R: Compute IDER-based similarity

getIDEr {CIDER}

R Documentation

Compute IDER-based similarity

Description

Calculate the similarity matrix based on the metrics of Inter-group Differential ExpRession (IDER) with the selected batch effects regressed out.

Usage

getIDEr(
  seu,
  group.by.var = "initial_cluster",
  batch.by.var = "Batch",
  verbose = TRUE,
  use.parallel = FALSE,
  n.cores = 1,
  downsampling.size = 40,
  downsampling.include = TRUE,
  downsampling.replace = TRUE
)

Arguments

`seu`	Seurat S4 object with the column of 'initial_cluster' in its meta.data. Required.
`group.by.var`	initial clusters (batch-specific groups) variable. Needs to be one of the 'colnames(seu@meta.data)'. Default: "initial_cluster".
`batch.by.var`	Batch variable. Needs to be one of the 'colnames(seu@meta.data)'. Default: "Batch".
`verbose`	Boolean. Print the message and progress bar. (Default: TRUE)
`use.parallel`	Boolean. Use parallel computation, which requires doParallel; no progress bar will be printed out. Run time will be 1/n.cores compared to the situation when no parallelisation is used. (Default: FALSE)
`n.cores`	Numeric. Number of cores used for parallel computing (default: 1).
`downsampling.size`	Numeric. The number of cells representing each group. (Default: 40)
`downsampling.include`	Boolean. Using 'include = TRUE' to include the group smaller than required size. (Default: FALSE)
`downsampling.replace`	Boolean. Using 'replace = TRUE' if the group is smaller than required size and some cells will be repeatedly used. (Default: FALSE)

Value

A list of four objects: a similarity matrix, a numeric vector recording cells used and the data frame of combinations included.

Examples

library(CIDER)
data("pancreas")
ider <- getIDEr(pancreas, downsampling.size = 30)
head(ider)