GENE EXPRESSION ANALYSIS IN THE PRESENCE OF HETEROGENEITY

Abstract

While methods for differential expression analysis have evolved significantly in the last two decades, they are unable to account for undeclared heterogeneity in the groups under comparison. On the other hand, heterogeneity, of either biological or non-biological origins, is observed to be invariably present in gene expression datasets. Delineating the basis of such heterogeneity in relation to biological pathways is a difficult problem. Our work is addresses this challenge in three ways:

First, we propose a normalization technique based on rank-fuzzification – Gene Fuzzy Scores (GFS), which retains meaningful variation in gene expression and attenuates obscuring noise. Second, we present SPSNet – a method for differential expression analysis of samples with potential heterogeneity. SPSNet reports a list of significant subnetworks (smaller components of biological pathways) whose expression reveals undeclared sub-populations within the given sample phenotypes. Our approach reveals factors relevant to biological or non-biological heterogeneity in the form of gene subnetworks, amplifies their effects in the data, and facilitates discrimination of subpopulations within phenotypes. Finally, we demonstrate the potential of our methods -- GFS and SPSNet -- to analyze RNA-Seq datasets.