proteomics-de

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Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Workflow includes preprocessing, imputation, and statistical testing.

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🥚 Proteomics Differential Expression

This skill performs differential expression analysis on label-free quantitative (LFQ) intensity data from MaxQuant and DIA-NN outputs, including preprocessing, imputation, statistical testing, and visualization.

Domain Decisions

1. Multi-format Input Support

Supports MaxQuant proteinGroups.txt
- Automatic filtering of reverse hits, contaminants, and site-only identifications
Supports DIA-NN output
- Automatically extracts protein IDs and .raw intensity columns

2. Preprocessing Strategy

MaxQuant:
- Filters:
  - Reverse
  - Potential contaminant / Contaminant
  - Only identified by site
DIA-NN:
- Extracts protein identifiers and intensity matrix directly

3. Intensity Transformation

LFQ intensities are transformed using log2 scaling
Ensures approximate normality for downstream statistical testing

4. Missing Value Imputation

Uses down-shifted Gaussian imputation
- Mean shifted by: median - shift × std
- Default:
  - shift = 1.8
  - scale = 0.3
Assumption:
- Missing values represent low-abundance proteins

5. Statistical Testing

Two-sample t-test between treatment and control groups
Default degrees of freedom:
- df = 4 (for 3 vs 3 replicates)

6. s0-based FDR Correction

Uses s0-based thresholding to stabilize variance
Combines:
- log2 fold change
- p-value
Based on:
- Giai Gianetto et al. (2016)

7. Significance Thresholding

Default:
- FDR = 0.05
- s0 = 0.1
Produces:
- Adjusted significance boundary (used in volcano plot)

8. Visualization Outputs

PCA plot
Volcano plot (with s0 curve)
Imputation distribution comparison

Safety Rules

Local-first
- No data upload without explicit user consent
Statistical caution
- Statistical results should be interpreted with caution and not overinterpreted
- Avoid drawing conclusions beyond what the data supports
Missing data assumptions
- Imputation assumes missing values correspond to low abundance
- May not hold in all experimental designs
Small sample limitations
- t-test reliability depends on sufficient replicates
Reproducibility
- All parameters and commands are logged
No hallucinated science
- All methods are based on established proteomics workflows

Agent Boundary

This skill DOES:

Perform differential expression analysis on LFQ proteomics data
Handle MaxQuant and DIA-NN outputs
Generate statistical results and visualizations
Produce reproducible reports

This skill DOES NOT:

Process raw mass spectrometry data (e.g. RAW files)
Perform peptide identification or database search
Conduct pathway or functional enrichment analysis
Provide biological interpretation of results

Input Contract

Supported Input Formats

MaxQuant proteinGroups.txt
DIA-NN output (.tsv / .txt)

Metadata Requirements

.csv or .tsv
Must include:
- sample_id
- group

Supports:

raw names
full paths (e.g. /path/sample.raw)

Output Structure

proteomics_de_report/
├── report.md
├── figures/
│   ├── imputation_distribution.png
│   ├── pca.png
│   └── volcano.png
├── tables/
│   ├── imputed_proteinGroups.csv
│   └── de_results.csv
├── ro-crate-metadata.json
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Usage

Demo

python proteomics_de.py \
  --demo \
  --output report_dir

MaxQuant Input

python proteomics_de.py \
  --input proteinGroups.txt \
  --input-type maxquant \
  --metadata metadata.csv \
  --contrast "treated,control" \
  --output report_dir

DIA-NN Input

python proteomics_de.py \
  --input diann_output.tsv \
  --input-type diann \
  --metadata metadata.csv \
  --contrast "treated,control" \
  --output report_dir

Parameters

Parameter	Description	Default
`--input`	Input file path	-
`--input-type`	`maxquant` or `diann`	maxquant
`--metadata`	Metadata file	-
`--contrast`	treatment,control	treated,control
`--s0`	s0 parameter	0.1
`--fdr`	FDR threshold	0.05
`--ttest-df`	Degrees of freedom	4
`--imputation-shift`	Imputation shift	1.8
`--imputation-scale`	Imputation scale	0.3
`--output`	Output directory	-

References

test_proteinGroups.txt is from: Keilhauer EC, Hein MY, Mann M. Accurate protein complex retrieval by affinity enrichment mass spectrometry (AE-MS) rather than affinity purification mass spectrometry (AP-MS). Mol Cell Proteomics. 2015 Jan;14(1):120-35. doi: 10.1074/mcp.M114.041012. Epub 2014 Nov 2. PMID: 25363814; PMCID: PMC4288248.
s0 correction algorithm is from: Giai Gianetto Q, Couté Y, Bruley C, Burger T. Uses and misuses of the fudge factor in quantitative discovery proteomics. Proteomics. 2016 Jul;16(14):1955-60. doi: 10.1002/pmic.201600132. PMID: 27272648.
s0 correction algorithm is cited by: Michaelis AC, Brunner AD, Zwiebel M, Meier F, Strauss MT, Bludau I, Mann M. The social and structural architecture of the yeast protein interactome. Nature. 2023 Dec;624(7990):192-200. doi: 10.1038/s41586-023-06739-5. Epub 2023 Nov 15. PMID: 37968396; PMCID: PMC10700138.