NormalizeData: Normalize Data

In ibseq/scs-analysis: Tools for Single Cell Genomics

Description

Normalize the count data present in a given assay.

Usage

NormalizeData(object, ...)

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

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

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

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

Value

Returns object after normalization

Examples

## Not run: 
data("pbmc_small")
pbmc_small
pmbc_small <- NormalizeData(object = pbmc_small)

## End(Not run)

ibseq/scs-analysis documentation built on Feb. 27, 2021, 12:35 a.m.