Run ComBat on Seurat's Assay5 object through IntegrateLayers

A wrapper to run ComBat or ComBat_seq on multi-layered Seurat V5 object

Usage

CombatIntegration(
  object,
  orig = NULL,
  groups = NULL,
  groups.name = NULL,
  layers = "data",
  scale.layer = "scale.data",
  features = NULL,
  reconstructed.assay = "combat.reconstructed",
  key.assay = "combat_",
  combat.function = c("combat", "combat_seq"),
  use.scaled = FALSE,
  verbose = TRUE,
  ...
)

Arguments

object

A Seurat object (or an Assay5 object if not called by IntegrateLayers)

orig

DimReduc object. Not to be set directly when called with IntegrateLayers, use orig.reduction argument instead

groups

A named data frame with grouping information. Preferably one-column when groups.name = NULL

groups.name

Column name from groups data frame that stores grouping information. If groups.name = NULL, the first column is used

layers

Name of the layers to use in the integration

scale.layer

Name of the scaled layer in Assay

features

Vector of feature names to input to the integration method. When features = NULL (default), the VariableFeatures are used. To pass all features, use the output of Features()

reconstructed.assay

Name for the assay containing the corrected expression matrix

key.assay

Optional key for the new combat assay. Format: "[:alnum:]*_"

combat.function

ComBat implementation to use. One of combat, combat_seq. Note that ComBat_seq is an improved model from ComBat but requires a dense matrix. Sparse to dense matrix conversion can be memory-intensive.

use.scaled

By default the layer passed to the layer argument is used. When use.scaled = TRUE, the scale.layer is input to ComBat.

verbose

Print messages. Set to FALSE to disable

...

Additional arguments passed on to ComBat or ComBat_seq.

Value

The function itself returns a list containing:

• a new Assay of name reconstructed.assay (key set to assay.key) with corrected cell counts.

When called via IntegrateLayers, a Seurat object with the new assay is returned

Note

This function requires the sva (Surrogate Variable Analysis) package to be installed

References

Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2006). DOI

Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genomics and Bioinformatics 2 (2020). DOI

Examples

if (FALSE) { # \dontrun{
# Preprocessing
obj <- UpdateSeuratObject(SeuratData::LoadData("pbmcsca"))
obj[["RNA"]] <- split(obj[["RNA"]], f = obj$Method)
obj <- NormalizeData(obj)
obj <- FindVariableFeatures(obj)
obj <- ScaleData(obj)
obj <- RunPCA(obj)

# After preprocessing, we integrate layers based on the "Method" variable:
obj <- IntegrateLayers(object = obj, method = CombatIntegration,
                       verbose = TRUE, layers = "data", scale.layer = NULL,
                       features = VariableFeatures(
                         FindVariableFeatures(obj, nfeatures = 5e3)
                       ))

# We can also change parameters such as the input data.
# Here we use the scale data, the ComBat implementation and we use the cell
# labels as a "biological condition of interest" (/!\ long):

obj <- IntegrateLayers(object = obj,  method = CombatIntegration,
                       verbose = TRUE, features = VariableFeatures(obj),
                       use.scaled = FALSE, combat.function = 'combat_seq',
                       group = obj[[]]$CellType, groups = obj[[]],
                       groups.name = "Method", layers = "counts")
} # }