BIOSTATISTICS SEMINAR
DeMix-Bayes: A Bayesian Model for the Deconvolution
of Mixed Cancer Transcriptomes
Wenyi Wang, PhD
Department of Bioinformatics and Computational Biology,
Division of Quantitative Sciences
The University of Texas MD Anderson Cancer Center
Abstract
Clinically derived tumor tissues are often times made of both cancer and surrounding
stromal cells. The expression measures of these samples are therefore partially derived
from the non-tumor cells. This may explain why some previous studies have identified only
a fraction of differentially expressed genes between tumor and surrounding tissue
samples. What makes the in silico estimation of mixture components difficult is that the
percentage of stromal cells varies from one tissue sample to another. Until recently, there
has been limited work on statistical methods development that accounts for tumor
heterogeneity in gene expression data. To this end, we propose a Bayesian deconvolution
models (DeMix-Bayes) for both RNA-seq read counts and microarray expressions. Similar
to our previous method DeMix, a heuristic search algorithm, DeMix-Bayes address two
challenges: 1) estimation of both tumor proportion and tumor-specific expression, when
neither is known a priori, 2) estimation of individualized expression profiles for both tumor
and stromal tissues. We demonstrate the performance of our model in both synthetic
datasets and RNA-seq validation datasets.
The Johns Hopkins Bloomberg School of Public Health, Department of
Biostatistics, Monday, Monday, April 6, 12:15-1:15, Room W3008,
School of Public Health (Refreshments: 12:00-12:15)
Note: Taking photos during the seminar is prohibited
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