Seurat (version 4.3.0.1)

NormalizeData: Normalize Data

Description

Normalize the count data present in a given assay.

Usage

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,
  ...
)

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 try to split evenly across threads

verbose

display progress bar for normalization procedure

assay

Name of assay to use

Examples

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