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JEFF MIECZNIKOWSKI - University of Buffalo

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Empirical based statistical methods for the analysis of RNA-Seq data A method to exploit the structure of genetic ancestry space to enhance case-control studies
04 December 2014 from 4:00 PM to 5:00 PM
201 Thomas Bldg.
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RNA-Seq is a powerful new method for transcriptome analysis that is quickly overtaking microarrays as the tool for gene expression analysis. However, RNA-Seq technology measures gene expression differently compared to microarrays and hence the data analysis methods for microarrays cannot be directly applied. Factors such as gene length are not a concern in microarrays, however, this bias should be adjusted for in RNA-Seq experiments. Inspired by Efron's gene set analysis, we present a novel method for gene pathway enrichment designed to empirically adjust for gene length bias in pathway analyses. Additionally, we have also developed novel methods to empirically estimate the null distribution for differential expression analysis in RNA-Seq experiments. We present our methods with examples and simulations comparing favorably with other RNA-Seq differential expression techniques.

This is based on joint work with Drs. Liu and Wang and my Ph.D. student Xing Ren.