期刊
NATURE COMMUNICATIONS
卷 10, 期 -, 页码 -出版社
NATURE RESEARCH
DOI: 10.1038/s41467-019-12235-0
关键词
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资金
- NIH-NIAID [U19 AI090023]
- NIH [T32 GM067547]
- Chan-Zuckerberg Initiative [2018-18034]
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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