Cited by 406Gene expression and cancer classificationGenomics and Phylogenetic StudiesOptimal Experimental Design Methods

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Statistical Design and Analysis of RNA Sequencing Data

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Auer, Paul L, Doerge, R W·2010·Genetics

DOI 10.1534/genetics.110.114983PMC PMC2881125

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Abstract

Next-generation sequencing technologies are quickly becoming the preferred approach for characterizing and quantifying entire genomes. Even though data produced from these technologies are proving to be the most informative of any thus far, very little attention has been paid to fundamental design aspects of data collection and analysis, namely sampling, randomization, replication, and blocking. We discuss these concepts in an RNA sequencing framework. Using simulations we demonstrate the benefits of collecting replicated RNA sequencing data according to well known statistical designs that partition the sources of biological and technical variation. Examples of these designs and their corresponding models are presented with the goal of testing differential expression.

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Authors: Auer, P. L., & Doerge, R. W.
Affiliations: Purdue University West Lafayette (US)
Corresponding author: Doerge, R W
ORCID: Auer, Paul L
Venue: Genetics
Publisher: Oxford University Press (OUP)
Published: 2010-06-01
Volume: 185
Issue: 2
Pages: 405-416
ISSN: 0016-6731 (ISSN-L)
1943-2631
DOI: 10.1534/genetics.110.114983
PMC: PMC2881125
OpenAlex: W1995070233
Cited by: 393
References: 53
Citations by year: 2026: 5 · 2025: 7 · 2024: 12 · 2023: 11 · 2022: 14 · 2021: 17 · 2020: 24 · 2019: 18 · 2018: 43 · 2017: 25 · 2016: 32 · 2015: 49 · 2014: 42 · 2013: 39 · 2012: 28
FWCI: 12.26
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations: Genetics (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Keywords: RNA sequencing, statistical design, sampling, randomization, replication, blocking, differential gene expression, biological variation, technical variability, experimental design, gene expression quantification
MeSH terms: Base Sequence, Research Design, Clinical Laboratory Techniques

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Statistical Design and Analysis of RNA Sequencing Data

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