Determining transcription factor activity from microarray data using Bayesian Markov chain Monte Carlo sampling.

ABSTRACT

Many biological processes rely on remodeling of the transcriptional response of cells through activation of transcription factors. Although determination of the activity level of transcription factors from microarray data can provide insight into developmental and disease processes, it requires careful analysis because of the multiple regulation of genes. We present a novel approach that handles both the assignment of genes to multiple patterns, as required by multiple regulation, and the linking of genes in prior probability distributions according to their known transcriptional regulators. We demonstrate the power of this approach in simulations and by application to yeast cell cycle and deletion mutant data. The results of simulations in the presence of increasing noise showed improved recovery of patterns in terms of chi2 fit. Analysis of the yeast data led to improved inference of biologically meaningful groups in comparison to other techniques, as demonstrated with ROC analysis. The new algorithm provides an approach for estimating the levels of transcription factor activity from microarray data, and therefore provides insights into biological response.