Correction: Correct for batch effects
In debsin/dropClust: Single Cell Transcriptome Analysis

Description Usage Arguments Details Value Examples

Correct the merged count data based on rank values to obtain a set of reduced and corrected dimensions.

1 2	Correction(object, method = "default", close_th = 0.1, cells_th = 0.1, components = 2, ...)

`object`	A list of SingleCellExperiment objects, each representing a SingleCellExperiment object from a single batch.
`method`	character, one of c("default","fastmnn"). `default` mode performs the dropClust based correction followed by UMAP embedding. The `fastmnn` option performs the mutual neighbourhood based correction which is implemented in the `batchelor` package. when `FALSE` the batches are merged on the set of common genes across batches.
`close_th`	for the method = default, specifies the value at which the expression values of two genes will be considered as close pairs.
`cells_th`	for the method default, specifies the value to determine what proportion of total number of cells have close pairs.
`components`	number of reduced dimensions to return.
`...`	`umap` arguments may be passed.

Concatenate the expression counts of all cells from different batches into one expression count object. The merging is done on the set of union of DE genes obtained from the clustering of each batch.

A SingleCellExperiment object with two new entry under the reducedDim() container to store the reduced dimension components with the name "CComponents" and the rank expression matrix named "RankMat".

## Not run: 
library(SingleCellExperiment)
ncells <- 100
ngenes <- 1200
lambda <-abs(rnorm(ngenes))
counts.1 <- matrix(rpois(ncells*ngenes, lambda =  lambda), ncol=ncells, nrow=ngenes, byrow=TRUE)
rownames(counts.1) <- paste0("Gene", seq_len(ngenes))
colnames(counts.1) <- paste0("Cell", seq_len(ncells))
sce.1 <- SingleCellExperiment(assays = list(counts = counts.1))
rowData(sce.1)$Symbol <- paste0("Gene", seq_len(ngenes))

lambda <-abs(rnorm(ngenes))
counts.2 <- matrix(rpois(ncells*ngenes, lambda =  lambda), ncol=ncells, nrow=ngenes, byrow=TRUE)
rownames(counts.2) <- paste0("Gene", seq_len(ngenes))
colnames(counts.2) <- paste0("Cell", seq_len(ncells))
sce.2 <- SingleCellExperiment(assays = list(counts = counts.2))
rowData(sce.2)$Symbol <- paste0("Gene", seq_len(ngenes))

mixed_sce <- Merge(list(sce.1, sce.2), use.de.genes =TRUE)
mixed_sce <- Correction(mixed_sce, close_th=0.1, cells_th=0.2)

## End(Not run)