proteomics-de

name	proteomics-de
description	Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Workflow includes preprocessing, imputation, and statistical testing.
version	0.1.0
metadata	{"openclaw":{"requires":{"bins":["python3"],"env":[],"config":[]},"always":false,"emoji":"🥚","homepage":"https://github.com/ClawBio/ClawBio","os":["darwin","linux","win32"],"install":[{"kind":"pip","package":"pandas"},{"kind":"pip","package":"numpy"},{"kind":"pip","package":"matplotlib"},{"kind":"pip","package":"scikit-learn"},{"kind":"pip","package":"scipy"},{"kind":"pip","package":"seaborn"}],"trigger_keywords":["Differential expression analysis of proteomics data from MaxQuant or DIA-NN output"]}}

🥚 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.

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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

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2. Preprocessing Strategy

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

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3. Intensity Transformation

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

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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

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5. Statistical Testing

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

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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)

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7. Significance Thresholding

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

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8. Visualization Outputs

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

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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

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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

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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

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Input Contract

Supported Input Formats

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

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Metadata Requirements

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

Supports:

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

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Output Structure

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

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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.