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


Nikolayeva, Olga; Robinson, Mark D (2014). edgeR for differential RNA-seq and ChIP-seq analysis: an application to stem cell biology. Methods in Molecular Biology, 1150:45-79.

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.

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.

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26 citations in Web of Science®
27 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Molecular Life Sciences
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Date:2014
Deposited On:02 Jul 2014 15:59
Last Modified:05 Apr 2016 17:56
Publisher:Springer
ISSN:1064-3745
Publisher DOI:https://doi.org/10.1007/978-1-4939-0512-6_3
PubMed ID:24743990

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