Variational Infinite Heterogeneous Mixture Model for Semi-supervised Clustering of Heart Enhancers

Mehdi, T. F.; Singh, G.; Mitchell, J. A.; Moses, A. M.

doi:10.1101/442392

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bioRxiv · 10.1101/442392

Variational Infinite Heterogeneous Mixture Model for Semi-supervised Clustering of Heart Enhancers

Mehdi, T. F.·Singh, G.·Mitchell, J. A.·Moses, A. M.

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Abstract

MotivationPMammalian genomes can contain thousands of enhancers but only a subset are actively driving gene expression in a given cellular context. Integrated genomic datasets can be harnessed to predict active enhancers. One challenge in integration of large genomic datasets is the increasing heterogeneity: continuous, binary and discrete features may all be relevant. Coupled with the typically small numbers of training examples, semi-supervised approaches for heterogeneous data are needed; however, current enhancer prediction methods are not designed to handle heterogeneous data in the semi-supervised paradigm.\n\nResultsWe implemented a Dirichlet Process Heterogeneous Mixture model that infers Gaussian, Bernoulli and Poisson distributions over features. We derived a novel variational inference algorithm to handle semi-supervised learning tasks where certain observations are forced to cluster together. We applied this model to enhancer candidates in mouse heart tissues based on heterogeneous features. We constrained a small number of known active enhancers to appear in the same cluster, and 47 additional regions clustered with them. Many of these are located near heart-specific genes. The model also predicted 1176 active promoters, suggesting that it can discover new enhancers and promoters.\n\nAvailabilityWe created the dphmix Python package: https://pypi.org/project/dphmix/\n\nContactalan.moses@utoronto.ca

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Mehdi, T. F., Singh, G., Mitchell, J. A., Moses, A. M.. 2018-10-13. Variational Infinite Heterogeneous Mixture Model for Semi-supervised Clustering of Heart Enhancers. https://doi.org/10.1101/442392

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