cosg: Accurate and fast cell marker gene identification with COSG

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

Accurate and fast cell marker gene identification with COSG

Description

Marker gene identification for cell groups in a given dataset.

Usage

cosg(
  object,
  groups = "all",
  assay = "RNA",
  slot = "data",
  mu = 1,
  remove_lowly_expressed=TRUE,
  expressed_pct=0.1,
  n_genes_user = 100
)

Arguments

assay	Assay to use in marker gene identification
slot	Slot to pull data from
mu	The penalty factor to penalize gene expression in cells not belonging to the cluster of interest
remove_lowly_expressed	If TRUE, genes that express a percentage of target cells smaller than a specific value (expressed_pct) are not considered as marker genes for the target cells. The default value is TRUE.
expressed_pct	When remove_lowly_expressed is set to TRUE, genes that express a percentage of target cells smaller than a specific value (expressed_pct) are not considered as marker genes for the target cells.
n_genes_user	Number of top ranked genes returned in the result

Value

A list containing two dataframes for ranked marker genes' names and scores, respectively

Examples

suppressMessages(library(Seurat))
data('pbmc_small',package='Seurat')
# Check cell groups:
table(Idents(pbmc_small))
#######
# Run COSG:
marker_cosg <- cosg(
 pbmc_small,
 groups='all',
 assay='RNA',
 slot='data',
 mu=1,
 n_genes_user=100)
#######
# Check the marker genes:
 head(marker_cosg$names)
 head(marker_cosg$scores)

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