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Activity Number: 328 - Statistical Methods for Multi-Omics Data Integration
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #312214
Title: IMIX: A Multivariate Mixture Model Framework for Integrative Analysis of Multiple Types of Omics Data
Author(s): Ziqiao Wang* and Peng Wei
Companies: The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center
Keywords: integrative genomics; FDR control; multivariate mixture model; model selection; TCGA
Abstract:

Integrative genomic analysis is a powerful tool that evaluates whether a disease is associated with genes in multiple genomic data types, such as DNA methylation, copy number variation and gene expression, to study the underlying biological mechanisms. It is common to conduct the analysis for each data type separately and combine the results ad hoc, leading to loss of statistical power and uncontrolled overall false discovery rate (FDR). We propose a multivariate mixture model framework (IMIX) that integrates multiple types of genomic data to examine and relax the commonly adopted conditional independence assumption. We investigate multi-class FDR control in IMIX, and show the gain in lower misclassification rates at controlled overall FDR compared with established individual data type analysis strategies, such as Benjamin-Hochberg FDR control, the q-value, and family-wise error rate control by extensive simulations. The proposed IMIX features statistically-principled model selection, FDR control and computational efficiency. Applications to the TCGA data provide novel multi-omic insights into the luminal/basal subtyping of bladder cancer and the prognosis of pancreatic cancer.


Authors who are presenting talks have a * after their name.

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