This function calls sctransform::vst. The sctransform package is available at https://github.com/ChristophH/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.
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)), conserve.memory = FALSE, return.only.var.genes = TRUE, seed.use = 1448145, verbose = TRUE, ... )
A seurat object
Name of assay to pull the count data from; default is 'RNA'
Name for the new assay containing the normalized data
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.
Place corrected UMI matrix in assay counts slot; default is TRUE
Number of subsampling cells used to build NB regression; default is 5000
Genes to calculate residual features for; default is NULL (all genes). If specified, will be set to VariableFeatures of the returned object.
Use this many features as variable features after ranking by residual variance; default is 3000. Only applied if residual.features is not set.
Instead of setting a fixed number of variable features,
use this residual variance cutoff; this is only used when
is set to NULL; default is 1.3. Only applied if residual.features is not set.
Variables to regress out in a second non-regularized linear regression. For example, percent.mito. Default is NULL
Whether to scale residuals to have unit variance; default is FALSE
Whether to center residuals to have mean zero; default is TRUE
Range to clip the residuals to; default is
where n is the number of cells
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
If set to TRUE the scale.data matrices in output assay are subset to contain only the variable genes; default is TRUE
Set a random seed. By default, sets the seed to 1448145. Setting NULL will not set a seed.
Whether to print messages and progress bars
Additional parameters passed to
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.
data("pbmc_small") SCTransform(object = pbmc_small) #> Calculating cell attributes from input UMI matrix: log_umi #> Variance stabilizing transformation of count matrix of size 220 by 80 #> Model formula is y ~ log_umi #> Get Negative Binomial regression parameters per gene #> Using 220 genes, 80 cells #> | | | 0% | |======================================================================| 100% #> Second step: Get residuals using fitted parameters for 220 genes #> | | | 0% | |======================================================================| 100% #> Computing corrected count matrix for 220 genes #> | | | 0% | |======================================================================| 100% #> Calculating gene attributes #> Wall clock passed: Time difference of 0.9715059 secs #> Determine variable features #> Place corrected count matrix in counts slot #> Centering data matrix #> Set default assay to SCT #> An object of class Seurat #> 450 features across 80 samples within 2 assays #> Active assay: SCT (220 features, 220 variable features) #> 3 layers present: counts, data, scale.data #> 1 other assay present: RNA #> 2 dimensional reductions calculated: pca, tsne