diff --git a/R/calcPCBrainAge.R b/R/calcPCBrainAge.R
index 60581ae..0ff71a7 100644
--- a/R/calcPCBrainAge.R
+++ b/R/calcPCBrainAge.R
@@ -35,16 +35,23 @@ calcPCBrainAge <- function(DNAm, pheno = NULL, CpGImputation = NULL){
   ###################################################
   ### Check if all necessary CpGs are in the data ###
   ###################################################
+
+  # Debug: convert input data to data.frame if it's a matrix
+  if (is.matrix(DNAm)) {
+    DNAm <- as.data.frame(DNAm)
+    message("Input matrix is converted to a data frame.")
+  }
+
   missingCpGs <- rownames(PCBrainAge_Model$rotation)[!(rownames(PCBrainAge_Model$rotation) %in% colnames(DNAm))]
   DNAm[,missingCpGs] <- NA
   if(!is.na(missingCpGs[1])){           #if there are columns of CpGs missing...
     if(is.null(CpGImputation)){         #...and the user didn't specify a mean CpG vector, use the Hannum 2013 blood mean data
       data("imputeMissingBrainCpGs")
       for(i in 1:length(missingCpGs)){
-        datMeth[,missingCpGs[i]] <- imputeMissingBrainCpGs[missingCpGs[i]]
+        DNAm[,missingCpGs[i]] <- imputeMissingBrainCpGs[missingCpGs[i]]
       }} else{                          #...otherwise use the user's mean CpG vector which contains the required CpG values
         for(i in 1:length(missingCpGs)){
-          datMeth[,missingCpGs[i]] <- CpGImputation[missingCpGs[i]]
+          DNAm[,missingCpGs[i]] <- CpGImputation[missingCpGs[i]]
         }
       }
     }
@@ -54,7 +61,7 @@ calcPCBrainAge <- function(DNAm, pheno = NULL, CpGImputation = NULL){
   DNAm <- DNAm[,match(rownames(PCBrainAge_Model$rotation), colnames(DNAm))]
 
   if(any(is.na(DNAm))){
-    DNAm <- apply(DNAm,2,meanimpute)
+    DNAm <- apply(DNAm,2,meanImpute) # Debug: call the function meanImpute
     message("Mean imputation successfully completed for missing CpG values")
   }
 
@@ -73,13 +80,3 @@ calcPCBrainAge <- function(DNAm, pheno = NULL, CpGImputation = NULL){
     pheno
   }
 }
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