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

摘要: Many biological processes rely on remodeling of the transcriptional response cells through activation transcription factors. Although determination activity level factors from microarray data can provide insight into developmental and disease processes, it requires careful analysis because multiple regulation genes. We present a novel approach that handles both assignment genes to patterns, as required by regulation, linking in prior probability distributions according their known regulators. demonstrate power this simulations application yeast cell cycle deletion mutant data. The results presence increasing noise showed improved recovery patterns terms 2 fit. Analysis led inference biologically meaningful groups comparison other techniques, demonstrated with ROC analysis. new algorithm provides an for estimating levels factor data, therefore insights response.