Gene profiling for determining pluripotent genes in a time course microarray experiment

TL;DR

This paper introduces a novel gene profiling method for microarray data that combines differential and equivalence testing to accurately identify genes matching specific temporal expression profiles.

Contribution

The paper presents a new statistical methodology called gene profiling, integrating equivalence testing with differential expression analysis for more precise gene identification.

Findings

Effective ranking of genes according to specified profiles

Application to stem cell data demonstrates method's utility

Theoretical foundation based on equivalence and intersection-union tests

Abstract

In microarray experiments, it is often of interest to identify genes which have a pre-specified gene expression profile with respect to time. Methods available in the literature are, however, typically not stringent enough in identifying such genes, particularly when the profile requires equivalence of gene expression levels at certain time points. In this paper, the authors introduce a new methodology, called gene profiling, that uses simultaneous differential and equivalent gene expression level testing to rank genes according to a pre-specified gene expression profile. Gene profiling treats the vector of true gene expression levels as a linear combination of appropriate vectors, i.e., vectors that give the required criteria for the profile. This gene-profile model is fitted to the data and the resultant parameter estimates are summarized in a single test statistic that is then used to rank the genes. The theoretical underpinnings of gene profiling (equivalence testing, intersection-union tests) are discussed in this paper, and the gene profiling methodology is applied to our motivating stem cell experiment.