jackstrawPlot

Source: R/dimension_reduction.R

Identify significant principal components (PCs)

jackstrawPlot(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  reduction = c("cells", "feats"),
  feats_to_use = "hvf",
  center = TRUE,
  scale_unit = TRUE,
  ncp = 20,
  ylim = c(0, 1),
  iter = 10,
  threshold = 0.01,
  random_subset = NULL,
  set_seed = TRUE,
  seed_number = 1234,
  verbose = TRUE,
  show_plot = NULL,
  return_plot = NULL,
  save_plot = NULL,
  save_param = list(),
  default_save_name = "jackstrawPlot"
)

Arguments

gobject

giotto object

spat_unit

spatial unit (e.g. "cell")

feat_type

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

expression_values

expression values to use

reduction

cells or genes

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

ylim

y-axis limits on jackstraw plot

iter

number of iterations for jackstraw

threshold

p-value threshold to call a PC significant

random_subset

randomized subset of matrix to use to approximate but speed up calculation

set_seed

logical. whether to set a seed when random_subset is used

seed_number

seed number to use when random_subset is used

verbose

show progress of jackstraw method

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

if return_plot = TRUE: ggplot object for jackstraw method if return_plot = FALSE: silently returns number of significant PCs

Details

The Jackstraw method uses the permutationPA function. By systematically permuting genes it identifies robust, and thus significant, PCs. This implementation makes small modifications to SVD calculation for improved efficiency and flexibility with different matrix types.
This implementation supports both dense and sparse input matrices.

steps

  1. Select singular values to calculate based on matrix dims and ncp

  2. Find SVD to get variance explained of each PC

  3. Randomly sample across features then re-calculate randomized variance

  4. Determine P-value by comparing actual vs randomized explained variance, indicating the significance of each PC

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
#> python already initialized in this session
#>  active environment : '/usr/bin/python3'
#>  python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"

jackstrawPlot(gobject = g)
#> using 'jackstraw' to identify significant PCs If used in
#>   published research, please cite:
#>   Neo Christopher Chung and John D. Storey (2014).
#>   'Statistical significance of variables driving systematic variation in
#>   high-dimensional data. Bioinformatics
#> "hvf" column was found in the feats metadata information and will be
#>  used to select highly variable features
#> Estimating number of significant principal components:
#> 
#> number of estimated significant components: 7