proteomics

name	proteomics
description	Mass spectrometry proteomics QC, quantification, comparative analysis, and export for DDA, DIA, and protein-level result tables.
tool_type	python
primary_tool	pyopenms

Proteomics

[!note] Vault audit 2026-07-24 — USE-16 Use this for general mass-spec proteomics analysis and interpretation; for OpenMS-based pipelines use pyopenms, for differential-expression statistics use proteomics-de, and for the FragPipe/pyteomics workflow use fragpipe-pyteomics-proteomics. Which mass-spec tool/stage you need is the distinguishing axis.

Version Compatibility

Reference examples assume:

• pyopenms 3.0+
• pandas 2.2+
• numpy 1.26+
• seaborn 0.13+

Overview

Use this skill when the user needs:

• proteomics QC
• protein table cleanup
• replicate review
• differential abundance analysis
• publication-ready proteomics figures

When To Use This Skill

• MaxQuant, FragPipe, DIA-NN, or similar outputs exist
• the task is protein-level quantification or comparative proteomics
• missingness, batch effects, and replicate quality need review before interpretation

Quick Route

• DDA and DIA should not be treated identically
• protein-level tables should remain distinct from peptide-level tables
• QC comes before differential analysis

Progressive Disclosure

• Read technical_reference.md for assay branching, QC interpretation, and missingness handling.
• Read commands_and_thresholds.md for table-loading patterns, QC thresholds, and output conventions.

Expected Inputs

• protein or peptide result table
• sample metadata
• assay context: DDA, DIA, PTM-enriched, or targeted

Expected Outputs

• results/protein_abundance.tsv
• qc/proteomics_qc_summary.tsv
• figures/correlation_heatmap.pdf
• figures/missingness.pdf
• results/differential_proteins.tsv

Starter Pattern

import pandas as pd

protein_df = pd.read_csv("protein_groups.tsv", sep="\t")
sample_cols = [c for c in protein_df.columns if c.startswith("LFQ intensity")]
matrix = protein_df[sample_cols].replace(0, pd.NA)
qc = pd.DataFrame({
    "n_proteins": matrix.notna().sum(),
    "missing_pct": matrix.isna().mean() * 100,
})
qc.to_csv("qc/proteomics_qc_summary.tsv", sep="\t")

Workflow

1. Clarify assay and table level

• DDA versus DIA
• peptide versus protein table
• PTM-enriched versus unenriched data

2. Run QC before comparisons

Inspect:

• missingness
• replicate correlation
• batch effects
• intensity distributions

3. Normalize and summarize consistently

Keep the normalization approach explicit and do not collapse peptides into proteins without documenting the rule.

4. Perform comparative analysis

Use replicate-aware differential abundance with clear filtering and missingness policy.

5. Export interpretable artifacts

Save both the cleaned abundance matrix and the differential results table.

Output Artifacts

results/
├── protein_abundance.tsv
└── differential_proteins.tsv
qc/
└── proteomics_qc_summary.tsv
figures/
├── correlation_heatmap.pdf
├── missingness.pdf
└── intensity_density.pdf

Quality Review

• overall missingness > 30% should trigger caution
• technical replicate correlation should usually be > 0.9
• biological replicate correlation much below 0.8 deserves review
• do not trust differential calls before batch structure and missingness are understood

Anti-Patterns

• mixing peptide and protein tables in one downstream matrix
• running differential abundance before QC
• ignoring missingness patterns
• hiding whether values are raw, normalized, or imputed

Related Skills

• Metabolomics
• Structural Biology

Optional Supplements

• pyopenms