View source: R/perform.scran.normalisation.R
perform.scran | R Documentation |
A new method-assay is produced. Raw counts are normalised and HVGs identified using Scran
perform.scran( object, assay = "RAW", slot = "counts", batch = NULL, vars.to.regress = NULL, do.scale = TRUE, do.center = TRUE, new.assay.suffix = "", n.genes = 1500, max.cluster.size = 1000, center_size_factors = TRUE, verbose = FALSE, seed = 1234, ... )
object |
IBRAP S4 class object |
assay |
Character. String containing indicating which assay to use |
slot |
Character. String indicating which slot within the assay should be sourced |
batch |
Character. Which column in the metadata defines the batches. Default = NULL |
vars.to.regress |
Character. Which column in the metadata should be regressed. Default = NULL |
do.scale |
Boolean. Whether to scale the features variance. Default = TRUE |
do.center |
Boolean. Whether to centre features to zero. Default = TRUE |
new.assay.suffix |
Character. What should be added as a suffix for SCRAN |
n.genes |
Numerical. Top number of genes to retain when finding HVGs. Default = 1500 |
max.cluster.size |
Numerical. When performing quickCluster, what is the maximum size the clusters can be. Default = 1000 |
center_size_factors |
Boolean Should size factor variance be centred. Default = TRUE |
verbose |
Logical Should function messages be printed? |
seed |
Numerical What seed should be set. Default = 1234 |
... |
Arguments to pass to Seurat::ScaleData |
Produces a new 'methods' assay containing normalised, scaled and HVGs.
object <- perform.scran(object = object, assay = 'RAW', slot = 'counts', vars.to.regress = 'RAW_total.counts', do.scale = T)
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