View source: R/dimension_reduction.R
jackstrawPlot | R Documentation |
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"
)
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 |
The Jackstraw method uses the permutationPA
function. By
systematically permuting genes it identifies robust, and thus significant, PCs.
ggplot object for jackstraw method
data(mini_giotto_single_cell)
# jackstraw package is required to run
jackstrawPlot(mini_giotto_single_cell, ncp = 10)
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