Perform transpose and scaling of matrix — prep

This function uses python to perform transformations of the input matrix

prep_matrix(
  mat,
  log2 = TRUE,
  transpose = FALSE,
  scale = TRUE,
  backend = c("r", "rtorch", "pytorch"),
  cores = 2,
  device = c("CPU", "GPU")
)

Arguments

mat: A numeric R matrix.
transpose: Boolean. Whether the matrix needs to be transposed. If starting with samples as columns, set to TRUE. Default is FALSE
scale: Boolean. Whether to center and scale the matrix. Default to TRUE
backend: Character. The backend which to use for performing transformations. Default is "rtorch". Options are "r", "rtorch", or "pytorch". See details for information about the pytorch/conda environments
cores: Numeric. The number of cores to use.

Value

A matrix that has been rotated (transposed) and scaled if needed

Details

Depending on the backend chosen, the session may need to be reset with rstudioapi::restartSession(). Mainly, this is due to conflicts between some underlying system level variables with 'rtorch' and 'pytorch'. Once one is used in a session, the other will fail. Even testing using 'rtorch' and then starting the conda environment resulted in the environment being loaded by the python libraries/modules are not available. Unless absolutely needed and for testing, would stick with 'rtorch'

Examples

if (FALSE) { # \dontrun{
  #need to have environment first
  reticulate::use_condaenv("FastPCA")

  # Create a sample R matrix (e.g., 20 samples, 100 features)
  # Ensure values are positive for log transform.
  set.seed(123)
  test_data <- matrix(runif(20 * 100, min=1, max=100), nrow = 20, ncol = 100)
  colnames(test_data) <- paste0("Feature", 1:ncol(test_data))
  rownames(test_data) <- paste0("Sample", 1:nrow(test_data))

  print(paste("Original R matrix dimensions:", paste(dim(test_data), collapse = "x")))

  # Perform Randomized SVD
  prepped_matrix <- FastPCA::prep_matrix(test_data, log2 = TRUE,
                                  transpose = FALSE, scale = TRUE)
} # }