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bioRxiv · 10.1101/403055

Functional Interpretation of Single-Cell Similarity Maps

DeTomaso, D.·Jones, M.·Subramaniam, M.·Ashuach, T.·Ye, C. J.·Yosef, N.
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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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DeTomaso, D., Jones, M., Subramaniam, M., Ashuach, T., Ye, C. J., Yosef, N.. 2018-08-29. Functional Interpretation of Single-Cell Similarity Maps. https://doi.org/10.1101/403055

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