Functional interpretation of single cell similarity maps

David DeTomaso, Matthew G. Jones, Meena Subramaniam, Tal Ashuach, Chun J. Ye and Nir Yosef ()
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David DeTomaso: University of California Berkeley
Matthew G. Jones: University of California
Meena Subramaniam: University of California
Tal Ashuach: University of California Berkeley
Chun J. Ye: University of California
Nir Yosef: University of California, Berkeley

Nature Communications, 2019, vol. 10, issue 1, 1-11

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-12235-0

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