Gene feature interference deconvolution

Abstract

High-throughput microarray technologies measure the abundance of thousands of mRNA targets simultaneously. Due to the usual disparity between a few available samples (from limited conditions or time course points) and many gene expression values (entire genomes), a complex high-dimensional genomic system has to be analyzed, for instance by reverse engineering methods. The latter aim to reconstruct gene networks from experimentally observed expression changes caused by various kinds of perturbations. In particular, elucidating regulatory paths and assessing their reliability across replicates are central topics in this article. The reconstruction problem requires efficiency and accuracy from numerical optimization algorithms and statistical inference techniques. To this end, we focus on methods but also on the available experimental information produced in technical replicates. We propose a model-based approach based on a few steps. First, feature selection is performed by a projective method aimed to combine the gene measurements observed across replicates. Second, a quite heuristic sieving strategy is pursued to bypass the usual recourse to averaging. Third, the impact of dimensionality reduction on the biological system under study is evaluated. Evidence is obtained from the application of our approach to microarray time course experimental replicated data, and suggests that gene features, once identified, can be used for stabilization purposes relatively to the replicate variability. Both quantitative representation and qualitative assessment of the observed gene feature interference are reported in order to decipher specific gene regulatory map and the pathway-associated dynamics.