perform.tpm: Performs TPM normalisation

In connorhknight/IBRAP: Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP)

Performs TPM normalisation

Description

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

Usage

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)

connorhknight/IBRAP documentation built on March 9, 2023, 7:01 p.m.