Our group develops statistical methods and software for the analysis of data from high-throughput genomics projects, with particular interest in methods for identifying the genomic mechanisms underlying complex traits. Our main projects include:

RNA-seq Analysis

RNA-seq is a revolutionary tool for transcriptomics with a number of compelling advantages over microarrays. One in particular is the ability to quantify isoform-specific expression levels. We have developed powerful and efficient methods that allow for both differentially expressed (DE) genes and isoforms to be identified. We are also extending the methods to enable identification of DE patterns in single-cel RNA-seq experiments across conditions and over time, in collaboration with
Jamie Thomson's lab.

Differential Networks

Recognizing that in many cases complex disease may arise from a de- or re-regulation of genes that does not affect average expression levels significantly (or at all), but rather affects the ways in which genes interact within a network, we have developed an interest in methods for identifying differentially regulated networks of genes. One specific type of differential regulation we have considered is differential co-expression (or differential correlation -DC ). We are currently developing an empirical Bayes model based approach to enable the powerful and efficient identification of DC networks; and we are working on extensions to identify genomic regions where allelic variation results in DC between and among transcripts (referred to locally as DC mapping). This has been done in collaboration with
Alan Attie's lab.

Statistical Methods for Personalized Genomic Medicine

Technological advances over the past decade allow for unprecedented measurement of genomic and clinical variables that, ideally, may be used to better treat patients. Unfortunately, this ideal has yet to be achieved. A major obstacle is the lack of statistical models that are both predictive of an individual's outcome as well as clinically actionable. Our group is working to develop methods to this end. Much of our work in this area focuses on the cancer genome atlas ovary study in collaboration with MCW gynecological oncologists William Bradley and Janet Rader.