runComBatSeq {singleCellTK} | R Documentation

## Apply ComBat-Seq batch effect correction method to SingleCellExperiment object

### Description

The ComBat-Seq batch adjustment approach assumes that batch effects represent non-biological but systematic shifts in the mean or variability of genomic features for all samples within a processing batch. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for batch effects.

### Usage

    runComBatSeq(
      inSCE,
      useAssay = "counts",
      batch = "batch",
      covariates = NULL,
      bioCond = NULL,
      useSVA = FALSE,
      assayName = "ComBatSeq",
      shrink = FALSE,
      shrinkDisp = FALSE,
      nGene = NULL
    )

### Arguments

`inSCE` | SingleCellExperiment inherited object. Required.
`useAssay` | A single character indicating the name of the assay requiring batch correction. Default `"counts"`.
`batch` | A single character indicating a field in `colData` that annotates the batches. Default `"batch"`.
`covariates` | A character vector indicating the fields in `colData` that annotates other covariates, such as the cell types. Default `NULL`.
`bioCond` | A single character indicating a field in `colData` that annotates the biological conditions. Default `NULL`.
`useSVA` | A logical scalar. Whether to estimate surrogate variables and use them as an empirical control. Default `FALSE`.
`assayName` | A single characeter. The name for the corrected assay. Will be saved to `assay`. Default `"ComBat"`.
`shrink` | A logical scalar. Whether to apply shrinkage on parameter estimation. Default `FALSE`.
`shrinkDisp` | A logical scalar. Whether to apply shrinkage on dispersion. Default `FALSE`.
`nGene` | An integer. Number of random genes to use in empirical Bayes estimation, only useful when `shrink` is set to `TRUE`. Default `NULL`.

### Details

For the parameters `covariates` and `useSVA`, when the cell type information is known, it is recommended to specify the cell type annotation to the argument `covariates`; if the cell types are unknown but expected to be balanced, it is recommended to run with default settings, yet informative covariates could still be useful. If the cell types are unknown and are expected to be unbalanced, it is recommended to set `useSVA` to `TRUE`.

### Value

The input SingleCellExperiment object with `assay(inSCE, assayName)` updated.

### Examples

    data('sceBatches', package = 'singleCellTK')
    sceBatches <- sample(sceBatches, 40)
    # Cell type known
    sceBatches <- runComBatSeq(sceBatches, "counts", "batch",
                               covariates = "cell_type",
                               assayName = "ComBat_cell_seq")
    # Cell type unknown but balanced
    #sceBatches <- runComBatSeq(sceBatches, "counts", "batch",
    #                           assayName = "ComBat_seq")
    # Cell type unknown and unbalanced
    #sceBatches <- runComBatSeq(sceBatches, "counts", "batch",
    #                           useSVA = TRUE,
    #                           assayName = "ComBat_sva_seq")

[Package singleCellTK version 2.2.0 Index]