title: edgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology.
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.
authors.affiliation: Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
authors.forename: Olga
authors.initials: O
authors.lastname: Nikolayeva
authors.forename: Mark D
authors.initials: MD
authors.lastname: Robinson
mesh_terms: D056808:Biostatistics / Q000379:methods*; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D053595:Embryonic Stem Cells / Q000378:metabolism*; D006801:Humans; D017423:Sequence Analysis, RNA / Q000379:methods*