Decomposing gene expression into cellular processes.
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 g in array a is a sum, over all processes in which g participates, of the activity levels of these processes in array a. 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.
Segal
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Computer Science Department, Stanford, CA 94305-9010, USA. eran@cs.stanford.edu
Battle
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Koller
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D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D005800:Genes, Fungal; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D012441:Saccharomyces cerevisiae / Q000235:genetics