Identify Significant PCs

jackstrawPlot()

Identify significant principal 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 interactions 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

Important

When 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

# \donttest{

data(mini_giotto_single_cell)

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

#> Estimating a number of significant principal component: #> 1  2  3  4  5  6  7  8  9  10  number of estimated significant components:  0
# }

ADD IMAGE