jackstrawPlot

identify significant prinicipal components (PCs)

jackstrawPlot(
  gobject,
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "genes"),
  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

expression_values

expression values to use

reduction

cells or genes

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 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

Value

ggplot object for jackstraw method

Details

The Jackstraw method uses the permutationPA function. By systematically permuting genes it identifies robust, and thus significant, PCs.

Examples

# \donttest{

data(mini_giotto_single_cell)

# jackstraw package is required to run
jackstrawPlot(mini_giotto_single_cell, ncp = 10)

# }