Identify Significant PCs¶

signPCA()

Identify significant principal components (PCs).

signPCA(
    gobject,
    name = "pca",
    method = c("screeplot", "jackstraw"),
     expression_values = c("normalized", "scaled", "custom"),
    reduction = c("cells", "genes"),
    pca_method = c("irlba", "factominer"),
    rev = FALSE,
    genes_to_use = NULL,
    center = T,
    scale_unit = T,
    ncp = 50,
    scree_ylim = c(0, 10),
    jack_iter = 10,
    jack_threshold = 0.01,
    jack_ylim = c(0, 1),
    verbose = TRUE,
    show_plot = NA,
    return_plot = NA,
    save_plot = NA,
    save_param = list(),
    default_save_name = "signPCA"
)

Arguments¶

gobject	giotto object
name	name of PCA object if available
method	method to use to identify significant PCs
expression_values	expression values to use
reduction	cells or genes
pca_method	which implementation to use
rev	do a reverse PCA
genes_to_use	subset of genes to use for PCA
center	center data before PCA
scale_unit	scale features before PCA
ncp	number of principal components to calculate
scree_ylim	y-axis limits on scree plot
jack_iter	number of interations for jackstraw
jack_threshold	p-value threshold to call a PC significant
jack_ylim	y-axis limits on jackstraw plot
verbose	verbosity
show_plot	show plot
return_plot	return ggplot object
save_plot	directly save the plot [boolean]
save_param	list of saving parameters from all_plots_save_function()
default_save_name	default save name for saving, don’t change, change save_name in save_param

Value¶

ggplot object for scree method and maxtrix of p-values for jackstraw.

Details¶

Two different methods can be used to assess the number of relevant or significant prinicipal components (PC’s).

Screeplot works by plotting the explained variance of each individual PC in a barplot allowing you to identify which PC provides a significant contribution (a.k.a. ‘elbow method’).
The Jackstraw method uses the permutationPA() function. By systematically permuting genes it identifies robust, and thus significant, PCs.