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edger-basics

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Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.

When to use

• running bulk DE with edgeR
• choosing edgeR over DESeq2
• needing TREAT for a fold-change-threshold hypothesis
• troubleshooting v3-to-v4 reproducibility
• building paired or interaction designs
• handling library-quality heterogeneity
• transcript DE via catchSalmon
• large samples

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