We are an academic research lab in the Department of Biomedical Engineering at Johns Hopkins University. We are interested in understanding the molecular and spatial-contextual factors shaping cellular identity and heterogeneity, particularly in the context of cancer and how this heterogeneity impacts tumor progression, therapeutic resistance, and ultimately clinical prognosis. We develop machine learning and other statistical approaches as computational software to enable ourselves and the scientific community to harness the power of large-scale multi-omic and imaging data in addressing these basic science and translational research questions.

Research Synopsis

* Denotes equal contribution

Statistical methods and software for analyses of single cell 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. Applying traditional bulk analysis methods on single cells has met with varied degrees of success due to the high levels of technical as well as biological stochasticity and noise inherent in single cell measurements. Therefore, we develop novel statistical methods to identify and characterize varying aspects of heterogeneity (genomic, transcriptomic, epigenomic) and their interplay in single cells.

Role of alternative splicing in development and disease

Alternative splicing governs cell-type transitions and cell fate in a variety of normal developmental processes including the mammalian brain. Aberrations in alternative splicing have also been shown to dysregulate cellular functions and drive cancer pathogenesis. We aim to gain a better understanding of the functional consequences of alternative splicing and dysregulation in normal development and in cancer.

Molecular and spatial-contextual factors impacting cancer progression

Advancements in high-throughput sequencing and imaging technologies have uncovered tremendous genetic, epigenetic, transcriptional, and even spatial heterogeneity in various cancers but their impact on clinical course is not well understood. We aim to establish close collaborations with clinical collaborators to focus on developing and applying bioinformatics methods that contribute to a more complete understanding of cancer pathogenesis, progression, treatment response, and resistance. We are particularly interested in pediatric gliomas.