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Activity Number: 182 - SPEED: New Methods in Statistical Genomics and Genetics Part 2
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 11:15 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #307539
Title: Comparing Performance of Gene Set Test Methods Using Biologically Relevant Simulated Data
Author(s): Richard Lambert* and John Stevens
Companies: Utah State University and Utah State University
Keywords: gene set testing; gene expression data; simulated data; high performance computing; meta analysis

Determining differential gene expression in two or more sample groups is of great biomedical interest in understanding the genetic causes of diseases and health conditions and evaluating efficacy of genetic treatments. Gene set testing is a relatively new method of testing for differential expression between sample groups by creating groups of functionally related genes called gene sets. In this research, we compared the statistical power and false discovery rate of the following gene set test methods: mvGST, ROAST, CAMERA, ROMER, GlobalTest, GSA, PAGE, SAFE, sigPathway, and GSEAlm.

We developed a simulation framework to generate datasets that are both biologically relevant and representative of actual gene expression data. We identified several biological parameters of interest and determined realistic values for each of them by either sampling real gene expression data sets or literature review. We then identified 5 interesting parameter pairings and tested each combination of parameter values with either 50 or 100 simulated data sets to determine how power and FDR vary as a function of each parameter as well as identify possible interactions between parameters.

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

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