proteomics-de
Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Workflow includes preprocessing, imputation, and statistical testing.
npx skills add BioTender-max/awesome-bio-agent-skills --skill proteomics-de --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 🥚 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 tr
- Domain Decisions
- 1. Multi-format Input Support
- 2. Preprocessing Strategy
- 3. Intensity Transformation
- 4. Missing Value Imputation
- 5. Statistical Testing
- 6. s0-based FDR Correction
- 7. Significance Thresholding
- 8. Visualization Outputs
- Safety Rules
- Agent Boundary
- This skill DOES:
- This skill DOES NOT:
- Input Contract
python proteomics_de.py \
What does the proteomics-de skill do?
Differential expression analysis for label-free quantitative (LFQ) intensity data with standard MaxQuant and DIA-NN output. Workflow includes preprocessing, imputation, and statistical testing.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill proteomics-de --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.