It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR

  • Aaron T. L. Lun
  • Yunshun Chen
  • Gordon K. Smyth
Part of the Methods in Molecular Biology book series (MIMB, volume 1418)


RNA sequencing (RNA-seq) is widely used to profile transcriptional activity in biological systems. Here we present an analysis pipeline for differential expression analysis of RNA-seq experiments using the Rsubread and edgeR software packages. The basic pipeline includes read alignment and counting, filtering and normalization, modelling of biological variability and hypothesis testing. For hypothesis testing, we describe particularly the quasi-likelihood features of edgeR. Some more advanced downstream analysis steps are also covered, including complex comparisons, gene ontology enrichment analyses and gene set testing. The code required to run each step is described, along with an outline of the underlying theory. The chapter includes a case study in which the pipeline is used to study the expression profiles of mammary gland cells in virgin, pregnant and lactating mice.

Key words

RNA-seq Differential expression Generalized linear models Quasi-likelihood Variability Read alignment Read counts 



This worked was funded by the University of Melbourne (Elizabeth and Vernon Puzey Scholarship to Aaron T.L. Lun), by the National Health and Medical Research Council (NHMRC) (Fellowship 1058892 and Program 1054618 to Gordon K. Smyth), by the NHMRC Independent Research Institutes Infrastructure Support (IRIIS) Scheme, and by a Victorian State Government Operational Infrastructure Support (OIS) Grant.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Aaron T. L. Lun
    • 1
  • Yunshun Chen
    • 1
  • Gordon K. Smyth
    • 1
  1. 1.Walter and Eliza Hall Institute of Medical ResearchParkvilleAustralia

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