bio-de-edger-basics
Perform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR.
npx skills add majiayu000/claude-skill-registry --skill edger-basics-gptomics-bioskills --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.
# edgeR Basics Differential expression analysis using edgeR's quasi-likelihood framework for RNA-seq count data. ## Required Libraries ```r library(edgeR) library(limma) # For design matrices and voom ``` ## Installation ```r if (!require('BiocManager', quietly = TRUE)) install.packages('BiocManager') BiocManager::install('edgeR') ``` ## Creating DGEList Object ```r # From count matrix # counts: matrix with genes as rows, samples as columns # group: factor indicating sample groups y <- DGEList(counts = counts, group = group) # With gene annotation y <- DGEList(counts = counts, group = group, genes = gene_info) # Check structure y ``` ## Standard edgeR Workflow (Quasi-Likelihood) ```r # Create DGEList y <- DGEList(counts = counts, group = group) # Filter low-expression genes keep <- filterByExpr(y, group = group) y <- y[keep, , keep.lib.sizes = FALSE] # Normalize (TMM by default) y <- calcNormFactors(y) # Create design matrix design <- model.matrix(~ group) # Estimate dispersion (optional in edgeR v4+ but improves BCV plots) y <- estimateDisp(y, design) # Fit quasi-likelihood model fit <- glmQLFit(y, design) # Perform quasi-likelihood F-test qlf <- glmQLFTest(fit, coef = 2) # View t
- Required Libraries
- Installation
- Creating DGEList Object
- Standard edgeR Workflow (Quasi-Likelihood)
- Filtering Low-Expression Genes
- Normalization Methods
- Design Matrices
- Dispersion Estimation
- Quasi-Likelihood Testing
- Making Contrasts
- Accessing Results
- Result Columns
- Alternative: Exact Test (Classic edgeR)
- Alternative: glmLRT (Likelihood Ratio Test)
What does the bio-de-edger-basics skill do?
Perform differential expression analysis using edgeR in R/Bioconductor. Use for analyzing RNA-seq count data with the quasi-likelihood F-test framework, creating DGEList objects, normalization, dispersion estimation, and statistical testing. Use when performing DE analysis with edgeR.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill edger-basics-gptomics-bioskills --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 majiayu000/claude-skill-registry, a repository with 534 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.