VISION

Functional interpretation for scRNA-seq data

VISION aids in the interpretation of single-cell RNA-seq (scRNA-seq) data by selecting for gene signatures which describe coordinated variation between cells. While the software only requires an expression matrix and a signature library (available in online databases), it is also designed to integrate into existing scRNA-seq analysis pipelines by taking advantage of precomputed dimensionality reductions, trajectory inferences or clustering results. The results of this analysis are made available through a dynamic web-app which can be shared with collaborators without requiring them to install any additional software.

Please checkout the project on GitHub.



Relevant publications


Functional Interpretation of Single-Cell Similarity Maps.
D. Detomaso*, M. Jones*, M. Subramanian, J. Ye, N. Yosef.
Nature Communications , 2019
10.1038/s41467-019-12235-0
GitHub
FastProject: A Tool for Low-Dimensional Analysis of Single-Cell RNA-Seq Data
D. DeTomaso, N. Yosef.
BMC Bioinformatics , 2016
10.1186/s12859-016-1176-5
GitHub