Scale and center the data.

Scales and centers features in the dataset. If variables are provided in vars.to.regress, they are individually regressed against each feature, and the resulting residuals are then scaled and centered.

Usage

ScaleData(object, ...)

# Default S3 method
ScaleData(
  object,
  features = NULL,
  vars.to.regress = NULL,
  latent.data = NULL,
  split.by = NULL,
  model.use = "linear",
  use.umi = FALSE,
  do.scale = TRUE,
  do.center = TRUE,
  scale.max = 10,
  block.size = 1000,
  min.cells.to.block = 3000,
  verbose = TRUE,
  ...
)

# S3 method for class 'IterableMatrix'
ScaleData(
  object,
  features = NULL,
  vars.to.regress = NULL,
  latent.data = NULL,
  do.scale = TRUE,
  do.center = TRUE,
  scale.max = 10,
  verbose = TRUE,
  ...
)

# S3 method for class 'Assay'
ScaleData(
  object,
  features = NULL,
  vars.to.regress = NULL,
  latent.data = NULL,
  split.by = NULL,
  model.use = "linear",
  use.umi = FALSE,
  do.scale = TRUE,
  do.center = TRUE,
  scale.max = 10,
  block.size = 1000,
  min.cells.to.block = 3000,
  verbose = TRUE,
  ...
)

# S3 method for class 'Seurat'
ScaleData(
  object,
  features = NULL,
  assay = NULL,
  vars.to.regress = NULL,
  split.by = NULL,
  model.use = "linear",
  use.umi = FALSE,
  do.scale = TRUE,
  do.center = TRUE,
  scale.max = 10,
  block.size = 1000,
  min.cells.to.block = 3000,
  verbose = TRUE,
  ...
)

Arguments

object

An object

...

Arguments passed to other methods

features

Vector of features names to scale/center. Default is variable features.

vars.to.regress

Variables to regress out (previously latent.vars in RegressOut). For example, nUMI, or percent.mito.

latent.data

Extra data to regress out, should be cells x latent data

split.by

Name of variable in object metadata or a vector or factor defining grouping of cells. See argument f in split for more details

model.use

Use a linear model or generalized linear model (poisson, negative binomial) for the regression. Options are 'linear' (default), 'poisson', and 'negbinom'

use.umi

Regress on UMI count data. Default is FALSE for linear modeling, but automatically set to TRUE if model.use is 'negbinom' or 'poisson'

do.scale

Whether to scale the data.

do.center

Whether to center the data.

scale.max

Max value to return for scaled data. The default is 10. Setting this can help reduce the effects of features that are only expressed in a very small number of cells. If regressing out latent variables and using a non-linear model, the default is 50.

block.size

Default size for number of features to scale at in a single computation. Increasing block.size may speed up calculations but at an additional memory cost.

min.cells.to.block

If object contains fewer than this number of cells, don't block for scaling calculations.

verbose

Displays a progress bar for scaling procedure

assay

Name of Assay to scale

Details

ScaleData now incorporates the functionality of the function formerly known as RegressOut (which regressed out given the effects of provided variables and then scaled the residuals). To make use of the regression functionality, simply pass the variables you want to remove to the vars.to.regress parameter.

Setting center to TRUE will center the expression for each feature by subtracting the average expression for that feature. Setting scale to TRUE will scale the expression level for each feature by dividing the centered feature expression levels by their standard deviations if center is TRUE and by their root mean square otherwise.