scCTS
is an R package for the statistical modeling of the
gene 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 package SingleCellExperiment
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 and DESeq2
.
You can install scCTS
from GitHub using the devtools
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 run scCTS
. Once the package is installed, you can load the simulated dataset included in the package:
data(sim.sce)
sim.sce
is a SingleCellExperiment
object with 200 genes and 10,000 cells.
Next, you can run scCTS
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 the
colData
slot. - celltype.rep: The name of the column that stores cell type labels in the
colData
slot. - numCores: Number of cores for parallel computation.
In the tested environment, the code finishes running within 10 seconds. The return value res
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 in celltype1
using the following code:
res$celltype1$pp.d1
For more details about how to run scCTS
and classic DE methods, please refer to vignette("scCTS")
.
If you want to reproduce results shown in the paper, please refer to the directory reproducibility/ 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
Coming soon.