Support sparse matrix mode

R/mfairInitialization.R

@@ -20,16 +20,33 @@ createMFAIR <- function(Y, X, Y_center = TRUE, K_max = 1L, project = "MFAIR") {
  stop("The number of samples in Y and X should be consistent!")
  } # End
 
+ if(is.matrix(Y)){
+ Y_sparse <- FALSE
+ }else{
+ Y_sparse <- TRUE
+ }
+
  # Center the matrix Y
  if (Y_center) {
- Y_mean <- mean(Y, na.rm = TRUE)
- Y <- Y - Y_mean
+ if(!Y_sparse){
+ Y_mean <- mean(Y, na.rm = TRUE)
+ Y <- Y - Y_mean
+ }
+else{
+ Y_mean <- mean(summary(Y)$x)
+ Y@x <- Y@x - Y_mean
+}
  } else {
  Y_mean <- 0
  }
 
  # Check Y's sparsity
- n_missing <- sum(is.na(Y))
+ if(!Y_sparse){
+ n_missing <- sum(is.na(Y))
+ }
+ else{
+ n_missing <- length(Y) - length(Y@x)
+ }
  if (n_missing >= 1) {
  if (n_missing == length(Y)) {
  stop("The main data matrix Y has no observed values!")
@@ -52,6 +69,7 @@ createMFAIR <- function(Y, X, Y_center = TRUE, K_max = 1L, project = "MFAIR") {
  Class = "MFAIR",
  Y = Y,
  X = as.data.frame(X),
+ Y_sparse = Y_sparse,
  Y_center = Y_center,
  Y_mean = Y_mean,
  Y_missing = Y_missing,

R/mfairObject.R

@@ -1,8 +1,9 @@
 #' Each MFAIR object has a number of slots which store information. Key slots to access are listed below.
 #'
 #'
-#' @slot Y A matrix. The main data matrix of N samples and M features.
+#' @slot Y A matrix or Matrix::dgCMatrix. The main data matrix of N samples and M features.
 #' @slot X A data.frame. The auxiliary information data frame of N samples and C covariates.
+#' @slot Y_sparse Logical. Whether the main data matrix Y is stored in the sparse mode.
 #' @slot Y_center Logical. Whether the main data matrix Y is centered.
 #' @slot Y_mean Numeric. Mean of the main data matrix Y if centered. Zero if not.
 #' @slot Y_missing Logical. Whether the main data matrix Y is partially observed.
@@ -34,8 +35,9 @@ setClass(
 
  # Define the slots
  slots = c(
- Y = "matrix",
+ Y = "matrixORdgCMatrix",
  X = "data.frame",
+ Y_sparse = "logical",
  Y_center = "logical",
  Y_mean = "numeric",
  Y_missing = "logical",