SuperCell (version 1.0)

supercell_2_sce: Super-cells to SingleCellExperiment object

Description

This function transforms super-cell gene expression and super-cell partition into SingleCellExperiment object

Usage

supercell_2_sce(
  SC.GE,
  SC,
  fields = c(),
  var.genes = NULL,
  do.preproc = TRUE,
  is.log.normalized = TRUE,
  do.center = TRUE,
  do.scale = TRUE,
  ncomponents = 50
)

Value

SingleCellExperiment object

Arguments

SC.GE

gene expression matrix with genes as rows and cells as columns

SC

super-cell (output of SCimplify function)

fields

which fields of SC to use as cell metadata

var.genes

set of genes used as a set of variable features of SingleCellExperiment (by default is the set of genes used to generate super-cells)

do.preproc

whether to do prepocessing, including data normalization, scaling, HVG, PCA, nearest neighbors, TRUE by default, change to FALSE to speed up conversion

is.log.normalized

whether SC.GE is log-normalized counts. If yes, then SingleCellExperiment field assay name = 'logcounts' else assay name = 'counts'

do.center

whether to center gene expression matrix to compute PCA

do.scale

whether to scale gene expression matrix to compute PCA

ncomponents

number of principal components to compute

Examples

# \donttest{
data(cell_lines)
SC           <- SCimplify(cell_lines$GE, gamma = 20)
SC$ident     <- supercell_assign(clusters = cell_lines$meta, supercell_membership = SC$membership)
SC.GE        <- supercell_GE(cell_lines$GE, SC$membership)
sce          <- supercell_2_sce(SC.GE = SC.GE, SC = SC, fields = c("ident"))
# }