725 research outputs found
MAD Bayes for Tumor Heterogeneity Feature Allocation with Non-Normal Sampling
We propose small-variance asymptotic approximations for the inference of
tumor heterogeneity (TH) using next-generation sequencing data. Understanding
TH is an important and open research problem in biology. The lack of
appropriate statistical inference is a critical gap in existing methods that
the proposed approach aims to fill. We build on a hierarchical model with an
exponential family likelihood and a feature allocation prior. The proposed
approach generalizes similar small-variance approximations proposed by Kulis
and Jordan (2012) and Broderick et.al (2012) for inference with Dirichlet
process mixture and Indian buffet prior models under normal sampling. We show
that the new algorithm can successfully recover latent structures of different
subclones and is also magnitude faster than available Markov chain Monte Carlo
samplers, the latter often practically infeasible for high-dimensional genomics
data. The proposed approach is scalable, simple to implement and benefits from
the flexibility of Bayesian nonparametric models. More importantly, it provides
a useful tool for the biological community for estimating cell subtypes in
tumor samples
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