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)
#> using 'jackstraw' to identify significant PCs If used in published research, please cite:
#>   Neo Christopher Chung and John D. Storey (2014).
#>   'Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics
#> Estimating a number of significant principal component: 
#> 1  2  3  4  5  6  7  8  9  10  number of estimated significant components:  0 

# }