Quantitative set analysis for gene expression: A method to quantify gene set differential expression including gene-gene correlations: A method to quantify gene set differential expression including gene-gene correlations

Gur Yaari, Christopher R. Bolen, Juilee Thakar, Steven H. Kleinstein

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137 Scopus citations

Abstract

Enrichment analysis of gene sets is a popular approach that provides a functional interpretation of genome-wide expression data. Existing tests are affected by inter-gene correlations, resulting in a high Type I error. The most widely used test, Gene Set Enrichment Analysis, relies on computationally intensive permutations of sample labels to generate a null distribution that preserves gene-gene correlations. A more recent approach, CAMERA, attempts to correct for these correlations by estimating a variance inflation factor directly from the data. Although these methods generate P-values for detecting gene set activity, they are unable to produce confidence intervals or allow for post hoc comparisons. We have developed a new computational framework for Quantitative Set Analysis of Gene Expression (QuSAGE). QuSAGE accounts for inter-gene correlations, improves the estimation of the variance inflation factor and, rather than evaluating the deviation from a null hypothesis with a P-value, it quantifies gene-set activity with a complete probability density function. From this probability density function, P-values and confidence intervals can be extracted and post hoc analysis can be carried out while maintaining statistical traceability. Compared with Gene Set Enrichment Analysis and CAMERA, QuSAGE exhibits better sensitivity and specificity on real data profiling the response to interferon therapy (in chronic Hepatitis C virus patients) and Influenza A virus infection. QuSAGE is available as an R package, which includes the core functions for the method as well as functions to plot and visualize the results. © 2013 The Author(s). Published by Oxford University Press.
Original languageEnglish
Pages (from-to)e170
JournalNucleic Acids Research
Volume41
Issue number18
DOIs
StatePublished - 1 Oct 2013

Bibliographical note

Funding Information:
The authors thank Ronen Globinsky and Efrat Oron for helpful discussion and Hailong Meng for testing the R package. They also thank the Yale High Performance Computing Center (funded by NIH grant: RR19895) for use of their computing resources.

Funding Information:
Funding for open access charge: NIAID contract [HHSN272201000054C]; NIH (in part) [T15 LM07056 to C.R.B.].

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