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Activity Number: 126 - SPEED: New Methods in Statistical Genomics and Genetics Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #306529 Presentation
Title: Bayesian Inference for Reconstructing Intra-Tumor Phylogeny
Author(s): Tingting of Zhai* and Jinpeng of Liu and Chi of Wang
Companies: University of Kentucky and University of Kentucky and University of Kentucky
Keywords: Clonal evolution; RNA seq; gene expression; tree structure; bayesian inference

Intra-tumor heterogeneity arises because tumors evolve over time and different descendants of the original cell acquire new mutations, which they then pass on to their progeny. However, to date there are limited methods for inferring clonal evolution of tumor from RNA-seq data. In this poster, we present a new statistical method to reconstruct the evolutionary history and population frequency of the subclonal lineages of tumor cells from RNA-seq measurements. Our method uses a Bayesian nonparametric prior and nested stick-breaking process to allow for evolutional trees of infinite nodes, and to identify cell population frequencies which have the highest likelihood of generating the observed RNA-seq data. Markov Chain Monte Carlo method based on slice sampling is incorporated to perform Bayesian inference. Simulations demonstrate that the proposed method reliably recover the phylogenetic chain and population frequency of the subclonal lineages of tumor cells.

Authors who are presenting talks have a * after their name.

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