Normalize the count data present in a given assay.

NormalizeData(object, ...)

# S3 method for default
NormalizeData(
object,
normalization.method = "LogNormalize",
scale.factor = 10000,
margin = 1,
block.size = NULL,
verbose = TRUE,
...
)

# S3 method for Assay
NormalizeData(
object,
normalization.method = "LogNormalize",
scale.factor = 10000,
margin = 1,
verbose = TRUE,
...
)

# S3 method for Seurat
NormalizeData(
object,
assay = NULL,
normalization.method = "LogNormalize",
scale.factor = 10000,
margin = 1,
verbose = TRUE,
...
)

## Arguments

object An object Arguments passed to other methods Method for normalization. LogNormalize: Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor. This is then natural-log transformed using log1p. CLR: Applies a centered log ratio transformation RC: Relative counts. Feature counts for each cell are divided by the total counts for that cell and multiplied by the scale.factor. No log-transformation is applied. For counts per million (CPM) set scale.factor = 1e6 Sets the scale factor for cell-level normalization If performing CLR normalization, normalize across features (1) or cells (2) How many cells should be run in each chunk, will try to split evenly across threads display progress bar for normalization procedure Name of assay to use

## Value

Returns object after normalization

## Examples

if (FALSE) {
data("pbmc_small")
pbmc_small
pmbc_small <- NormalizeData(object = pbmc_small)
}