Analysis of Inter-individual Variation in Population-scale scRNA-seq Studies

Peter Kharchenko, PhD

PhD Associate Professor of Biomedical Informatics, Harvard Medical School

Received a PhD in biophysics at Harvard University, studying gene regulation and metabolic networks under the advisement of George Church. He then completed a four-year postdoctoral fellowship in computational biology and genomics in the laboratory of Peter Park. He is currently studying the epigenetic mechanisms that regulate the growth and maintenance of normal tissues as well as the disruptions of the epigenetic state that contribute to a variety of disorders.

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Hosted by

Jan Křivánek

About the lecture

Tissue - and organism-level biological processes often involve coordinated action of multiple distinct cell types. Current computational methods for the analysis of single-cell RNA-sequencing (scRNA-seq) data, however, are not designed to capture co-variation of cell states across samples, in part due to the low number of biological samples in most scRNA-seq datasets. Recent advances in sample multiplexing have enabled population-scale scRNA-seq measurements of tens to hundreds of samples. To take advantage of such datasets, we developed a computational approach called single-cell Interpretable Tensor Decomposition (scITD). This method allows to characterize multicellular gene expression patterns that vary across different biological samples. These patterns capture how changes in one cell type are connected to changes in other cell types. The multicellular patterns can be further associated with known covariates (e.g., disease, treatment, or technical batch effects) and used to stratify heterogeneous samples. I will detail the approach and illustrate applications of this method to large-scale scRNA-seq disease studies.