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Bioinformatics Advance Access published online on September 11, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn465
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Gene Set Enrichment Analysis using Linear Models and Diagnostics

Assaf P. Oron a,*, Zhen Jiang and Robert Gentleman a

aFred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, Washington 98109-1024, U.S.A.

*to whom correspondence should be addressed. Dr. Assaf P. Oron, E-mail: aoron{at}fhcrc.org,rgentlem{at}fhcrc.org


  Abstract

Motivation: Gene-set enrichment analysis (GSEA) can be greatly enhanced by linear model (regression) diagnostic techniques. Diagnostics can be used to identify outlying or influential samples, and also to evaluate model fit and explore model expansion.

Results: We demonstrate this methodology on an adult acute lymphoblastic leukemia (ALL) dataset, using GSEA based on chromosome-band mapping of genes. Individual residuals, grouped or aggregated by chromosomal loci, indicate problematic samples and potential data-entry errors, and help identify hyperdiploidy as a factor playing a key role in expression for this dataset. Subsequent analysis pinpoints suspected DNA copy number abnormalities of specific samples and chromosomes (most prevalent are chromosomes X, 21 and 14), and also reveals significant expression differences between the hyperdiploid and diploid groups on other chromosomes (most prominently 19, 22, 3 and 13) - differences which are apparently not associated with copy number.

Availability: Software for the statistical tools demonstrated in this article is available as Bioconductor package GSEAlm.

Contact: assaf.oron{at}gmail.com,rgentlem{at}fhcrc.org

Associate Editor: Prof. John Quackenbush

Received on June 27, 2008; revised on July 29, 2008; accepted on August 26, 2008

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