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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.

Author information

1
Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA.
2
Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA.
3
Department of Epidemiology and Biostatistics, Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, University of California, San Francisco, CA, USA.
4
Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA. niryosef@berkeley.edu.
5
Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA, USA. niryosef@berkeley.edu.
6
Chan-Zuckerberg Biohub, San Francisco, CA, 94158, USA. niryosef@berkeley.edu.

Abstract

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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