Gene set analysis for self-contained tests: complex null and specific alternative hypotheses.

ABSTRACT:

Motivation

The analysis of differentially expressed gene sets became a routine in the analyses of gene expression data. There is a multitude of tests available, ranging from aggregation tests that summarize gene-level statistics for a gene set to true multivariate tests, accounting for intergene correlations. Most of them detect complex departures from the null hypothesis but when the null hypothesis is rejected, the specific alternative leading to the rejection is not easily identifiable.

Results

In this article we compare the power and Type I error rates of minimum-spanning tree (MST)-based non-parametric multivariate tests with several multivariate and aggregation tests, which are frequently used for pathway analyses. In our simulation study, we demonstrate that MST-based

SUBMITTER: Rahmatallah Y 

PROVIDER: S-EPMC3509490 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature