runPCA

runPCA

Description

runs a Principal Component Analysis

Usage

runPCA(
  gobject,
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "genes"),
  name = "pca",
  genes_to_use = "hvg",
  return_gobject = TRUE,
  center = TRUE,
  scale_unit = TRUE,
  ncp = 100,
  method = c("irlba", "factominer"),
  rev = FALSE,
  set_seed = TRUE,
  seed_number = 1234,
  verbose = TRUE,
  ...
)

Arguments

Argument	Description
gobject	giotto object
expression_values	expression values to use
reduction	cells or genes
name	arbitrary name for PCA run
genes_to_use	subset of genes to use for PCA
return_gobject	boolean: return giotto object (default = TRUE)
center	center data first (default = TRUE)
scale_unit	scale features before PCA (default = TRUE)
ncp	number of principal components to calculate
method	which implementation to use
rev	do a reverse PCA
set_seed	use of seed
seed_number	seed number to use
verbose	verbosity of the function
...	additional parameters for PCA (see details)

Details

See ``prcomp_irlba` <#prcompirlba>`_ and ``PCA` <#pca>`_ for more information about other parameters.

• genes_to_use = NULL: will use all genes from the selected matrix

• genes_to_use = : can be used to select a column name of highly variable genes, created by (see ``calculateHVG` <#calculatehvg>`_ )

• genes_to_use = c(‘geneA’, ‘geneB’, …): will use all manually provided genes

Value

giotto object with updated PCA dimension recuction

Examples

data(mini_giotto_single_cell)

# run PCA
mini_giotto_single_cell <- runPCA(gobject = mini_giotto_single_cell,
center = TRUE, scale_unit = TRUE)

# plot PCA results
plotPCA(mini_giotto_single_cell)