Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments.

Abstract

Microarray technology is widely recognized as one of the most important tools when it comes to understanding genetic expression in biological processes. In light of the thousands of gene expression level measurements (including measurements across a number of conditions), identifying differentially expressed genes necessarily implies data mining or large-scale multiple testing procedures. To date, advances with regard to this field have been multivariate-descriptive or inferential-univariate in nature and therefore have important limitations regarding the biological validity of detected genes. In the present article, we present a new multivariate inferential method designed to detect active differentially expressed genes in gene expression data. The proposed method estimates false discovery rates using artificial components. Our method excels when applied to the most common gene expression data structures, providing new insights into differentially expressed genes. The method described herein was programmed in an R-Bioconductor package called acde that has been available since 2015.