Some Statistical Problems in Cancer Evolution

Event Details

Type

Center for Studies in Physics and Biology Seminars

Speaker(s)

Simon Tavaré, Ph.D., professor, Columbia University

Speaker bio(s)

This talk addresses some statistical and computational problems arising in the study of cancer evolution. The starting point comes from population genetics: how should we estimate evolutionarily relevant parameters from DNA sequence data taken from samples of individuals? Tavaré will give a brief overview of what has been learned, touching on approximate Bayesian computation (ABC) as an inference method when likelihoods are intractable. To illustrate ABC, he will give an example concerning inference about the number of distinct DNA sequences in a sample, given only information about the relative frequency of point mutations in the samples. This provides an introduction to inference from typical cancer sequencing data, in which individuals are replaced by cells and in which typically it is not known which mutations occur in which cells. Tavaré will discuss a stochastic model that exploits coalescent theory to study clonal sweeps, and describe new techniques for deconvolving clones from single cell sequencing data. Time permitting, he will describe some novel experimental methods his group is developing to understand the 3D structure of tumors, paving the way for some challenging inferential problems that will require engagement from data scientists and others.

Open to

Public

Contact

Melanie Lee

Phone

(212) 327-8636

Sponsor

Melanie Lee
(212) 327-8636
leem@rockefeller.edu