Detecting phenotype-specific interactions between biological processes from microarray data and annotations

Abstract

High throughput technologies enable researchers to measure expression levels on a genomic scale. However, the correct and efficient biological interpretation of such voluminous data remains a challenging problem. Many tools have been developed for the analysis of GO terms that are over- or under-represented in a list of differentially expressed genes. However, a previously unexplored aspect is the identification of changes in the way various biological processes interact in a given condition with respect to a reference. Here, we present a novel approach that aims at identifying such interactions between biological processes that are significantly different in a given phenotype with respect to normal. The proposed technique uses vector-space representation, SVD-based dimensionality reduction, differential weighting, and bootstrapping to asses the significance of the interactions under the multiple and complex dependencies expected between the biological processes. We illustrate our approach on two real data sets involving breast and lung cancer. More than 88 percent of the interactions found by our approach were deemed to be correct by an extensive manual review of literature. An interesting subset of such interactions is discussed in detail and shown to have the potential to open new avenues for research in lung and breast cancer.

Fig. 1. Singular value decomposition of a gene-function matrix

Singular value decomposition of gene function matrix, GF into G, S, and F^T. G and F are matrices of left and right singular vectors and are orthonormal, i.e., GG^T = I, F^T F = I, where I is the identity matrix; S is a diagonal matrix of singular values s_i, such that s₁ ≥ s₂ … ≥ s_l.

Fig. 2. Approximation of a gene-function matrix

GF_k is an approximation of original gene function matrix, G F, in a subspace with only k singular vectors. However, note that the dimensionality of G F_k remains the same, i.e., there is an element for every gene and every biological process.