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northstar: leveraging cell atlases to identify healthy and neoplastic cells in transcriptomes from human tumors

By Fabio Zanini, Bojk A. Berghuis, Robert C. Jones, Benedetta Nicolis di Robilant, Rachel Yuan Nong, Jeffrey Norton, Michael F Clarke, Stephen R Quake

Posted 27 Oct 2019
bioRxiv DOI: 10.1101/820928

Cell atlases are revolutionizing our understanding of tissue and disease heterogeneity, yet most single-cell transcriptomic analyses on tumors are not leveraging atlases effectively. We developed northstar, a computational approach to classify cells in tumor datasets guided by but not restricted by previously annotated cell atlases. To benchmark northstar, we transferred annotations from a human brain atlas to a published dataset on glioblastoma and could recapitulate the tumor composition accurately and within seconds. We then collected 1,622 cells from 11 pancreatic tumors and could robustly identify healthy pancreatic and immune cells and neoplastic cell states. Three cell populations were shared across patients while five were private to a single sample. northstar's cell type classification offered rapid insight into the origins of neuroendocrine and exocrine tumors and fibromatosis. northstar is a useful tool to classify single-cell transcriptomes into known and novel cell types in the age of cell atlases.

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