We are actively working on the development of statistical methods and tools for novel single and spatial technologies. We have developed the first model that accounts for the bimodality of expression observed in single-cells. This model is implemented in a package called MAST, and it is one of the most cited methods for differential gene expression analysis of scRNA-seq data. We have also developed one of the first models for the analysis of spatial transcriptomics that enables near single-cell resolution analysis of 10X Visium data. This method is implemented in the BayesSpace package available. Both MAST and BayesSpace are available from the Bioconductor project.

Our group has contributed to many studies using single-cell and spatial computational analyses to characterize the tumor microenvironment (including immune cells) to better understand mechanisms of response and relapse to immunotherapy. Using computational tools (including some that we have developed) we have identified novel biomarkers of responses to anti-PD1 therapy (Greene et al 2021) and escape mechanisms to adoptive T-cell therapies (Paulson et al 2018, Chapuis et al 2019, Lahman et al. 2022).