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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.

The workflow of RSCORE

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
  2. Human embryonic cell altas (HECA)

As for PPI data, we have collected some information for human (9606) and mouse (10090) in PPI_data. You can also use our functions getPPI_Biogrid() or getPPI_String().

News

2019/10/26:

2019/07/23: