`jackstrawPlot`¶

jackstrawPlot

Description¶

identify significant prinicipal components (PCs)

Usage¶

jackstrawPlot(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "feats"),
  feats_to_use = NULL,
  genes_to_use = NULL,
  center = FALSE,
  scale_unit = FALSE,
  ncp = 20,
  ylim = c(0, 1),
  iter = 10,
  threshold = 0.01,
  verbose = TRUE,
  show_plot = NA,
  return_plot = NA,
  save_plot = NA,
  save_param = list(),
  default_save_name = "jackstrawPlot"
)

Arguments¶

Argument	Description
`gobject`	giotto object
`spat_unit`	spatial unit
`feat_type`	feature type
`expression_values`	expression values to use
`reduction`	cells or genes
`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 jackstraw plot
`iter`	number of interations for jackstraw
`threshold`	p-value threshold to call a PC significant
`verbose`	show progress of jackstraw method
`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

Details¶

The Jackstraw method uses the ``permutationPA` <#permutationpa>`_ function. By

systematically permuting genes it identifies robust, and thus significant, PCs.: list()

Value¶

ggplot object for jackstraw method