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variancePartition: interpreting drivers of variation in complex gene expression studies

Hoffman, Gabriel E., Schadt, Eric E.·2016·BMC Bioinformatics
DOI 10.1186/s12859-016-1323-zPMC PMC5123296

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Abstract

Background: As large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. Results: We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets. Conclusions: Our open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition .

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Cited by

Sorted by citations

Authors
Hoffman, G. E., & Schadt, E. E.
ORCID
Hoffman, Gabriel E.
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2016-11-25
Volume
17
Issue
1
ISSN
1471-2105
DOI
10.1186/s12859-016-1323-z
PMC
PMC5123296
Cited by
831
References
17
Language
en
Licence
Funders
  • National Heart, Lung, and Blood Institute (U01 HL107388-04)
  • National Institute on Aging (U01 AG046170-02)

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