screePlot: screePlot

In RubD/Giotto: Spatial Single-Cell Transcriptomics Toolbox

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

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

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)

Value

ggplot object for scree method

Examples

data(mini_giotto_single_cell)

screePlot(mini_giotto_single_cell, ncp = 10)

RubD/Giotto documentation built on April 29, 2023, 5:52 p.m.