Identification of Differentially Expressed Genes with Artificial Components – the `acde' Package

Microarrays and RNA Sequencing have become the most important tools in understanding genetic expression in biological processes. With measurments of thousands of genes’ expression levels across several conditions, identification of differentially expressed genes will necessarily involve data mining or large scale multiple testing procedures. To the date, advances in this regard have either been multivariate but descriptive, or inferential but univariate. In this work, we present a new multivariate inferential method for detecting differentially expressed genes in gene expression data implemented in the acde package for R (R Core Team 2014). It estimates the FDR using artificial components close to the data’s principal components, but with an exact interpretation in terms of differential genetic expression. Our method works best under the most common gene expression data structure and gives way to a new understanding of genetic differential expression. We present the main features of the acde package and illustrate its functionality on a publicly available data set.