signPCA

identify significant prinicipal components (PCs)

signPCA(
  gobject,
  name = "pca",
  method = c("screeplot", "jackstraw"),
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "genes"),
  pca_method = c("irlba", "factominer"),
  rev = FALSE,
  genes_to_use = NULL,
  center = T,
  scale_unit = T,
  ncp = 50,
  scree_ylim = c(0, 10),
  jack_iter = 10,
  jack_threshold = 0.01,
  jack_ylim = c(0, 1),
  verbose = TRUE,
  show_plot = NA,
  return_plot = NA,
  save_plot = NA,
  save_param = list(),
  default_save_name = "signPCA"
)

Arguments

gobject

giotto object

name

name of PCA object if available

method

method to use to identify significant PCs

expression_values

expression values to use

reduction

cells or genes

pca_method

which implementation to use

rev

do a reverse PCA

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

scree_ylim

y-axis limits on scree plot

jack_iter

number of interations for jackstraw

jack_threshold

p-value threshold to call a PC significant

jack_ylim

y-axis limits on jackstraw plot

verbose

verbosity

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 scree method and maxtrix of p-values for jackstraw

Details

Two different methods can be used to assess the number of relevant or significant prinicipal components (PC's).
1. Screeplot works by plotting the explained variance of each individual PC in a barplot allowing you to identify which PC provides a significant contribution (a.k.a. 'elbow method').
2. The Jackstraw method uses the permutationPA function. By systematically permuting genes it identifies robust, and thus significant, PCs.