CreateBigSingleRObject: Analyze very big data sets. Runs SingleR on small chunks...
In dviraran/SingleR: Reference-based single-cell RNA-seq annotation

Analyze very big data sets. Runs SingleR on small chunks (10,000 cells per run) and then combines them together.

CreateBigSingleRObject(counts, annot = NULL, project.name, xy, clusters,
  N = 10000, min.genes = 200, technology = "10X",
  species = "Human", citation = "", ref.list = list(),
  normalize.gene.length = F, variable.genes = "de", fine.tune = T,
  reduce.file.size = T, do.signatures = F, do.main.types = T,
  temp.dir = getwd(), numCores = SingleR.numCores)

`counts`	a tab delimited text file containing the counts matrix, a 10X directory name or a matrix with the counts.
`annot`	a tab delimited text file or a data.frame. Rownames correspond to column names in the counts data
`project.name`	the project name
`xy`	a matrix with the xy coordinates. From the original single-cell object.
`clusters`	the clusters identities.From the original single-cell object.
`N`	number of cells in each iteration. Default is 10000.
`min.genes`	Include cells where at least this many genes are detected (number non-zero genes).
`technology`	The technology used for creating the single-cell data.
`species`	The species of the sample ('Human' or 'Mouse').
`citation`	a citation for the project.
`ref.list`	a list of reference objects. If NULL uses the predefined reference objects - Mouse: ImmGen and Mouse.RNAseq, Human: HPCA and Blueprint+Encode.
`normalize.gene.length`	if a full-length method set to TRUE, if a 3' method set to FALSE.
`variable.genes`	variable gene method to use - 'sd' or 'de'. Default is 'de'.
`fine.tune`	perform fine tuning. Default is TRUE. Fine-tuning may take long to run.
`do.signatures`	create signatures data
`do.main.types`	run the SingleR pipeline for main cell types (cell types grouped together) as well.
`temp.dir`	used by the SingleR webtool.
`numCores`	Number of cores to use.
`clusters`	input cluster id for each of the cells with at least min.genes, if NULL uses SingleR clusterings.