Identify Significant PCs With A Screeplot

screePlot()

Identify significant prinicipal components (PCs) using a screeplot (a.k.a. elbow-plot).

screePlot(
    gobject,
    name = "pca",
    expression_values = c("normalized", "scaled", "custom"),
    reduction = c("cells", "genes"),
    method = c("irlba", "factominer"),
    rev = FALSE,
    genes_to_use = NULL,
    center = F,
    scale_unit = F,
    ncp = 100,
    ylim = c(0, 20),
    verbose = T,
    show_plot = NA,
    return_plot = NA,
    save_plot = NA,
    save_param = list(),
    default_save_name = "screePlot",
    ...
)

Arguments

gobject	giotto object
name	name of PCA object if available
expression_values	expression values to use
reduction	cells or genes
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
ylim	y-axis limits on scree plot
verbose	verobsity
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
…	additional arguments to pca function, see runPCA

Value

ggplot object for scree method.

Details

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’). Screeplot will use an available pca object, based on the parameter ‘name’, or it will create it if it’s not available (see runPCA()).

Examples

data(mini_giotto_single_cell)

screePlot(mini_giotto_single_cell, ncp = 10)
#> PCA with name:  pca  already exists and will be used for the screeplot