perform.scanpy: Performs Scanpy normalisation, hvg selection, scaling and...

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

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

Description

A new method-assay is produced. Raw counts are normalised and HVGs identified using Scanpy

Usage

perform.scanpy(
  object,
  assay = "RAW",
  slot = "counts",
  new.assay.suffix = "",
  target_sum = 10000,
  exclude_highly_expressed = FALSE,
  max_fraction = 0.05,
  key_added = "scanpy_norm_factor",
  log1 = TRUE,
  n_top_genes = NULL,
  max_mean = 6,
  min_mean = 0.0125,
  min_disp = 0.5,
  span = 0.3,
  n_bins = 20,
  flavor = "seurat",
  batch_key = NULL,
  vars.to.regress = 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
new.assay.suffix	Character. What should be added as a suffix to 'SCANPY
target_sum	Numerical. What should the data be scaled to. Default = 1e6
exclude_highly_expressed	Boolean. Should highly expressed genes be excluded. Default = FALSE
max_fraction	Numerical. If exclude_highly_expressed=True, consider cells as highly expressed that have more counts than max_fraction of the original total counts in at least one cell. Default = 0.05
key_added	Character. What should the column name be that contains cell scaling factors. Default = 'scanpy_norm_factor'
n_top_genes	Numerical. How many HVGs should be identified. Default = NULL
max_mean	Numerical. If n_top_genes is NULL, this is the maximum mean to determine HVGs. Default = 6
min_mean	Numerical. If n_top_genes is NULL, this is the minimum mean to determine HVGs. Default = 0.0125
min_disp	Numerical. If n_top_genes is NULL, The minimum dispersion that should be presented in a gene for it to be considered highly varaible. Default = 0.5
span	Numerical. The fraction of cells that should be subset for the LOESS fit model. Default = 0.3
n_bins	Numerical. Number of bins to produce when determining HVGs
batch_key	Character. Which column in the metadata identifies the batches of the cells. Default = NULL
vars.to.regress	Character. A single or multiple columns of information in the metadata that should be regressed from the dataset. Default = NULL
verbose	Logical Should function messages be printed?
seed	Numerical What seed should be set. Default = 1234
flavour	Character. Choosing which HVG selection method to use when, options: 'seurat', 'cell_ranger', 'seurat_v3'. Default = 'seurat'

Value

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

Examples

object <- perform.scanpy(object = object, 
                         vars.to.regress = 'RAW_total.counts')

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