Computing and Mathematical Sciences Colloquium

Monday January 23, 2017 4:00 PM

Structured Factor Models to Find Interpretable Signal in Genomic Data

Speaker: Professor Barbara Engelhardt, Computer Science Department Center for Statistics and Machine Learning , Princeton University
Location: Annenberg 105

Latent factor models have been the recent focus of much attention in `big data' applications because of their ability to quickly allow the user to explore the underlying data in a controlled and interpretable way. In genomics, latent factor models are commonly used to identify population substructure, identify gene clusters, and control noise in large data sets. In this talk I present a general framework for Bayesian structured latent factor models. I will illustrate the power of these models for a broad class of problems in genomics via application to the Genotype-tissue Expression (GTEx) data set. In particular, by using a Bayesian biclustering version of this model, the estimated latent structure may be used to identify gene co-expression networks that co-vary uniquely in one tissue type (and other conditions). We validate network edges using tissue-specific expression quantitative trait loci.

Series Computing and Mathematical Sciences Colloquium Series

Contact: Carmen Nemer-Sirois at (626) 395-4561 carmens@cms.caltech.edu