runPCA — runPCA • Giotto

runs a Principal Component Analysis

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

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)

Value

giotto object with updated PCA dimension recuction

Details

See prcomp_irlba and PCA for more information about other parameters.

genes_to_use = NULL: will use all genes from the selected matrix
genes_to_use = <hvg name>: can be used to select a column name of highly variable genes, created by (see calculateHVG)
genes_to_use = c('geneA', 'geneB', ...): will use all manually provided genes

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)