Bayesian non-negative factor analysis for reconstructing transcriptional regulatory network

Transcriptional regulation by transcription factors (TFs) controls when and how much RNA is created. Due to technical limitations, the protein level expressions of TFs are usually unknown, making computational reconstruction of transcriptional network a difficult task. We proposed here a novel Bayesian non-negative factor approach, which is capable to estimate both the non-negative abundances of the transcription factors, their regulatory effects, and sample clustering information by integrating microarray data and existing knowledge regarding TFs regulated target genes. The results demonstrated its validity and effectiveness to reconstructing transcriptional networks by transcription factors through simulated systems and real data.