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Characterization using Latent Dirichlet Allocation TCF 21 Binding Sites Characterization using Latent Dirichlet Allocation

Transcription factors play multiple roles in cell activity and gene expression, and discovering these roles often requires experimentation in a wet lab. We hope to bypass this process computationally by using topic modeling to infer the myriad of functions of a given transcription factor. Specifically, we apply Latent Dirichlet Allocation (LDA) to all peaks derived from running ChIP-seq on TCF21 in fetal heart cells, and model each peak as a mixture of topics composed of known binding motifs and cell types. We cluster peaks with similar topic distributions together, and we leverage GREAT to demonstrate that a significant fraction of the resulting clusters have functional biological significance. Functional significance of the peak clusterings implies that we have found functional subgroups of sites that bind TCF21, and thus functions in which TCF21 is involved. Furthermore, we also show that the topics deduced by LDA can associate motifs with cell activity in a variety of human cells and thus also yield useful information. These results are summarized in Figure 1