proteomics-de

name	proteomics-de
description	Load when computing two-group differential protein abundance (group2 vs group1, log2FC + p-value + BH-adjusted FDR) via Welch t-test, equal-variance t-test, or Mann-Whitney on a wide protein × sample CSV. Skip when you need multi-condition DE (run pairwise contrasts manually) or label-based TMT linear-mixed models.
version	0.5.0
author	OmicsClaw
license	MIT
tags	["proteomics","differential-expression","ttest","welch","mann-whitney","bh-fdr"]
requires	["pandas","numpy","scipy"]

proteomics-de

When to use

The user has a wide protein × sample CSV (rows = proteins as index, columns = samples) and wants two-group differential abundance. Three backends:

• ttest (default) — Student's two-sample t-test (equal variance).
• welch — Welch's t-test (unequal variance).
• mann_whitney — non-parametric Mann-Whitney U.

All return per-protein log2fc (group2 vs group1), pvalue, and BH-adjusted padj. --alpha controls significance threshold for the tables/significant.csv shortlist; --log2fc-threshold optionally adds an absolute log2FC filter.

For multi-condition DE, run pairwise contrasts manually. For label-based TMT linear-mixed models, use MSstats / limma in R.

Inputs & Outputs

Input	Format	Required
Protein × sample table	.csv with protein index in column 0 and equal-count sample columns	yes (unless --demo)
Method	--method {ttest,welch,mann_whitney} (default ttest)	no
Significance	--alpha <float> (default 0.05); --log2fc-threshold <float> (default 0.0)	no

Output	Path	Notes
Full results	tables/differential_abundance.csv	per-protein log2fc, pvalue, padj, group1_mean, group2_mean
Significant subset	tables/significant.csv	filtered by --alpha (and optional --log2fc-threshold)
Report	report.md + result.json	summary["method"], summary["n_tested"], summary["n_significant"]

Flow

1. Load CSV with pd.read_csv(args.input_path, index_col=0) (proteomics_de.py:289); split columns at midpoint — first half = group1, second half = group2 (:290-292). NO CLI flag for prefix/suffix.
2. Dispatch on --method (proteomics_de.py:295); per-protein test → log2fc (mean(log2(g2)) − mean(log2(g1))) + raw pvalue.
3. Apply BH FDR adjustment (proteomics_de.py:137 / :178) → padj column.
4. Filter padj < args.alpha (and |log2fc| ≥ args.log2fc_threshold if > 0) → tables/significant.csv.
5. Write tables/differential_abundance.csv (proteomics_de.py:299) + tables/significant.csv (:306) + report.md + result.json (:322).

Gotchas

• Group assignment is by COLUMN POSITION — first half / second half. proteomics_de.py:290-292 splits data.columns[:mid] vs data.columns[mid:]. There is NO CLI flag for control / treatment prefixes; if your CSV columns are interleaved, pre-sort them. Demo uses control_1..N then treatment_1..N (:204-205).
• Index column 0 is treated as the protein ID. pd.read_csv(args.input_path, index_col=0) (proteomics_de.py:289) is unconditional — make sure your protein-ID column is the FIRST column in the CSV.
• Unknown --method raises ValueError. proteomics_de.py:192 rejects values outside ("ttest", "welch", "mann_whitney") — argparse choices= enforces this at parse time too.
• --input REQUIRED unless --demo. proteomics_de.py:288 raises ValueError("--input required").
• log2FC direction: group2 minus group1. Positive log2fc means group2 > group1. If your "control" is in the second half of columns, you'll get inverted signs — the script does NOT auto-detect direction.
• NaN handling differs per backend. ttest / welch (proteomics_de.py:116-118) drop rows where either group's mean is non-finite (np.isfinite filter). mann_whitney (:150-151) additionally drops 0 values (g1 > 0, g2 > 0) — small placeholder intensities silently disappear from Mann-Whitney runs but stay in t-test runs. Pre-impute zeros if you need consistent behaviour.

Key CLI

# Demo
python omicsclaw.py run proteomics-de --demo --output /tmp/de_demo

# Real CSV (first half = group1, second half = group2)
python omicsclaw.py run proteomics-de \
  --input protein_abundance.csv --output results/ \
  --method welch --alpha 0.05 --log2fc-threshold 1.0

# Mann-Whitney (non-parametric)
python omicsclaw.py run proteomics-de \
  --input protein_abundance.csv --output results/ \
  --method mann_whitney --alpha 0.01

See also

• references/parameters.md — every CLI flag
• references/methodology.md — t-test / Welch / Mann-Whitney trade-offs, BH FDR
• references/output_contract.md — tables/differential_abundance.csv schema
• Adjacent skills: proteomics-quantification (upstream — produces protein abundance), proteomics-data-import (upstream — schema normalisation), proteomics-enrichment (downstream — pathway enrichment on significant proteins), proteomics-ptm (parallel — PTM site analysis)