Title: "Gene Set Testing to Characterize Multivariately Differentially Expressed Genes"
Speaker: John Stevens, Department of Mathematics and Statistics, Utah State University, Logan UT
Place: HORT 117; November 29, 2011, Tuesday, 4:30pm


In a gene expression experiment (using oligo array, RNA-Seq, or other platform), researchers typically seek to characterize differentially expressed genes based on common gene function or pathway involvement. The field of gene set testing provides numerous characterization methods, some of which have proven to be more valid and powerful than others. These existing gene set testing methods focus on experimental designs where there is a single null hypothesis (usually involving association with a continuous or categorical phenotype) for each gene. Increasingly common experimental designs lead to multiple null hypotheses for each gene, and the characterization of these multivariately differentially expressed genes is of great interest. We explore extensions of existing gene set testing methods to achieve this characterization, with application to a RNA-Seq study in livestock cloning.

1. Nettleton et al. (2008) "Identification of differentially expressed gene categories in microarray studies using nonparametric multivariate analysis." Bioinformatics 24(2):192-201.
2. Fridley et al. (2010) "Self-contained gene-set analysis of expression data: An evaluation of existing and novel methods." PLoS ONE 5(9):e12693.

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