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DEBASHIS GHOSH - Penn State University

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” Two methods for modelling gene expression and copy number expression data”
12 September 2013 from 4:00 PM to 5:00 PM
201 Thomas Bldg.
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Copy number aberration is a common form of genomic instability in cancer. Gene expression is closely tied to cytogenetic events by the central dogma of molecular biology, and serves as a mediator of copy number changes in disease phenotypes. Accordingly, it is of interest to develop proper statistical methods for jointly analyzing copy number and gene expression data. This talk describes two approaches.  The first is a novel Bayesian inferential approach for a double-layered mixture model (DLMM) which directly models the stochastic nature of copy number data and identifies abnormally expressed genes due to aberrant copy number.  The second is a multivariate mutliple testing approach in which p-vectors are generated, and the correlation structure between the two platforms are treated as unknown. The approach generalizes the Benjamini-Hochberg procedure to accommodate multivariate data and has a computational complexity that is scalable across multiple platforms.   Simulation studies are used to illustrate the procedures, as well as application to some cancer datasets.