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