Nonparametric Methods for Gene Set Enrichment Analysis

• Activity Number: 124
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Lena Granovsky*+ and Paul David Feigin and Ruth Heller
• Companies: IBM and Technion and Tel Aviv University
• Keywords: gene set ; K-Means ; cross-match ; permutation methods

Abstract

In recent years, gene set analysis (GSA) methods have drawn much attention. Rather than assessing the significance of individual genes, GSA algorithms assess the significance of pre-defined gene-sets, which are groups of genes that share a common biological function. In this work we propose two new non-parametric techniques to check if the joint distribution of expression levels for genes in a gene set differs between the control and treatment groups. The first method is based on the K-Means approach, and the second one uses the permutation-based t-test approach. We check the performance of the proposed methods on simulated data sets, and compare them to a cross-match and cross-match rank sum algorithms proposed by Rosenbaum. In this study, the cross-match rank sum algorithm is used for the first time in gene enrichment analysis. Power calculations conducted on the simulated data show that the performance of the algorithms depends on the strength of the correlation between the genes in the gene set, and the number of genes differing between the control and treatment samples. Our study demonstrates which algorithms yield the best results for different data characteristics.