jackstrawPlot#

Last Updated: Jan 29, 2024

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#

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 function. By systematically permuting genes it identifies robust, and thus significant, PCs.

Value#

ggplot object for jackstraw method