We propose a probabilistic model for cellular processes, and an algorithm for discovering them from gene expression data. A process is associated with a set of genes that participate in it; unlike clustering techniques, our model allows genes to participate in multiple processes. Each process may be active to a different degree in each experiment. The expression measurement for gene in array ¡ is a sum, over all processes in which participates, of the activity levels of these processes in array ¡ . We describe an iterative procedure, based on the EM algorithm, for decomposing the expression matrix into a given number of processes. We present results on Yeast gene expression data, which indicate that our approach identifies real biological processes.
REF proposed a method to decompose gene expression to infer activity levels of several abstract processes and simultaneously predict the gene-process association.
Decomposing Gene Expression into Cellular Processes
Pacific Symposium on Biocomputing
Computer Science Biology Medicine