`screePlot`¶

screePlot

Description¶

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

Usage¶

screePlot(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  name = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "feats"),
  method = c("irlba", "exact", "random", "factominer"),
  rev = FALSE,
  feats_to_use = NULL,
  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
`spat_unit`	spatial unit
`feat_type`	feature type
`name`	name of PCA object if available
`expression_values`	expression values to use
`reduction`	cells or features
`method`	which implementation to use
`rev`	do a reverse PCA
`feats_to_use`	subset of features to use for PCA
`genes_to_use`	deprecated, use feats_to_use
`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