identify significant prinicipal components (PCs)
signPCA(
gobject,
feat_type = NULL,
spat_unit = NULL,
name = NULL,
method = c("screeplot", "jackstraw"),
expression_values = c("normalized", "scaled", "custom"),
reduction = c("cells", "feats"),
pca_method = c("irlba", "factominer"),
rev = FALSE,
feats_to_use = NULL,
center = TRUE,
scale_unit = TRUE,
ncp = 50,
scree_ylim = c(0, 10),
jack_iter = 10,
jack_threshold = 0.01,
jack_ylim = c(0, 1),
verbose = TRUE,
show_plot = NULL,
return_plot = NULL,
save_plot = NULL,
save_param = list(),
default_save_name = "signPCA"
)
giotto object
feature type (e.g. "rna", "dna", "protein")
spatial unit (e.g. "cell")
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 features 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
be verbose
logical. show plot
logical. return ggplot object
logical. save the plot
list of saving parameters, see showSaveParameters
default save name for saving, don't change, change save_name in save_param
ggplot object for scree method and maxtrix of p-values for jackstraw
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.
g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#>
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/bin/python3"
signPCA(g)
#> PCA with name: pca already exists and will be used for the screeplot