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Activity Number: 188 - Bayesian Application to Biological and Health Sciences
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #313645
Title: A Bayesian Latent Feature Model for Phenotypic Heterogeneity in Single-Cell Mass Cytometry Data
Author(s): Thomas Madsen* and Franziska Michor
Companies: Carleton College and Dana-Farber Cancer Institute
Keywords: Bayesian nonparametrics; Biostatistics; Cancer Biology
Abstract:

Phenotypic heterogeneity is a key determinant of cancer progression and the emergence of therapeutic resistance, but meaningful quantification of this heterogeneity remains a challenge. We present a latent feature model which quantifies heterogeneity in single-cell mass cytometry data, permitting comparison of heterogeneity across experimental conditions. We apply a Bayesian matrix factorization approach to learn the underlying biological pathways that generate the data. Simulation studies confirm that this model is able to correctly rank samples in terms of heterogeneity in these underlying features. The model is also able to distinguish a pooled sample of mass cytometry data from thirteen triple-negative breast cancer cell line samples, demonstrating its capacity to identify and rank samples in terms of phenotypic heterogeneity. Our work suggests that latent feature models can provide biologically relevant quantification of heterogeneity, permitting the investigation of therapies which target phenotypic heterogeneity itself.


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