A Nonparametric Bayesian Model for Single-cell Variant Calling

14 Sep
Thursday, 09/14/2017 11:45am to 1:30pm
Computer Science Building, Room 150/151
Machine Learning and Friends Lunch
Speaker: Pat Flaherty

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.

Patrick Flaherty is a Professor in the Department of Mathematics & Statistics at UMass Amherst. He received his PhD in Electrical Engineering and Computer Science from the University of California, Berkeley and he was a postdoctoral scholar at Stanford University in the Department of Biochemistry.  His research focuses on scalable, statistical methods for analyzing large genomic data sets.