diff --git a/R/simulation.R b/R/simulation.R
index 9bf7d01e1f643c9c3676df3b0f904ade90bea64a..d507012664d1c9fdbaddbede8f9579542586ccbb 100644
--- a/R/simulation.R
+++ b/R/simulation.R
@@ -79,13 +79,15 @@ get_effects_from_rnorm <- function(list_var, metadata){
   
   variable_2rnorm <- names(list_stdev)
   l_effects <- lapply(stats::setNames(variable_2rnorm, variable_2rnorm) , function(var){
-    col_labels <- paste("label", var, sep = "_")
-    list_combinations <- paste(metadata[["geneID"]], metadata[[col_labels]])
+    col_labels <- paste("label", unlist(strsplit(var, ":")), sep = "_")
+    cols2paste <- c("geneID", col_labels)
+    list_combinations <- apply( metadata[ , cols2paste ] , 1 , paste , collapse = "-" )
     list_effects <- unique(list_combinations)
     list_beta <- rnorm(length(list_effects), mean = list_mu[var], sd = list_stdev[var])
     names(list_beta) <- list_effects
     unname(list_beta[list_combinations])
   })
+       
   
   df_effects <- do.call("cbind", l_effects)
   return(df_effects)
diff --git a/dev/flat_full.Rmd b/dev/flat_full.Rmd
index d070e85983cc738b84845b9e047f1791c29bcc56..70b35a89607213acc89a0f6c300434df357f4656 100644
--- a/dev/flat_full.Rmd
+++ b/dev/flat_full.Rmd
@@ -79,7 +79,7 @@ first_non_null_index <- function(lst) {
 #' @return A logical vector indicating rows below the threshold
 #' @export
 detect_row_matx_bellow_threshold <- function(matrix, threshold) {
-             apply(matrix, 1, function(row) all(row < threshold))
+    apply(matrix, 1, function(row) all(row < threshold))
 }
 
 
@@ -1596,13 +1596,15 @@ get_effects_from_rnorm <- function(list_var, metadata){
   
   variable_2rnorm <- names(list_stdev)
   l_effects <- lapply(stats::setNames(variable_2rnorm, variable_2rnorm) , function(var){
-    col_labels <- paste("label", var, sep = "_")
-    list_combinations <- paste(metadata[["geneID"]], metadata[[col_labels]])
+    col_labels <- paste("label", unlist(strsplit(var, ":")), sep = "_")
+    cols2paste <- c("geneID", col_labels)
+    list_combinations <- apply( metadata[ , cols2paste ] , 1 , paste , collapse = "-" )
     list_effects <- unique(list_combinations)
     list_beta <- rnorm(length(list_effects), mean = list_mu[var], sd = list_stdev[var])
     names(list_beta) <- list_effects
     unname(list_beta[list_combinations])
   })
+       
   
   df_effects <- do.call("cbind", l_effects)
   return(df_effects)
@@ -2268,7 +2270,7 @@ get_messages_sequencing_depth <- function(scaling_factors, threshold_cov_var = 1
 warning_too_low_mu_ij_row <- function(mu_ij_matrix, threshold = 1 ){
   n_too_low_row <- length(which(detect_row_matx_bellow_threshold(mu_ij_matrix, threshold)))
   if (n_too_low_row > 0){
-    msg <- paste("INFO:", n_too_low_row, "genes have all(mu_ij) < 1, indicating very low counts. Consider removing them for future analysis using prepareData2fit with row_cnt_threshold = 10. To detect them, try increasing sequencing depth.\n", 
+    msg <- paste("INFO:", n_too_low_row, "genes have all(mu_ij) < 1, indicating very low counts. Consider removing them for future analysis using prepareData2fit with row_threshold = 10. To detect them in future experiment, try increasing sequencing depth.\n", 
                  sep = " ")
     message(msg)
   }
@@ -2694,7 +2696,7 @@ prepareData2fit <- function(countMatrix, metadata, response_name = "kij",
   if (row_threshold > 0){          
       message(paste("INFO: filtering", response_name, "<", row_threshold, sep = " " ))
       idx <- detect_row_matx_bellow_threshold(countMatrix, threshold = row_threshold)
-      message(paste(length(idx), "genes removed from data.", sep = " "))
+      message(paste(length(which(idx)), "genes removed from data.", sep = " "))
       countMatrix <- countMatrix[ !idx , ]
   }