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scCTS is an R package for the statistical modeling of thegene differential expression (DE) in scRNA-seq data. It identifies cell-type specific genes (markers) that consistently appear in historical population-level scRNA-seq (scRNA-seq) data.scCTS is built on top of the R packageSingleCellExperiment and supports parallel computation.

Except from our proposed method,scCTS also provides a common interface for classic DE methods such as the Wilcoxon rank-sum test, t-test andDESeq2.

You can installscCTS fromGitHub using thedevtools package:

if (!require("devtools",quietly=TRUE))    install.packages("devtools")devtools::install_github('ToryDeng/scCTS',dependencies=T,build_vignettes=T)library(scCTS)

Here we give a simple example to demonstrate how to runscCTS. Once the package is installed, you can load the simulated dataset included in the package:

data(sim.sce)

sim.sce is aSingleCellExperiment object with 200 genes and 10,000 cells.Next, you can runscCTS with a single line of code:

res<- scCTS(sim.sce,subject.rep='subject',celltype.rep='celltype',numCores=2)

Some explanations about the parameters:

• subject.rep: The name of the column that stores subject labels of cells in thecolData slot.
• celltype.rep: The name of the column that stores cell type labels in thecolData slot.
• numCores: Number of cores for parallel computation.

In thetested environment, the code finishes running within 10 seconds. The return valueres is a list containing lists for each cell type. Each list contains posterior probabilities of genes and parameter estimations for a particular cell type. For example, you can extract the posterior probabilities of genes to show DE incelltype1 using the following code:

res$celltype1$pp.d1

For more details about how to runscCTS and classic DE methods, please refer tovignette("scCTS").

If you want to reproduce results shown in the paper, please refer to the directoryreproducibility/ in this repo.

• CPU: AMD Ryzen Threadripper 3990X 64-Core Processor
• Memory: 256GB
• System: Ubuntu 20.04.6 LTS
• R version: 4.3.0

• CPU: Intel(R) Xeon(R) Gold 6240R CPU @ 2.40GHz
• Memory: 256GB
• System: Ubuntu 22.04.3 LTS
• R version: 4.4.0

@article{chenScCTSIdentifyingCell2024,title ={{{scCTS}}: Identifying the Cell Type-Specific Marker Genes from Population-Level Single-Cell {{RNA-seq}}},shorttitle ={{{scCTS}}},author ={Chen, Luxiao and Guo, Zhenxing and Deng, Tao and Wu, Hao},year ={2024},month = oct,journal ={Genome Biology},volume ={25},number ={1},pages ={269},issn ={1474-760X},doi ={10.1186/s13059-024-03410-8},langid ={english}}