Pat Flaherty (UMass Math) will present “A Nonparametric Bayesian Model For Single-cell Variant Calling” in the Machine Learning and Friends Lunch Thursday Sept.. 14 from 12:00pm to 1:00pm in CS 150. Abstract and bio follows.
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.