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https://yoseflab.github.io/VISION/
Signature Analysis and Visualization for Single-Cell RNA-seq
https://yoseflab.github.io/VISION/
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Signature Analysis and Visualization for Single-Cell RNA-seq
- Host: GitHub
- URL: https://yoseflab.github.io/VISION/
- Owner: YosefLab
- License: mit
- Created: 2017-01-19T06:51:45.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2023-02-08T19:08:08.000Z (over 1 year ago)
- Last Synced: 2023-10-20T23:57:10.387Z (8 months ago)
- Language: R
- Homepage: https://yoseflab.github.io/VISION/
- Size: 94.6 MB
- Stars: 121
- Watchers: 5
- Forks: 27
- Open Issues: 27
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-expression-browser - VISION - cell RNA-seq. Source code available at https://github.com/YosefLab/VISION, described in https://doi.org/10.1038/s41467-019-12235-0 (Software list)
README
[](https://travis-ci.org/YosefLab/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.
* [Nature Communications publication](https://www.nature.com/articles/s41467-019-12235-0)
* [Full Documentation](https://yoseflab.github.io/VISION/)
* [Biorxiv Preprint](https://www.biorxiv.org/content/early/2018/08/29/403055)
## Installing VISION
We recommend installing VISION via github using devtools:
```r
library(devtools)
install_github("YosefLab/VISION")
```
See the DESCRIPTION file for a complete list of R dependencies. If the R dependencies are already installed, installation should finish in a few minutes.
## The VISION Pipeline
VISION generally follows the same pipeline from iteration to iteration, where minor differences can be specified via the various parameters in a VISION object. On a typical VISION run:
- For large datasets, or if the user so chooses, micropools are computed - grouping similar cells together to reduce the complexity of the analysis.
- If a latent space is not specified, PCA is performed and the top 30 components are retained.
- A KNN graph is constructed from the latent space, named the cell-cell similarity map
- Signature scores are computed using the expression matrix
- Signature local “consistencies” on the cell-cell similarity map are computed using the Geary-C statistic, an auto-correlation statistic.
- An interactive web-based report is generated that can be used to explore and interpret the dataset.
## How to run VISION
For general instructions on running VISION, see the [Getting Started](https://yoseflab.github.io/VISION/articles/VISION-vignette.html) vignette.
More information can be found throughout the rest of the tutorials on the [Documentation](https://yoseflab.github.io/VISION/) site.
## Sample Output
- [Live output example: ~9,000 CBMC's sequenced with the CITE-seq protocol](http://s133.cs.berkeley.edu:7703/)
- [Tour of the output report user interface(PDF)](https://github.com/YosefLab/VISION/raw/master/docs/VISION_Report_Manual.pdf)