In huangyh09/DCATS: Differential Composition Analysis Transformed by a Similarity matrix

knitr::opts_chunk$set(
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DCATS

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 0.99.0.

Installation

From Biocounductor

if (!requireNamespace("BiocManager"))
install.packages("BiocManager")
BiocManager::install("DCTAS")

From R

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")

For development

Download this repository to your local machine and open it in Rstudio as a project, and build it by install and restart.

Getting started

The best place to start are the vignettes. From inside an R session, load DCATS and then browse the vignettes:

library(DCATS)
browseVignettes("DCATS")

Example

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)

huangyh09/DCATS documentation built on Nov. 25, 2022, 7:02 a.m.