Cross-tissue RNA-seq prediction with reduced rank regression

Thomas M. Menino Convention & Exhibition Center 

Human diseases have distinct manifestations across biological tissues. RNA expression within the cells of each tissue provides unique insight into disease progression. Commonly, only a single tissue can be measured due to factors such as cost or ease of sample collection. For example, it is more difficult to collect samples from internal organs, like the lungs and heart, than to obtain blood samples. We propose a method to predict RNA expression in an unmeasured tissue using RNA expression from a measured tissue. We draw inspiration from sparse reduced rank regression by Chen and Huang (2012), which uses a group lasso constraint to remove entire genes from the predictor set. We explore alternative sparsity constraints, such as the standard lasso, which allows predictor gene sets to vary depending on the response gene, and the exclusive lasso, in which each predictor may contribute to at most one latent factor. As we consider solutions to this problem, we navigate the trade-off between complexity and interpretability.

Reduced rank regression (RRR)

Cross-tissue RNA-seq prediction

Transcriptomics

Lasso - standard, group, and exclusvie

Loss functions for counts data

Latent variable models 

Section on Statistics in Genomics and Genetics