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)#> hvg was found in the gene metadata information and will be used to select highly variable genes#> Warning: ncp >= minimum dimension of x, will be set to minimum dimension of x - 1#> Warning: You're computing too large a percentage of total singular values, use a standard svd instead.#> Warning: did not converge--results might be invalid!; try increasing work or maxit#> #> pca has already been used, will be overwritten
