Robust Bayesian analysis of Sparse Longitudinal cancer omics data using Mixed Effect Models

Music City Center 

The central task of analysis of omics data in complex diseases including cancers is to identify susceptible genetic factors that are associated with cancer phenotypes with inferential guarantees. Such an analysis is of a high-dimensional nature and has been further challenged if the disease phenotypes follow skewed distributions due to cancer heterogeneity and are longitudinally measured. To overcome the limitation of existing longitudinal studies that usually lack robustness and valid uncertainty quantification procedures, we have developed a sparse robust Bayesian mixed-effect model to analyze heterogeneous longitudinal omics data. The Gibbs samplers of the MCMC have been developed and efficiently implemented. Extensive numerical studies have indicated the superior performance of the proposed model in estimation and variable selection. In particular, we show that the proposed model can lead to valid inference results on finite samples even in the presence of heterogeneous omics data. Case studies on longitudinal cancer omics data and other types of longitudinal omics data show that the proposed method identifies susceptible genetic factors with important biological implications.

Robust Bayesian variable selection

Mixed effect models

Cancer omics data

Longitudinal studies

Uncertainty quantification 

Section on Statistics in Genomics and Genetics