An information theoretic exploratory method for learning patterns of conditional gene coexpression from microarray data.
In this article, we introduce an exploratory framework for learning patterns of conditional co-expression in gene expression data. The main idea behind the proposed approach consists of estimating how the information content shared by a set of M nodes in a network (where each node is associated to an expression profile) varies upon conditioning on a set of L conditioning variables (in the simplest case represented by a separate set of expression profiles). The method is non-parametric and it is based on the concept of statistical co-information, which, unlike conventional correlation based techniques, is not restricted in scope to linear conditional dependency patterns. Moreover, such conditional co-expression relationships can potentially indicate regulatory interactions that do not manifest themselves when only pair-wise relationships are considered. A moment based approximation of the co-information measure is derived that efficiently gets around the problem of estimating high-dimensional multi-variate probability density functions from the data, a task usually not viable due to the intrinsic sample size limitations that characterize expression level measurements. By applying the proposed exploratory method, we analyzed a whole genome microarray assay of the eukaryote Saccharomices cerevisiae and were able to learn statistically significant patterns of conditional co-expression. A selection of such interactions that carry a meaningful biological interpretation are discussed.
Boscolo
Riccardo
R
Department of Electrical Engineering, University of California, Los Angeles 90095, USA. riccardo@ee.ucla.edu
Liao
James C
JC
Roychowdhury
Vwani P
VP
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D020407:Internet; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*; D012984:Software; D018709:Statistics, Nonparametric
D029701:Saccharomyces cerevisiae Proteins