Performs TPM normalisation — perform.tpm • IBRAP

Performs TPM normalisation, scran hvg selection, scaling and variance stabilisation and regression.

perform.tpm(
  object,
  assay = "RAW",
  slot = "counts",
  n.genes = 1500,
  do.scale = FALSE,
  do.center = TRUE,
  vars.to.regress = NULL,
  new.assay.suffix = "",
  biomart.dataset = "hsapiens_gene_ensembl",
  gene.lengths = NULL,
  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
n.genes: Numerical. Top number of genes to retain when finding HVGs. Default = 1500
do.scale: Boolean. Whether to scale the features variance. Default = TRUE
vars.to.regress: Character. Which column in the metadata should be regressed. Default = NULL
new.assay.suffix: Character. What should the new assay be called. Default = 'SCRAN'
biomart.dataset: Character. Which biomart dataset should be used, this normally corresponds with the species in question, default = 'hsapiens_gene_ensembl'. Check available datasets by performing the following. ensembl <- biomaRt::useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl"), then biomaRt::listDatasets(ensembl)
gene.lengths: DataFrame. A dataframe containing two columns, external_gene_name and transcript_length.
verbose: Logical Should function messages be printed?
...: Arguments to pass to Seurat::ScaleData
do.centre: Boolean. Whether to centre features to zero. Default = TRUE

Value

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

Examples


object <- perform.scanpy(object = object, 
                         vars.to.regress = 'RAW_total.counts', do.scale = T)
#> Error in perform.scanpy(object = object, vars.to.regress = "RAW_total.counts",     do.scale = T): unused argument (do.scale = T)