Abstract:
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A key problem in biology is how the same copy of a genome within a person can give rise to hundreds of cell types. Plentiful convincing evidence indicates multiple elements, such as transcription factor binding, histone modification, and DNA methylation, all contribute to the regulation of gene expression levels in different cell types. Therefore, it is crucial to understand how these heterogeneous regulatory elements collaborate together, how the cooperation at a given genomic region changes across diverse cell lines, as well as how such dynamic cooperation patterns across cell lines vary along the whole genome. Here, we propose a scalable hierarchical probabilistic generative model to cluster genomic regions according to the dynamic changes of their open chromatin and DNA methylation status across cell types. The model will overcome the exponential growth of parameter space as the number of cell types integrated increases. The fitted results will provide a genome-wide region-specific, cell-line-specific open chromatin and DNA methylation landscape, and the model is applicable to a broader class of problems for jointly modeling heterogeneous data types across multiple conditions.
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