Functional interpretation of single cell similarity maps

Nat Commun. 2019 Sep 26;10(1):4376. doi: 10.1038/s41467-019-12235-0.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Computational Biology / methods*
  • Gene Expression Profiling / methods*
  • High-Throughput Nucleotide Sequencing / methods*
  • Humans
  • Internet
  • Reproducibility of Results
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis / methods*