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.