number]). String-typed intensities (e.g. quoted numbers in some Spectronaut exports) are silently treated as metadata, not samples — your n_samples will be 0 and the run raises ValueError at :74.
- No intensity columns ⇒ hard fail.
proteomics_ms_qc.py:74 raises ValueError("No intensity/sample columns detected in input data") — there is no auto-detection of intensity_* prefixes; only dtype-based.
--input REQUIRED unless --demo. proteomics_ms_qc.py:228 raises ValueError("--input required when not using --demo").- Both
NaN and 0.0 count as missing. proteomics_ms_qc.py:80 computes missing_mask = np.isnan(intensities) | (intensities == 0) — zero is treated as "not detected" (the proteomics convention). If your search engine writes a small placeholder (e.g. 1.0) for undetected proteins, the missing rate is artificially LOW; pre-impute placeholders to 0 or NaN first.
- CV is per-protein across samples. Reported
median_cv / mean_cv are aggregations across the per-protein CV distribution — interpret as "typical protein-level reproducibility", not "sample-level reproducibility".
Key CLI
python omicsclaw.py run proteomics-ms-qc --demo --output /tmp/qc_demo
python omicsclaw.py run proteomics-ms-qc \
--input results/tables/proteins.csv --output qc_results/
See also
references/parameters.md — every CLI flagreferences/methodology.md — CV definition, missing-value handlingreferences/output_contract.md — tables/qc_metrics.csv schema- Adjacent skills:
proteomics-data-import (upstream — produces the protein table), proteomics-quantification (downstream — LFQ / iBAQ / spectral count), proteomics-identification (parallel — peptide-level summary), proteomics-de (downstream — differential abundance)
