We are a bioinformatics research lab in the Department of Biomedical Engineering at Johns Hopkins University. We are also a part of the Center for Computational Biology and Department of Computer Science.

We develop methods for analyzing single cell multi-omic sequencing and imaging data.

While heterogeneity within cellular systems has long been widely recognized, only recently have technological advances enabled measurements to be made on a single cell level. We develop machine learning and other statistical approaches as open-source computational software to analyze such high-throughput single-cell resolution multi-omic and imaging data in order to identify and characterize varying aspects of heterogeneity (transcriptomic, epigenomic, spatial/contextual) and their interplay.

We apply these methods to better understand the impact of cellular heterogeneity on cancer pathogenesis and prognosis.

Advancements in high-throughput sequencing and imaging technologies have uncovered tremendous genetic, epigenetic, transcriptional, and spatial heterogeneity in various cancers but their impact on clinical outcomes is not well understood. We establish close collaborations with clinical collaborators to develop and apply bioinformatics methods that contribute to a more complete understanding of how cellular heterogeneity impacts tumor progression, therapeutic resistance, and ultimately clinical prognosis. We are particularly interested in pediatric gliomas.