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Activity Number: 355 - Contributed Poster Presentations: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #324233
Title: A Bayesian Approach to Analyzing Gene Expression in Heterogeneous Tissue Samples
Author(s): Megan Stefanski* and David Spade
Companies: University of Missouri - Kansas City and University of Missouri - Kansas City
Keywords: RNA-seq ; cancer biomarker ; differential gene expression ; microarray ; heterogeneous tissue
Abstract:

Identification of cancer biomarkers can aid in determining personalized treatment plans and improve patient outcomes. RNA transcript quantification of cancer tissue can identify biomarkers by determining what genes are being over-or under-expressed, leading to aberrant cell behavior. Because tumor samples are inherently heterogeneous and manual separation is time consuming and costly, a new Bayesian method for mixed tissue expression analysis has been developed. This method was developed to utilize microarray expression data and prior information on constituent tissue proportions to determine individual tissue-specific expression profiles as well as identifying differential gene expression. This method has now also been adapted to analyze RNA-seq data, a newer format of expression data that has numerous advantages over microarray data. This work presents the results of differential gene expression analysis in heterogeneous tissue samples using RNA-seq expression data, and evaluates effects of the new data type with the model, specifically in regard to convergence and computational efficiency.


Authors who are presenting talks have a * after their name.

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