This R package contains methods to detect the differential composition abundances between multiple conditions in singel-cell experiments.
The latest version of the DCATS package is 0.99.7. It is under the
MIT license.
The latest DCATS package can be conveniently installed using the
devtools
package thus:
## install dependencies
install.packages(c("MCMCpack", "matrixStats", "robustbase", "aod", "e1071"))
## dependencies for vignette
install.packages(c("SeuratObject", "Seurat", "robustbase", "aod", "e1071"))
devtools::install_github('satijalab/seurat-data')# install.packages("devtools")
devtools::install_github("holab-hku/DCATS", build_vignettes = TRUE)You can also install DCATS without building the vignette:
devtools::install_github("holab-hku/DCATS")
if (!requireNamespace("BiocManager"))
install.packages("BiocManager")
BiocManager::install("DCTAS")Download this repository to your local machine and open it in Rstudio as a project, and build it by install and restart.
The best place to start are the vignettes. Inside an R session, load
DCATS and then browse the vignette about the usage guidance of
DCATS:
library(DCATS)
browseVignettes("DCATS")The tutorial demonstrating how to use DCATS after using
Seurat pipeline to process
data can be found in
Integrate DCATS with Seurat pipeline.
This is a basic example which shows you how to estimate a similarity matrix from KNN graph and do the differential abundance test using this similarity matrix.
library(DCATS)
data("simulation")
knn_mat = knn_simMat(simulation$knnGraphs, simulation$labels)
sim_count = rbind(simulation$numb_cond1, simulation$numb_cond2)
sim_design = data.frame(condition = c("c1", "c1", "c2"))
knn_mat[colnames(sim_count),]
res = dcats_GLM(as.matrix(sim_count), sim_design, similarity_mat = knn_mat)
print(res$LRT_pvals)