Seurat (version 6.0.0)

NormalizeData: Normalize Data

Description

Normalize the count data present in a given assay.

Usage

NormalizeData(object, ...)
# S3 method for V3Matrix
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,
  ...
)

Value

Returns object after normalization

Arguments

object

An object

...

Arguments passed to other methods

normalization.method

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

scale.factor

Sets the scale factor for cell-level normalization

margin

If performing CLR normalization, normalize across features (1) or cells (2)

block.size

How many cells should be run in each chunk - will be split evenly across threads. If supplied, temporarily sets the thread count to ceiling(ncol(object) / block.size), capped at the number of available cores.

verbose

Whether to display a progress bar

assay

Name of assay to use

Examples

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