CIDER (version 0.99.1)

getIDEr: 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
)

Value

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

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([e-mail])`. Default: "initial_cluster".

batch.by.var

Batch variable. Needs to be one of the `colnames([e-mail])`. 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)

See Also

plotNetwork finalClustering

Examples

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