SCTransform: Perform sctransform-based normalization

In Seurat: Tools for Single Cell Genomics

Perform sctransform-based normalization

Description

This function calls sctransform::vst. The sctransform package is available at https://github.com/satijalab/sctransform. Use this function as an alternative to the NormalizeData, FindVariableFeatures, ScaleData workflow. Results are saved in a new assay (named SCT by default) with counts being (corrected) counts, data being log1p(counts), scale.data being pearson residuals; sctransform::vst intermediate results are saved in misc slot of new assay.

Usage

SCTransform(object, ...)

## Default S3 method:
SCTransform(
  object,
  cell.attr,
  reference.SCT.model = NULL,
  do.correct.umi = TRUE,
  ncells = 5000,
  residual.features = NULL,
  variable.features.n = 3000,
  variable.features.rv.th = 1.3,
  vars.to.regress = NULL,
  latent.data = NULL,
  do.scale = FALSE,
  do.center = TRUE,
  clip.range = c(-sqrt(x = ncol(x = umi)/30), sqrt(x = ncol(x = umi)/30)),
  vst.flavor = "v2",
  conserve.memory = FALSE,
  return.only.var.genes = TRUE,
  seed.use = 1448145,
  verbose = TRUE,
  ...
)

## S3 method for class 'Assay'
SCTransform(
  object,
  cell.attr,
  reference.SCT.model = NULL,
  do.correct.umi = TRUE,
  ncells = 5000,
  residual.features = NULL,
  variable.features.n = 3000,
  variable.features.rv.th = 1.3,
  vars.to.regress = NULL,
  latent.data = NULL,
  do.scale = FALSE,
  do.center = TRUE,
  clip.range = c(-sqrt(x = ncol(x = object)/30), sqrt(x = ncol(x = object)/30)),
  vst.flavor = "v2",
  conserve.memory = FALSE,
  return.only.var.genes = TRUE,
  seed.use = 1448145,
  verbose = TRUE,
  ...
)

## S3 method for class 'Seurat'
SCTransform(
  object,
  assay = "RNA",
  new.assay.name = "SCT",
  reference.SCT.model = NULL,
  do.correct.umi = TRUE,
  ncells = 5000,
  residual.features = NULL,
  variable.features.n = 3000,
  variable.features.rv.th = 1.3,
  vars.to.regress = NULL,
  do.scale = FALSE,
  do.center = TRUE,
  clip.range = c(-sqrt(x = ncol(x = object[[assay]])/30), sqrt(x = ncol(x =
    object[[assay]])/30)),
  vst.flavor = "v2",
  conserve.memory = FALSE,
  return.only.var.genes = TRUE,
  seed.use = 1448145,
  verbose = TRUE,
  ...
)

## S3 method for class 'IterableMatrix'
SCTransform(
  object,
  cell.attr,
  reference.SCT.model = NULL,
  do.correct.umi = TRUE,
  ncells = 5000,
  residual.features = NULL,
  variable.features.n = 3000,
  variable.features.rv.th = 1.3,
  vars.to.regress = NULL,
  latent.data = NULL,
  do.scale = FALSE,
  do.center = TRUE,
  clip.range = c(-sqrt(x = ncol(x = object)/30), sqrt(x = ncol(x = object)/30)),
  vst.flavor = "v2",
  conserve.memory = FALSE,
  return.only.var.genes = TRUE,
  seed.use = 1448145,
  verbose = TRUE,
  ...
)

Arguments

object	UMI counts matrix
...	Additional parameters passed to sctransform::vst
cell.attr	A metadata with cell attributes
reference.SCT.model	If not NULL, compute residuals for the object using the provided SCT model; supports only log_umi as the latent variable. If residual.features are not specified, compute for the top variable.features.n specified in the model which are also present in the object. If residual.features are specified, the variable features of the resulting SCT assay are set to the top variable.features.n in the model.
do.correct.umi	Place corrected UMI matrix in assay counts slot; default is TRUE
ncells	Number of subsampling cells used to build NB regression; default is 5000
residual.features	Genes to calculate residual features for; default is NULL (all genes). If specified, will be set to VariableFeatures of the returned object.
variable.features.n	Use this many features as variable features after ranking by residual variance; default is 3000. Only applied if residual.features is not set.
variable.features.rv.th	Instead of setting a fixed number of variable features, use this residual variance cutoff; this is only used when variable.features.n is set to NULL; default is 1.3. Only applied if residual.features is not set.
vars.to.regress	Variables to regress out in a second non-regularized linear
latent.data	Extra data to regress out, should be cells x latent data regression. For example, percent.mito. Default is NULL
do.scale	Whether to scale residuals to have unit variance; default is FALSE
do.center	Whether to center residuals to have mean zero; default is TRUE
clip.range	Range to clip the residuals to; default is c(-sqrt(n/30), sqrt(n/30)), where n is the number of cells
vst.flavor	When set to 'v2' sets method = glmGamPoi_offset, n_cells=2000, and exclude_poisson = TRUE which causes the model to learn theta and intercept only besides excluding poisson genes from learning and regularization
conserve.memory	If set to TRUE the residual matrix for all genes is never created in full; useful for large data sets, but will take longer to run; this will also set return.only.var.genes to TRUE; default is FALSE
return.only.var.genes	If set to TRUE the scale.data matrices in output assay are subset to contain only the variable genes; default is TRUE
seed.use	Set a random seed. By default, sets the seed to 1448145. Setting NULL will not set a seed.
verbose	Whether to print messages and progress bars
assay	Name of assay to pull the count data from; default is 'RNA'
new.assay.name	Name for the new assay containing the normalized data; default is 'SCT'

Value

Returns a Seurat object with a new assay (named SCT by default) with counts being (corrected) counts, data being log1p(counts), scale.data being pearson residuals; sctransform::vst intermediate results are saved in misc slot of the new assay.

See Also

correct_counts get_residuals

Seurat documentation built on April 12, 2025, 2:27 a.m.