screePlot

screePlot

Description

identify significant prinicipal components (PCs) using an screeplot (a.k.a. elbowplot)

Usage

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

Argument	Description
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` <#runpca>`_

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’). list() Screeplot will use an available pca object, based on the parameter ‘name’, or it will create it if it’s not available (see ``runPCA` <#runpca>`_ )

Value

ggplot object for scree method

Examples

data(mini_giotto_single_cell)

screePlot(mini_giotto_single_cell, ncp = 10)