signPCA
¶
signPCA
Description¶
identify significant prinicipal components (PCs)
Usage¶
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¶
Argument |
Description |
---|---|
|
giotto object |
|
name of PCA object if available |
|
method to use to identify significant PCs |
|
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 |
|
number of interations for jackstraw |
|
p-value threshold to call a PC significant |
|
y-axis limits on jackstraw plot |
|
verbosity |
|
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 |
Details¶
- Two different methods can be used to assess the number of relevant or significant
prinicipal components (PC’s). list()
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’). list()
The Jackstraw method uses the ``permutationPA` <#permutationpa>`_ function. By systematically permuting genes it identifies robust, and thus significant, PCs. list()
Value¶
ggplot object for scree method and maxtrix of p-values for jackstraw