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Pat Flaherty - A Nonparametric Bayesian Model for Single-cell Variant Calling

Machine Learning and Friends Lunch
September 14
Computer Science Building, Room 150/151

 

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Abstract:
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.

 

Bio:
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.