edgeR Differential Expression Analysis

RNA-Seq differential gene expression workflow using edgeR

Upload raw integer count matrix. Rows = genes, Columns = samples
Metadata must contain sample conditions

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edgeR: Differential Expression Analysis

Function

edgeR is a Bioconductor package for RNA-seq differential expression analysis. It models count data using the negative binomial distribution and uses empirical Bayes methods to estimate dispersion.

Input Format

  • Counts Matrix (.csv)
  • Sample Metadata (.csv)
  • Condition Column
  • Treated Group
  • Control Group

Output Format

  • Differential Expression Results (.tsv)
  • Significant Genes Table (.tsv)
  • Normalized Counts Matrix (.tsv)
  • PCA Plot
  • Volcano Plot
  • MA Plot
  • Dispersion Plot

Applications

  • Disease vs Control Analysis
  • Small Sample Experiments
  • Drug Response Studies
  • Pathway Enrichment Analysis
  • Biomarker Discovery

Example Usage


library(edgeR)

y <- DGEList(counts)

keep <- filterByExpr(y)

y <- y[keep,,keep.lib.sizes=FALSE]

y <- normLibSizes(y)

design <- model.matrix(~group)

y <- estimateDisp(y,design)

fit <- glmQLFit(y,design)

qlf <- glmQLFTest(fit)

Suggested Reading

Citation

Robinson MD, McCarthy DJ, Smyth GK (2010).
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
Bioinformatics 26(1):139-140.
doi:10.1093/bioinformatics/btp616