signPCA

Source: R/dimension_reduction.R

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"
)

Arguments

gobject

giotto object

feat_type

feature type (e.g. "rna", "dna", "protein")

spat_unit

spatial unit (e.g. "cell")

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

feats_to_use

subset of features 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

be verbose

show_plot

logical. show plot

return_plot

logical. return ggplot object

save_plot

logical. save the plot

save_param

list of saving parameters, see showSaveParameters

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.

Examples

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