Journal
SCIENTIFIC REPORTS
Volume 4, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/srep06347
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Funding
- Key Discipline Fund of National 211 Project [SWU:TR201208-3]
- Open Fund of State Key Laboratory of Silkworm Genome Biology [sklsgb2013005]
- NIH [1RC1CA146849]
- University Cancer Research Fund at UNC-CH [CA123175-01A1]
- NIH Systems Biology Center Grant
- NSF grant [DMS-0732260, CCF-1049290, R01 CA125618-01]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1209155] Funding Source: National Science Foundation
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RNA-Seq is quickly becoming the preferred method for comprehensively characterizing whole transcriptome activity, and the analysis of count data from RNA-Seq requires new computational tools. We developed GSAASeqSP, a novel toolset for genome-wide gene set association analysis of sequence count data. This toolset offers a variety of statistical procedures via combinations of multiple gene-level and gene set-level statistics, each having their own strengths under different sample and experimental conditions. These methods can be employed independently, or results generated from multiple or all methods can be integrated to determine more robust profiles of significantly altered biological pathways. Using simulations, we demonstrate the ability of these methods to identify association signals and to measure the strength of the association. We show that GSAASeqSP analyses of RNA-Seq data from diverse tissue samples provide meaningful insights into the biological mechanisms that differentiate these samples. GSAASeqSP is a powerful platform for investigating molecular underpinnings of complex traits and diseases arising from differential activity within the biological pathways.
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