DPM-LGCP

We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors. The estimated protein clusters form regulatory modules in different chromatin states, which help explain how proteins work together in regulating gene expression. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. Furthermore, we observed that the identified regulatory modules and chromatin states may jointly determine the motif preference for specific class of proteins. Thus, our results reveal a relation between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process.