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",
...
)
giotto object
name of PCA object if available
expression values to use
cells or genes
which implementation to use
do a reverse PCA
subset of genes to use for PCA
center data before PCA
scale features before PCA
number of principal components to calculate
y-axis limits on scree plot
verobsity
show plot
return ggplot object
directly save the plot [boolean]
list of saving parameters from all_plots_save_function()
default save name for saving, don't change, change save_name in save_param
additional arguments to pca function, see runPCA
ggplot object for scree method
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
)
data(mini_giotto_single_cell)
screePlot(mini_giotto_single_cell, ncp = 10)