supercell_2_sce: Super-cells to SingleCellExperiment object

In SuperCell: Simplification of scRNA-Seq Data by Merging Together Similar Cells

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
)

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

Value

SingleCellExperiment object

Examples

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"))

SuperCell documentation built on Oct. 25, 2024, 5:07 p.m.