diff --git a/R/PCA_Plot.R b/R/PCA_Plot.R
index 6da35a5..df98667 100644
--- a/R/PCA_Plot.R
+++ b/R/PCA_Plot.R
@@ -2,6 +2,8 @@
 #'
 #' @param D                \strong{data.frame} \cr
 #'                         The data set containing intensities of the sample.
+#' @param id                \strong{data.frame} \cr
+#'                         The corresponding ID columns for the parameter D e.g. containing further columns like protein or gene names
 #' @param groupvar1        \strong{character vector} \cr
 #'                         The variable used for colors.
 #' @param groupvar2        \strong{character vector} \cr
@@ -49,6 +51,7 @@
 #'
 
 PCA_Plot <- function(D,
+                     id = NULL,
                      groupvar1 = NULL, groupvar2 = NULL,
 
                      impute = FALSE, impute_method = "mean", propNA = 0,
@@ -67,7 +70,9 @@ PCA_Plot <- function(D,
 
   mess = ""
 
-  filtered_D <- filter_PCA_data(D = D, impute = impute, impute_method = impute_method, propNA = propNA)
+  filtered_data <- filter_PCA_data(D = D, id = id, impute = impute, impute_method = impute_method, propNA = propNA)
+  filtered_D <- filtered_data$D
+  filtered_id <- filtered_data$id
 
   if(is.null(filtered_D)){
     mess <- paste0(mess, "All rows were filtered out. \n")
@@ -142,7 +147,12 @@ PCA_Plot <- function(D,
 
   message(mess)
 
-  return(list("plot" = pl, "D_PCA_plot" = cbind(D_PCA, "Sample" = colnames(D)),
-              "pca" = pca, "message" = mess, "filtered_D" = filtered_D, "loadings" = pca$rotation))
+  Loadings <- as.data.frame(pca$rotation)
+  if (!is.null(filtered_id)) {
+    Loadings <- cbind(filtered_id, Loadings)
+  }
+
+  return(list("D_PCA_plot" = cbind(D_PCA, "Sample" = colnames(D)),
+              "pca" = pca, "message" = mess, "filtered_D" = filtered_D, "loadings" = Loadings, "plot" = pl))
 }
 
diff --git a/R/PCA_helper.R b/R/PCA_helper.R
index 37516c8..1c7e27c 100644
--- a/R/PCA_helper.R
+++ b/R/PCA_helper.R
@@ -3,6 +3,8 @@
 #'
 #' @param D                \strong{data.frame} \cr
 #'                         The data set containing intensities of the sample.
+#' @param id               \strong{data.frame} \cr
+#'                         The corresponding ID columns for the parameter D e.g. containing further columns like protein or gene names
 #' @param impute           \strong{logical} \cr
 #'                         If \code{TRUE}, missing values will be imputed.
 #' @param impute_method    \strong{character} \cr
@@ -17,13 +19,17 @@
 #'
 #'
 
-filter_PCA_data <- function(D, impute = FALSE, impute_method = "mean", propNA = 0){
+filter_PCA_data <- function(D, id = NULL, impute = FALSE, impute_method = "mean", propNA = 0){
 
   # proportion if missing values per protein
   mean_NA <- apply(D, 1, function(x) mean(is.na(x)))
 
   ### remove rows with too many missing values
-  D <- D[mean_NA <= propNA, ]
+  index_to_keep <- mean_NA <= propNA
+  D <- D[index_to_keep, ]
+  if (!is.null(id)) {
+    id <- id[index_to_keep, ]
+  }
 
   if (nrow(D) == 0){
     return(NULL)
@@ -52,7 +58,7 @@ filter_PCA_data <- function(D, impute = FALSE, impute_method = "mean", propNA =
   ind_zeroVar <- which(v < 1e-25)
   if (length(ind_zeroVar) > 0) D <- D[-ind_zeroVar,]
 
-  return(D)
+  return(list(D = D, id = id))
 }