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https://github.com/wycwycpku/RSCORE
https://github.com/wycwycpku/RSCORE
Last synced: 23 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/wycwycpku/RSCORE
- Owner: wycwycpku
- Created: 2019-06-06T10:25:03.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-09-02T09:01:30.000Z (almost 3 years ago)
- Last Synced: 2024-02-24T14:38:33.197Z (4 months ago)
- Language: R
- Size: 21 MB
- Stars: 25
- Watchers: 5
- Forks: 9
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Lists
- awesome_single_cell - SCORE - [R] - [Enhancing single-cell cellular state inference by incorporating molecular network features](https://www.biorxiv.org/content/10.1101/699959v1). (Software packages / RNA-seq)
- awesome-single-cell - SCORE - [R] - [Enhancing single-cell cellular state inference by incorporating molecular network features](https://www.biorxiv.org/content/10.1101/699959v1). (Software packages / RNA-seq)
- awesome-single-cell - SCORE - [R] - [Enhancing single-cell cellular state inference by incorporating molecular network features](https://www.biorxiv.org/content/10.1101/699959v1). (Software packages / RNA-seq)
README
# We are now updating RSCORE to its second version. It's almost ready. Just a moment, please! 2021.09.02
# RSCORE
RSCORE is an R package to explore single-cell RNA-seq data with the view of molecular networks. The method is outlined in our manuscript
**Enhancing single-cell cellular state inference by incorporating molecular network features**
*Ji Dong, Peijie Zhou, Yichong Wu, Wendong Wang, Yidong Chen, Xin Zhou, Haoling Xie, Jiansen Lu, Xiannian Zhang, Lu Wen, Wei Fu, Tiejun Li, Fuchou Tang*.
The preprint version is available on [bioRxiv](https://doi.org/10.1101/699959).
## Install
To run RSCORE, you need to install some extra dependencies:
```
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
## Required
BiocManager::install(c("Seurat", "AUCell", "STRINGdb", "propr", "coop", "network","intergraph"))
## To support parallel execution
BiocManager::install("doRNG")
install.packages("doMC", repos="http://R-Forge.R-project.org")
install.packages("doParallel")
## To get marker genes quickly
BiocManager::install("mahmoudibrahim/genesorteR")
## To do GO enrichment
BiocManager::install(c("clusterProfiler", "org.Hs.eg.db"))
```
Then you can install RSCORE:
```
if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools")
devtools::install_github("wycwycpku/RSCORE")
```
if you want to build vignettes, you need to add an option
```
devtools::install_github("wycwycpku/RSCORE", build_opts = NULL)
```
## Tutorial
For more details and basic usage see following tutorials
1. [Single cells from different cell lines](https://github.com/wycwycpku/RSCORE/blob/master/vignettes/RSCORE_Tutorials.pdf)
2. [Human embryonic cell altas (HECA)](https://github.com/zorrodong/HECA)
As for PPI data, we have collected some information for human (9606) and mouse (10090) in [PPI_data](https://github.com/wycwycpku/PPI_data). You can also use our functions *getPPI_Biogrid()* or *getPPI_String()*.
## News
2019/10/26:
- Get PPI data from STRING 11.0.
2019/07/23:
- Add GO enrichment function.
- Correct some bugs.