Biostatistics Seminar Series

High-Dimensional Mixed-Effects Models for Multi-Omics Data - Jiebiao Wang

Tuesday 1/15 3:30PM - 4:30PM
Public Health Auditorium (G23)

High-Dimensional Mixed-Effects Models for Multi-Omics Data

Clustered data are common in biomedical research, including multi-omics studies. Accounting for clustered data structure can increase estimation accuracy and improve power in testing. In this talk, I will present two high-dimensional mixed-effects models motivated by multiple types of clustered omics data: genomics, transcriptomics, and proteomics.

1) The first part of my talk is motivated by clustered proteomics data. I will discuss a high-dimensional multivariate mixed-effects model to test the association between multiple proteins from a functional pathway and triple negative breast cancer. A graphical lasso penalty is imposed to facilitate computation and capture the sparse biological correlation structure among proteins. Through simulations and real data analysis, I will demonstrate that the proposed method improves power as compared to existing univariate models.

2) The second part of the talk is inspired by emerging single-cell RNA-sequencing (scRNA-seq) data. With scRNA-seq data as references, we can deconvolve tissue expression into cell-type-specific expression. I will introduce an empirical Bayes method to deconvolve rich multi-measure tissue expression data, such as multiple regions of the brain tissue per subject. The estimated subject-level cell-type-specific expression enables analyses that are infeasible based on existing data. For instance, it provides novel findings in cell-type-specific expression quantitative trait loci (eQTLs) by integrating the deconvolved expression with genotype, as well as in cell-type-specific co-expression networks.

Jiebiao Wang is with the Department of Statistics and Data Science, Carnegie Mellon University.

Last Updated On Monday, January 14, 2019 by McCullough, Caitlin Emily
Created On Friday, January 4, 2019