identify significant prinicipal components (PCs) using an screeplot (a.k.a. elbowplot)

screePlot(
  gobject,
  name = "pca",
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "genes"),
  method = c("irlba", "factominer"),
  rev = FALSE,
  genes_to_use = NULL,
  center = F,
  scale_unit = F,
  ncp = 100,
  ylim = c(0, 20),
  verbose = T,
  show_plot = NA,
  return_plot = NA,
  save_plot = NA,
  save_param = list(),
  default_save_name = "screePlot",
  ...
)

Arguments

gobject

giotto object

name

name of PCA object if available

expression_values

expression values to use

reduction

cells or genes

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

ylim

y-axis limits on scree plot

verbose

verobsity

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

...

additional arguments to pca function, see runPCA

Value

ggplot object for scree method

Details

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').
Screeplot will use an available pca object, based on the parameter 'name', or it will create it if it's not available (see runPCA)

Examples

data(mini_giotto_single_cell)

screePlot(mini_giotto_single_cell, ncp = 10)