A two-step hierarchical hypothesis set testing framework, with applications to gene expression data on ordered categories.

ABSTRACT:

Background

In complex large-scale experiments, in addition to simultaneously considering a large number of features, multiple hypotheses are often being tested for each feature. This leads to a problem of multi-dimensional multiple testing. For example, in gene expression studies over ordered categories (such as time-course or dose-response experiments), interest is often in testing differential expression across several categories for each gene. In this paper, we consider a framework for testing multiple sets of hypothesis, which can be applied to a wide range of problems.

Results

We adopt the concept of the overall false discovery rate (OFDR) for controlling false discoveries on the hypothesis set level. Based on an existing procedure for identifying differentially express

SUBMITTER: Li Y 

PROVIDER: S-EPMC4000433 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature