edgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology

Methods Mol Biol. 2014;1150:45-79. doi: 10.1007/978-1-4939-0512-6_3.

Abstract

The edgeR package, an R-based tool within the Bioconductor project, offers a flexible statistical framework for detection of changes in abundance based on counts. In this chapter, we illustrate the use of edgeR on a human embryonic stem cell dataset, in particular for RNA-seq and ChIP-seq data. We focus on a step-by-step statistical analysis of differential expression, going from raw data to a list of putative differentially expressed genes and give examples of integrative analysis using the ChIP-seq data. We emphasize data quality spot checks and the use of positive controls throughout the process and give practical recommendations for reproducible research.

MeSH terms

  • Biostatistics / methods*
  • Chromatin Immunoprecipitation / methods*
  • Computational Biology / methods*
  • Embryonic Stem Cells / metabolism*
  • Humans
  • Sequence Analysis, RNA / methods*