deseq2-differential-expression

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DESeq2 differential expression analysis skill with normalization, statistical modeling, and visualization

AI & Automation 814 stars 53 forks Updated today MIT

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Skill Content

# DESeq2 Differential Expression Skill ## Purpose Provide DESeq2 differential expression analysis with normalization, statistical modeling, and visualization. ## Capabilities - Size factor normalization - Negative binomial modeling - Shrinkage estimation - Batch effect modeling - Multi-factor designs - Result visualization (MA plots, volcano plots) ## Usage Guidelines - Design experiments with appropriate replication - Include batch effects in model when present - Apply appropriate shrinkage estimators - Use multiple testing correction - Generate publication-quality visualizations - Document analysis parameters and thresholds ## Dependencies - DESeq2 - edgeR - limma-voom ## Process Integration - RNA-seq Differential Expression Analysis (rnaseq-differential-expression) - Single-Cell RNA-seq Analysis (scrnaseq-analysis) - CRISPR Screen Analysis (crispr-screen-analysis)

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

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