/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.
license: MIT
metadata:
  version: 0.1.0
  openclaw:
    requires:
      bins:
      - python3
    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.

---

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

1. MaxQuant `proteinGroups.txt` 2. 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.
• s