proteomics-quantification

name	proteomics-quantification
description	Load when computing per-protein abundance from a peptide / PSM table via LFQ (intensity summation), iBAQ (intensity / tryptic peptide count), or spectral counting (PSMs per protein). Skip when the input is already protein-level (use `proteomics-ms-qc` for QC) or for label-based TMT / iTRAQ workflows (search upstream first).
version	0.5.0
author	OmicsClaw
license	MIT
tags	["proteomics","quantification","lfq","ibaq","spectral-counting"]
requires	["pandas","numpy"]

proteomics-quantification

When to use

The user has a peptide / PSM table and wants protein-level abundance via one of:

• lfq (default) — Label-Free Quantification by intensity summation. Requires an intensity column.
• ibaq — intensity-Based Absolute Quantification (intensity / theoretical tryptic peptide count). Requires an intensity column AND ONE OF: a per-protein sequence column (in-silico digested by the script) OR a pre-computed n_theoretical_peptides integer column. Without either, the script silently estimates unique_peptides × 1.5.
• spectral_count — PSM count per protein (no intensity needed).

Pick with --method {lfq,spectral_count,ibaq} (default lfq). For TMT / iTRAQ label-based workflows, perform the search-engine quant first; this skill is intensity- / count-only.

Inputs & Outputs

Input	Format	Required
Peptide / PSM table	.csv with protein column. intensity required for lfq / ibaq; for ibaq ALSO either sequence (per-protein AA sequence, in-silico digested at proteomics_quantification.py:42-72) OR n_theoretical_peptides (pre-computed integer); PSM rows for spectral_count	yes (unless --demo)
Method	--method {lfq,spectral_count,ibaq} (default lfq)	no

Output	Path	Notes
Protein abundance	tables/protein_abundance.csv	one row per protein with the chosen abundance metric
Report	report.md + result.json	summary["method"], summary["n_proteins"]

Flow

1. Load CSV (--input <peptides.csv>) or generate a demo (--demo).
2. Dispatch on --method (proteomics_quantification.py:156); validate required columns per method.
3. Aggregate per protein:
   • lfq: sum intensity per protein.
   • ibaq: sum intensity per protein, divide by n_theoretical_peptides. Source order at proteomics_quantification.py:115-130: sequence (compute on the fly) → n_theoretical_peptides (use as-is) → unique_peptides × 1.5 (silent estimate with warning).
   • spectral_count: count PSMs per protein.
4. Write tables/protein_abundance.csv (proteomics_quantification.py:277) + report.md + result.json (:283).

Gotchas

• lfq and ibaq require an intensity column; method enforces this. proteomics_quantification.py:77 raises ValueError("Input requires an 'intensity' column for LFQ"); :109 raises the same for iBAQ. spectral_count only needs row counts (no intensity).
• ibaq requires either sequence OR n_theoretical_peptides; otherwise it SILENTLY ESTIMATES. proteomics_quantification.py:115-130 checks for sequence first (in-silico digest at :42-72, K/R not before P, length 7-30), then n_theoretical_peptides, otherwise falls back to unique_peptides × 1.5 with only a logger warning. The wrong column name (theoretical_peptides instead of n_theoretical_peptides) silently triggers the estimate path — always pass one of the two correct columns.
• Unknown --method raises ValueError. proteomics_quantification.py:156 rejects values outside ("lfq", "spectral_count", "ibaq"). The argparse choices= already enforces this — the :156 raise is defence-in-depth for direct library calls.
• --input REQUIRED unless --demo. proteomics_quantification.py:269 raises ValueError("--input required").
• Missing intensities in lfq are summed as 0. pd.Series.sum(skipna=True) is the default — proteins with all-NaN intensities yield 0, indistinguishable from "all detected as zero". Pre-filter or impute upstream if NaN-vs-zero matters.

Key CLI

# Demo (LFQ default)
python omicsclaw.py run proteomics-quantification --demo --output /tmp/quant_demo

# LFQ on real peptides
python omicsclaw.py run proteomics-quantification \
  --input peptides.csv --output results/ --method lfq

# iBAQ via per-protein sequence (in-silico digest)
python omicsclaw.py run proteomics-quantification \
  --input peptides_with_sequence.csv --output results/ --method ibaq

# iBAQ via pre-computed n_theoretical_peptides
python omicsclaw.py run proteomics-quantification \
  --input peptides_with_n_theo.csv --output results/ --method ibaq

# Spectral counting
python omicsclaw.py run proteomics-quantification \
  --input psms.csv --output results/ --method spectral_count

See also

• references/parameters.md — every CLI flag, per-method input requirements
• references/methodology.md — LFQ / iBAQ / spectral-count semantics
• references/output_contract.md — tables/protein_abundance.csv schema
• Adjacent skills: proteomics-data-import (upstream — produces normalised peptide / protein tables), proteomics-identification (upstream — peptide-level summary), proteomics-ms-qc (parallel — protein-table QC), proteomics-de (downstream — differential abundance)