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Nat Commun. 2019 Sep 26;10(1):4376. doi: 10.1038/s41467-019-12235-0.

Functional interpretation of single cell similarity maps.

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Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA.
Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA.
Department of Epidemiology and Biostatistics, Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, University of California, San Francisco, CA, USA.
Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA, USA.
Chan-Zuckerberg Biohub, San Francisco, CA, 94158, USA.


We present Vision, a tool for annotating the sources of variation in single cell RNA-seq data in an automated and scalable manner. Vision operates directly on the manifold of cell-cell similarity and employs a flexible annotation approach that can operate either with or without preconceived stratification of the cells into groups or along a continuum. We demonstrate the utility of Vision in several case studies and show that it can derive important sources of cellular variation and link them to experimental meta-data even with relatively homogeneous sets of cells. Vision produces an interactive, low latency and feature rich web-based report that can be easily shared among researchers, thus facilitating data dissemination and collaboration.

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