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Department of Statistics, UCLA

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Bayesian Sparse Hidden Components Analysis for Transcription Regulation Networks


We describe a framework where DNA sequence information and expression arrays data are used in concert to analyze the effects of a collection of regulatory proteins on genomic expres- sion levels. The search for potential binding sites in sequence data leads to the identification of potential target genes for each transcription factor. The analysis of array data with a Bayesian hidden component model allows us to identify which of the potential binding sites are actually used by the regulatory proteins in the studied cell conditions, the strength of their control, and their activation profile in a series of experiments. We apply our methodology to 35 expression studies in E. Coli.

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