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Functional Interpretation of Single-Cell Similarity Maps

David DeTomaso, Matthew Jones, Meena Subramaniam, Tal Ashuach, Chun J. Ye, Nir Yosef
doi: https://doi.org/10.1101/403055
David DeTomaso
1Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA
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Matthew Jones
2Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
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Meena Subramaniam
2Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
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Tal Ashuach
1Center for Computational Biology, University of California Berkeley, Berkeley, CA, USA
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Chun J. Ye
3Department of Epidemiology and Biostatistics, Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics, University of California, San Francisco, CA, USA
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Nir Yosef
5Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
6Ragon Institute of Massachusetts General Hospital, MIT and Harvard, Cambridge, MA, USA
7Chan Zuckerberg Biohub Investigator
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  • For correspondence: niryosef@berkeley.edu
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Abstract

We present VISION, a tool for annotating the sources of variation in single cell RNA-seq data in an automated, unbiased 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 using a relatively homogeneous set of B cells from a cohort of lupus patients and healthy controls and show that it can derive important sources of cellular variation and link them to clinical phenotypes in a stratification free manner. VISION produces an interactive, low latency and feature rich web-based report that can be easily shared amongst researchers.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 28, 2018.
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Functional Interpretation of Single-Cell Similarity Maps
David DeTomaso, Matthew Jones, Meena Subramaniam, Tal Ashuach, Chun J. Ye, Nir Yosef
bioRxiv 403055; doi: https://doi.org/10.1101/403055
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Functional Interpretation of Single-Cell Similarity Maps
David DeTomaso, Matthew Jones, Meena Subramaniam, Tal Ashuach, Chun J. Ye, Nir Yosef
bioRxiv 403055; doi: https://doi.org/10.1101/403055

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