In genecell/COSGR: Marker Gene Identification for Single Cell Sequencing Data

knitr::opts_chunk$set(echo = TRUE)

Overview

COSG is a cosine similarity-based method for more accurate and scalable marker gene identification.

• COSG is a general method for cell marker gene identification across different data modalities, e.g., scRNA-seq, scATAC-seq and spatially resolved transcriptome data.
• Marker genes or genomic regions identified by COSG are more indicative and with greater cell-type specificity.
• COSG is ultrafast for large-scale datasets, and is capable of identifying marker genes for one million cells in less than two minutes.

The method and benchmarking results are described in Dai et al., (2021).

Installation

Install from GitHub:

# install.packages('remotes')
remotes::install_github(repo = 'genecell/COSGR')

Load the library:

library(COSG) 
library(Seurat) 

Run COSG

marker_cosg<-cosg(
  pbmc_small,
  groups='all',
  assay='RNA',
  slot='data',
  mu=1,
  n_genes_user=2000)

Check markers:

head(marker_cosg$names)

Check scores:

head(marker_cosg$scores)

top_list<-c()
for (group in colnames(marker_cosg$names)){
    top_i<-marker_cosg$names[group][1:10,1]
    top_list<-c(top_list,top_i)
}

Expression pattern:

DotPlot(pbmc_small, 
        assay = 'RNA',
        features =  unique(top_list)) + RotatedAxis()

genecell/COSGR documentation built on Jan. 3, 2023, 10:57 a.m.