Mining Massive Amounts of Genomic Data: A Semiparametric Topic Modeling Approach

Characterizing the functional relevance of transcription factors (TFs) in different biological contexts is pivotal in systems biology. Given the massive amount of genomic data, computational identification of TFs is often necessary to generate new hypotheses for experimentalists. In this paper, we use large gene expression and chromatin immunoprecipitation (ChIP) data corpuses to conduct high-throughput TF-biological context association analysis. This work makes two contributions: (i) From a methodological perspective, we propose a unified topic modeling framework for exploring and analyzing large and complex genomic datasets. Under this framework, we develop new statistical optimization algorithms and semiparametric theoretical analysis which are also applicable to a variety of large-scale data analyses. (ii) From a scientific perspective, our method provides an informative list of new discoveries in biology. Our data-driven analysis of 38 TFs in 68 tumor biological contexts identifies functional signatures of epigenetic regulators, such as SUZ12 and SET-DB1, and nuclear receptors, in many tumor types. In particular, the TF signature of SUZ12 is present in a broad range of tumor types, suggesting the important role of SUZ12-mediated histone methylation in tumor biology.