examples/one_constant_cross-correlated_block/low-dim/constant_ordinary_SCCA.R

In agisga/FDRcorrectedSCCA: FDR-Corrected Sparse Canonical Correlation Analysis

library(dplyr)
library(readr)
library(doParallel)

out_path <- "/lustre/project/wyp/agossman/FDRcorrectedSCCA/one_constant_cross-correlated_block/low-dim/"
out_path <- paste0(out_path, "constant_low_dimensional_ordinary_SCCA/")

cores <- as.integer(Sys.getenv("SLURM_CPUS_PER_TASK"))
doParallel::registerDoParallel(cores)

setwd("../../..")
devtools::load_all()

slurm_task_id  <- Sys.getenv("SLURM_ARRAY_TASK_ID") # run this as an array job 500/10=50 times
num_iter       <- 10
n_row          <- 3000
n_col_X        <- 50
n_col_Y        <- 50
n_col          <- n_col_X + n_col_Y
n_signif_X_vec <- c(1, 10, 20, 30)
n_signif_Y_vec <- c(1, 10, 20, 30)

# ensure a different random seed in each array run
rand_seed <- as.numeric(paste0(format(Sys.time(), "%M%S"), slurm_task_id))
set.seed(rand_seed)

cor_btwn   <- 0.4 # (cross-)correlation between *significant* features of X and Y
cor_wthn   <- 0.1 # correlation between any pair of features within either X or Y
lambda_vec <- c(0.01, 0.3) # tuning parameter for sparse CCA

simulation_results_df <- NULL

for (lambda in lambda_vec) {
  for (n_signif_X in n_signif_X_vec) {
    for (n_signif_Y in n_signif_Y_vec) {

      # correlation matrix of matrix [X | Y]
      Sigma <- matrix(0, n_col, n_col)
      # correlations within X
      Sigma[1:n_col_X, 1:n_col_X] <- cor_wthn
      # correlations within Y
      Sigma[(n_col_X+1):n_col, (n_col_X+1):n_col] <- cor_wthn
      # correlations between X and Y
      Sigma[1:n_signif_X, (n_col_X+1):(n_col_X+n_signif_Y)] <- cor_btwn
      Sigma[(n_col_X+1):(n_col_X+n_signif_Y), 1:n_signif_X] <- cor_btwn
      # adjustment for positive definiteness
      Sigma[1:n_signif_X, 1:n_signif_X] <- Sigma[1:n_signif_X, 1:n_signif_X] + cor_btwn
      Sigma[(n_col_X+1):(n_col_X+n_signif_Y), (n_col_X+1):(n_col_X+n_signif_Y)] <- Sigma[(n_col_X+1):(n_col_X+n_signif_Y), (n_col_X+1):(n_col_X+n_signif_Y)] + cor_btwn
      # unit variances
      diag(Sigma) <- 1

      # Cholesky factorization
      Sigma_chol <- chol(Sigma)

      simulation_results <- foreach (iter = 1:num_iter, .combine = "cbind") %dopar% {

        set.seed(rand_seed + iter)

        print(paste("Iteration", iter))

        # Generate the data
        XY <- simulate_MVN_data(n_row, n_col_X, n_col_Y, Sigma_chol)
        X <- XY$X
        Y <- XY$Y

        # Apply SCCA on X0 and Y0
        CCA0 <- PMA::CCA(x = X, z = Y,
                         typex = "standard", typez = "standard", K = 1,
                         penaltyx = lambda, penaltyz = lambda,
                         trace = FALSE, niter = 5000)
        u0 <- CCA0$u
        v0 <- CCA0$v

        # Identify true and false discoveries in v0
        Y_selected <- which(v0 != 0)
        X_selected <- which(u0 != 0)

        # Return the results
        c("TP_v" = length(which(Y_selected <= n_signif_Y)),
          "FP_v" = length(which(Y_selected > n_signif_Y)),
          "TP_u" = length(which(X_selected <= n_signif_X)),
          "FP_u" = length(which(X_selected > n_signif_X)),
          "lambda_X" = CCA0$penaltyx,
          "lambda_Y" = CCA0$penaltyz)
      }

      sim_df <- t(simulation_results) %>% tbl_df %>%
        mutate(FDP_v = FP_v / max((TP_v + FP_v), 1),
               FDP_u = FP_u / max((TP_u + FP_u), 1)) %>%
        mutate(n_row = as.integer(n_row),
               n_col_X = as.integer(n_col_X),
               n_col_Y = as.integer(n_col_Y),
               n_signif_X = as.integer(n_signif_X),
               n_signif_Y = as.integer(n_signif_Y),
               iter = 1:num_iter)

      if (is.null(simulation_results_df)) {
        simulation_results_df <- sim_df
      } else {
        simulation_results_df <- bind_rows(simulation_results_df, sim_df)
      }

      # save just in case
      write_csv(simulation_results_df,
                path = paste0(out_path,
                              "constant_low_dimensional_ordinary_SCCA_",
                              slurm_task_id, ".csv"))
    }
  }
}

save(list = ls(),
     file = paste0(out_path,
                   "constant_low_dimensional_ordinary_SCCA_",
                   slurm_task_id,
                   ".RData"))

print("DONE!")