Performs Scran normalisation, hvg selection, scaling and variance stabilisation and regression.

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,
  ...
)

Arguments

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

Value

Produces a new 'methods' assay containing normalised, scaled and HVGs.

Examples


object <- perform.scran(object = object, 
                        assay = 'RAW', 
                        slot = 'counts', 
                        vars.to.regress = 'RAW_total.counts', do.scale = T)
#> Error in is(object = object, class2 = "IBRAP"): object 'object' not found