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Activity Number: 485 - Bayesian Latent Variable Methods for Life Sciences
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #329301 Presentation
Title: A Nonparametric Bayesian Model for Single-Cell Variant Calling
Author(s): Patrick Flaherty*
Companies: University of Massachusetts, Amherst
Keywords: nonparametric bayes; single-cell; dna sequencing

Advances in DNA sequencing technology have enabled surprising discoveries in basic science and novel diagnostics in personalized medicine. Recently, the ability to read the DNA sequence of a single cell has presented new statistical and computational challenges. We address the problem of calling single-nucleotide mutations in single-cell sequencing data. We present some results evaluating existing mutation calling algorithms on data generated from a single-cell sequence data simulator. We describe a nonparametric Bayesian generative model for combining single-cell and bulk DNA sequencing data, and we show preliminary results from this model.

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

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