--- 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 ```bash python proteomics_de.py \ --demo \ --output report_dir ``` ### MaxQuant Input ```bash python proteomics_de.py \ --input proteinGroups.txt \ --input-type maxquant \ --metadata metadata.csv \ --contrast "treated,control" \ --output report_dir ``` ### DIA-NN Input ```bash 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. --- Source attribution: ClawBio Original project: https://github.com/ClawBio/ClawBio License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: ClawBio Original project: https://github.com/ClawBio/ClawBio Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Data & Analytics, Education & Research, Product Management Roles: Business Intelligence Analyst, Data Scientist, Market Research Analyst, Product Manager, Researcher Specialties: API, Testing Tools: Matplotlib, Numpy, Pandas, Python3, Scikit Learn, Scipy, Seaborn Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 5e0c8e3617a24dc0b03a0293945ccd8dc63f33b15fd5f5a953b4df844e0338a6 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. 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