View source: R/dimension_reduction.R
runPCA | R Documentation |
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,
...
)
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) |
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
giotto object with updated PCA dimension recuction
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)
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