Functional interpretation of single cell similarity maps

David DeTomaso, Matthew G. Jones, Meena Subramaniam, Tal Ashuach, Chun J. Ye, Nir Yosef

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number4376
Number of pages11
JournalNature Communications
StatePublished - 26 Sep 2019
Externally publishedYes


Dive into the research topics of 'Functional interpretation of single cell similarity maps'. Together they form a unique fingerprint.

Cite this