Skip to contents

runSkrCCA

Source: R/14_run_skrCCA.R
runSkrCCA.Rd

runSkrCCA

Usage

runSkrCCA(
  object,
  scalePCs = TRUE,
  nCC = 2,
  tol = 1e-05,
  transferred_weight_1 = NULL,
  maxIter = 200,
  sigmaChoice = NULL,
  n_cores = 1,
  step_size = 1
)

# S4 method for class 'CoPro'
runSkrCCA(
  object,
  scalePCs = TRUE,
  nCC = 2,
  tol = 1e-05,
  transferred_weight_1 = NULL,
  maxIter = 200,
  sigmaChoice = NULL,
  n_cores = 1,
  step_size = 1
)

# S4 method for class 'CoProMulti'
runSkrCCA(
  object,
  scalePCs = TRUE,
  nCC = 2,
  tol = 1e-05,
  transferred_weight_1 = NULL,
  maxIter = 200,
  sigmaChoice = NULL,
  n_cores = 1,
  step_size = 1
)

Arguments

object

A CoPro object

scalePCs

Whether to scale each PCs to a uniform variance before running the program

nCC

Number of canonical vectors to compute, default = 2

tol

Tolerance for termination, default = 1e-5

transferred_weight_1

If we use cross-slide weight transfer function, the transferred weight on each PC. Otherwise, the value should be set to NULL.

maxIter

Maximum iterations

sigmaChoice

Specific sigma value to use (CoProMulti only, ignored for CoPro)

n_cores

Number of cores for parallel processing (CoProMulti only, ignored for CoPro)

step_size

Step size for damped power iteration. Default 1 (standard power iteration). Values in (0,1) blend old and new weights for smoother convergence, which can help with many cells or many CCs.

Value

CoPro object with skrCCA results computed

See also

computePCA(), computeKernelMatrix(), computeNormalizedCorrelation(), computeGeneAndCellScores()

Other spatial-pipeline: computeDistance(), computeKernelMatrix(), computePCA(), runGeneSpaceCCA()

Examples

# \donttest{
toy <- readRDS(system.file("extdata", "toy_copro_data.rds", package = "CoPro"))
obj <- newCoProSingle(
  normalizedData = toy$normalizedData,
  locationData   = toy$locationData,
  metaData       = toy$metaData,
  cellTypes      = toy$cellTypes
)
obj <- subsetData(obj, cellTypesOfInterest = unique(toy$cellTypes))
obj <- computePCA(obj, nPCA = 10)
#> Input is dense (matrixarray), performing irlba pca...
#> Input is dense (matrixarray), performing irlba pca...
obj <- computeDistance(obj, distType = "Euclidean2D", verbose = FALSE)
obj <- computeKernelMatrix(obj, sigmaValues = c(0.05, 0.1), verbose = FALSE)
obj <- runSkrCCA(obj, scalePCs = TRUE, nCC = 2)
#> Running skrCCA [1/2] for sigma = 0.05 ...
#> [1] "Convergence reached at 4 iterations (Max diff = 7.744e-06 )"
#> [1] "Convergence reached at 0 iterations (Max diff = 1.028e-14 )"
#> Running skrCCA [2/2] for sigma = 0.1 ...
#> [1] "Convergence reached at 4 iterations (Max diff = 4.003e-07 )"
#> [1] "Convergence reached at 0 iterations (Max diff = 2.331e-15 )"
#> skrCCA finished 2 sigma value(s) in 0.0 s.
#> Optimization succeeded for 2 sigma value(s): sigma_0.05, sigma_0.1
# }