Perform differential protein abundance testing with MSstats, limma, proDA, and scipy.
npx skills add https://github.com/TianGzlab/OmicsClaw --skill proteomics-de
Please help me install this Agent Skill. Skill: proteomics-de Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/proteomics/proteomics-de Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill proteomics-de
This Skill automates the statistical testing required to identify proteins that show significant differences in abundance between experimental conditions, a crucial step in many proteomics studies.
Run differential abundance testing on the provided protein matrix using the limma method.
High-intent search queries and answers about installing and using this skill.
You can perform differential abundance testing using statistical methods like limma, MSstats, proDA, and standard t-tests on a protein matrix, then apply Benjamini-Hochberg FDR correction to identify significantly up or down-regulated proteins.
Generating a volcano plot for proteomics data requires running differential abundance testing first, then plotting the statistical significance against the fold change of proteins to visualize up and down-regulated proteins across experimental groups.
Yes, the limma method is supported alongside MSstats and proDA within a Python environment utilizing numpy, pandas, scipy, and statsmodels to perform differential abundance analysis on proteomics data matrices.
Applying Benjamini-Hochberg FDR correction is a standard method to control false discovery rates in proteomics data, ensuring that proteins identified as significantly differentially abundant between groups are statistically reliable.
Yes, scipy and statsmodels provide the foundational statistical functions and standard t-tests needed to analyze mass spectrometry data matrices and pinpoint proteins with significant abundance changes between treated and control samples.
MDheweiAnalyze mass spectrometry proteomics data to identify differentially expressed proteins and generate publication-ready plots.
jaechang-hitsPerform GSEA and ORA on gene lists with Enrichr pathway lookups.
swarm-ai-researchAnalyze SWARM experimental data with hypothesis tests and multiple-comparison corrections.
KetomihineGuide single-cell analysis workflows from preprocessing to differential abundance testing.
