diff --git a/inst/scripts/crossvalidation_coRmiT.R b/inst/scripts/crossvalidation_coRmiT.R
index 8ffa464..ce2dbf0 100755
--- a/inst/scripts/crossvalidation_coRmiT.R
+++ b/inst/scripts/crossvalidation_coRmiT.R
@@ -2,20 +2,21 @@
 library(dplyr)
 
 filter_and_rank <- function(cv_cont_table){
-    # cv_cont_table <- cv_cont_table[cv_cont_table$Pvalue <= 0.05,]
+   # cv_cont_table <- cv_cont_table[cv_cont_table$Pvalue <= 0.05,]
     if (nrow(cv_cont_table) == 0 ) return(NULL)
 
     cv_cont_table <- cv_cont_table[,c("miRNA","strategy", "corr_cutoff", "crossval_status", "Odds_ratio", "TP")]
-    cv_cont_table <- reshape(cv_cont_table, idvar = c("miRNA","strategy", "corr_cutoff"), timevar = "crossval_status", direction = "wide")
-    if (sum(c("Odds_ratio.test", "Odds_ratio.train") %in% colnames(cv_cont_table)) != 2) return(NULL)
+     #cv_cont_table2 <- reshape(cv_cont_table, idvar = c("miRNA","strategy", "corr_cutoff"), timevar = "crossval_status", direction = "wide")
+    #if ("Odds_ratio.train" %in% colnames(cv_cont_table)) return(NULL)
     # cv_cont_table <- cv_cont_table  %>% arrange(desc(Odds_ratio.test), desc(TP.test), corr_cutoff)
     # cv_cont_table$OR_test_rank <- seq(1, nrow(cv_cont_table))    
     # str(cv_cont_table)
-    cv_cont_table$OR_test_rank <- rank(-cv_cont_table$Odds_ratio.test, ties.method ="min")
-    cv_cont_table$OR_train_rank <- rank(-cv_cont_table$Odds_ratio.train,ties.method ="min")
+  #  cv_cont_table$OR_test_rank <- rank(-cv_cont_table$Odds_ratio.test, ties.method ="min")
+    cv_cont_table$OR_train_rank <- rank(-cv_cont_table$Odds_ratio,ties.method ="min")
     return(cv_cont_table)
 
 }
+
 get_test_rank <- function(cv_cont_table){
     top_rank_train_val <- min(cv_cont_table$OR_train_rank)
     top_rank_train <- cv_cont_table[cv_cont_table$OR_train_rank ==top_rank_train_val,]
@@ -78,48 +79,49 @@ cormit_path <- file.path(opt$input_cormit, "temp.RData")
 load(cormit_path)
 
 p_val_cutoff <- 0.05
-all_pairs <- miRNA_cor_results$all_pairs
-miRNA_strat_result <- miRNA_cor_results$miRNA_cont_tables
-miRNA_strat_result <-  miRNA_strat_result[miRNA_strat_result$Pvalue <= 0.05 & miRNA_strat_result$db_group == "multimir",]
-best_miRNA_strategies <- as.data.frame(select_best_strategy(miRNA_strat_result))
-strat_combinations <- as.data.frame(unique(miRNA_strat_result[, c("corr_cutoff", "strategy")]))
-miRNA_cont_tables <- data.frame()
+# all_pairs <- miRNA_cor_results$all_pairs
+# miRNA_strat_result <- miRNA_cor_results$miRNA_cont_tables
+# miRNA_strat_result <-  miRNA_strat_result[miRNA_strat_result$Pvalue <= 0.05 & miRNA_strat_result$db_group == "multimir",]
+# best_miRNA_strategies <- as.data.frame(select_best_strategy(miRNA_strat_result))
+# strat_combinations <- as.data.frame(unique(miRNA_strat_result[, c("corr_cutoff", "strategy")]))
+# miRNA_cont_tables <- data.frame()
 
 
 
-for (combination in seq(1,nrow(strat_combinations))) {
-    corr_cutoff <- strat_combinations[combination, "corr_cutoff"]
-    strategy <- strat_combinations[combination, "strategy"]
+# for (combination in seq(1,nrow(strat_combinations))) {
+#     corr_cutoff <- strat_combinations[combination, "corr_cutoff"]
+#     strategy <- strat_combinations[combination, "strategy"]
 
-    contingency_tables <- prepare_for_stats(all_pairs, strategy, corr_cutoff,
-    db_groups = "multimir", p_val_cutoff = p_val_cutoff , crossval = TRUE, test_sample = opt$test_size)
-    miRNA_cont_tables <- rbind(miRNA_cont_tables, contingency_tables[["strat_miRNA_ct"]])
-}
+#     contingency_tables <- prepare_for_stats(all_pairs, strategy, corr_cutoff,
+#     db_groups = "multimir", p_val_cutoff = p_val_cutoff , crossval = TRUE, test_sample = opt$test_size)
+#     miRNA_cont_tables <- rbind(miRNA_cont_tables, contingency_tables[["strat_miRNA_ct"]])
+# }
 
 
-message("Stats prepared")
-miRNA_cont_tables <- v_get_stats(miRNA_cont_tables, 
-                              selected_stats = c("v.fisher.test","odds_ratio"))
+# message("Stats prepared")
+# miRNA_cont_tables <- v_get_stats(miRNA_cont_tables, 
+#                               selected_stats = c("v.fisher.test","odds_ratio"))
 
-miRNA_cont_tables$iterations <- paste(miRNA_cont_tables$miRNA, miRNA_cont_tables$crossval_it, sep = "_")
+# miRNA_cont_tables$iterations <- paste(miRNA_cont_tables$miRNA, miRNA_cont_tables$crossval_it, sep = "_")
 
-splitted_cont_tables <- split(miRNA_cont_tables, miRNA_cont_tables$iterations)
+# splitted_cont_tables <- split(miRNA_cont_tables, miRNA_cont_tables$iterations)
 
 
-cross_rank <- lapply(splitted_cont_tables, filter_and_rank)
-full_ranking <-as.data.frame(data.table::rbindlist(cross_rank, 
-                                     use.names = TRUE, 
-                                     idcol = "iters"))
-cross_rank <- lapply(cross_rank, get_test_rank)
+# cross_rank <- lapply(splitted_cont_tables, filter_and_rank)
+# full_ranking <-as.data.frame(data.table::rbindlist(cross_rank, 
+#                                      use.names = TRUE, 
+#                                      idcol = "iters"))
+### cross_rank <- lapply(cross_rank, get_test_rank)
 message("Train and test datasets ordered")
-cross_rank <-as.data.frame(data.table::rbindlist(cross_rank, 
-                                     use.names = TRUE, 
-                                     idcol = "iters"))
-colnames(cross_rank) <- c("iteration", "miRNA", "strategy","corr_cutoff","test_rank")
+#### cross_rank <-as.data.frame(data.table::rbindlist(cross_rank, 
+#                                      use.names = TRUE, 
+#                                      idcol = "iters"))
+#### colnames(cross_rank) <- c("iteration", "miRNA", "strategy","corr_cutoff","test_rank")
+#  save(full_ranking,best_miRNA_strategies, file = "test.Rdata")
+load("test.Rdata")
 best_miRNA_strategies$acc_ratio <- 0
 best_miRNA_strategies$total_strats <- 0
-# save(cross_rank,best_miRNA_strategies,full_ranking, file = "test.Rdata")
-# load("test.Rdata")
+
 train_rank_dist <- data.frame()
 for (miRNA in best_miRNA_strategies$miRNA) {
     best_miRNA_strat <- unlist(best_miRNA_strategies[best_miRNA_strategies$miRNA == miRNA,])
@@ -130,7 +132,8 @@ for (miRNA in best_miRNA_strategies$miRNA) {
     train_rank_dist <- rbind(train_rank_dist, best_rank_in_train)
 }
 
-str(full_ranking)
+# save(train_rank_dist, file = "test_stats.RData")
+# q()
 message("Computing report")
 dir.create(opt$output_files)
 outf <- file.path(normalizePath(opt$output_files),"coRmiT_cv.html")
diff --git a/inst/templates/cormit_cv.Rmd b/inst/templates/cormit_cv.Rmd
index 7d3b472..77eb8cc 100644
--- a/inst/templates/cormit_cv.Rmd
+++ b/inst/templates/cormit_cv.Rmd
@@ -33,8 +33,12 @@ output:
 
 ## Rank of the test dataset for the best strategies in train 
 
-```{r ranking_dist, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'} 
-mirna_names <- translate_miRNA_ids(cross_rank$miRNA)
+```{r config, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'}
+
+mirna_names <- translate_miRNA_ids(unique(full_ranking$miRNA))
+```
+
+```{r ranking_dist, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis', eval = FALSE} 
 
 cross_rank$significant <- ifelse(cross_rank$miRNA %in% best_miRNA_strategies$miRNA, "significant", "not significant")
 
@@ -57,11 +61,18 @@ Ranking values of the best strategy in corMiT but using random sections of datab
 
 train_rank_dist$miRNA_n <- mirna_names[match(train_rank_dist$miRNA, mirna_names$Accession), "TargetName"]   
 
+
+p_meds <- plyr::ddply(train_rank_dist, plyr::.(miRNA_n), summarise, med = median(OR_train_rank))
+
 ggplot2::ggplot(train_rank_dist, ggplot2::aes(x = miRNA_n, y = OR_train_rank)) + 
 ggplot2::geom_boxplot()+
+ggplot2::geom_text(data = p_meds, ggplot2::aes(x = miRNA_n, y = med, label = med), 
+              size = 3, vjust = -1.5)+
 
 
- ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 315,hjust=0,vjust=0)) +
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 60,hjust=1,vjust=1, size = 18),
+                 axis.text.y = ggplot2::element_text(size = 18), 
+                 axis.title.y = ggplot2::element_text(size = 15, face = "bold")) +
  ggplot2::labs(y= "Ranking position in 75% of DB", x = NULL)+
  ggplot2::scale_y_continuous(limits = c(0, 25))
 
@@ -83,23 +94,23 @@ plot_objects <- list()
 full_ranking <- full_ranking[full_ranking$miRNA %in% best_miRNA_strategies$miRNA,]
 full_ranking$miRNA_n <- mirna_names[match(full_ranking$miRNA, mirna_names$Accession), "TargetName"] 
 miRNA_cor_results$miRNA_cont_tables$miRNA_n  <- mirna_names[match(miRNA_cor_results$miRNA_cont_tables$miRNA, mirna_names$Accession), "TargetName"] 
-train_rank_dist <- data.frame()
+train_rank_dist2 <- data.frame()
 for (miRNA in unique(full_ranking$miRNA_n)) {
-    miRNA_ct <- miRNA_cor_results$miRNA_cont_tables[miRNA_cor_results$miRNA_cont_tables$miRNA_n == miRNA,]
+   miRNA_ct <- miRNA_cor_results$miRNA_cont_tables[miRNA_cor_results$miRNA_cont_tables$miRNA_n == miRNA,]
     miRNA_ct <- miRNA_ct[miRNA_ct$db_group == "multimir",]
-
     miRNA_ct$full_db_rank <- rank(-miRNA_ct$Odds_ratio, ties.method ="min")
+    
     miRNA_rank <- full_ranking[full_ranking$miRNA_n == miRNA,]
     full_and_sample_eq <- match(paste0(miRNA_rank$strategy, miRNA_rank$corr_cutoff),
                                 paste0(miRNA_ct$strategy, miRNA_ct$corr_cutoff))
     miRNA_rank$full_db_rank <- miRNA_ct$full_db_rank[full_and_sample_eq]
     miRNA_rank$Pvalue <- miRNA_ct$Pvalue[full_and_sample_eq]
+
     # miRNA_rank <- miRNA_rank[miRNA_rank$Pvalue <= 0.05,]
-    miRNA_rank$full_db_rank[is.na(miRNA_rank$full_db_rank)] <- max(miRNA_rank$full_db_rank[!is.na(miRNA_rank$full_db_rank)])
-    miRNA_rank$OR_train_rank[is.na(miRNA_rank$OR_train_rank)] <- max(miRNA_rank$OR_train_rank[!is.na(miRNA_rank$OR_train_rank)])
+    
+    top_miRNA_rank <- miRNA_rank[miRNA_rank$full_db_rank == 1 & !is.na(miRNA_rank$full_db_rank),]
    
-    top_miRNA_rank <- miRNA_rank[miRNA_rank$full_db_rank == 1,]
-    train_rank_dist <- rbind(train_rank_dist,top_miRNA_rank)
+    train_rank_dist2 <- rbind(train_rank_dist2,top_miRNA_rank)
     pp <- ggplot2::ggplot(miRNA_rank, ggplot2::aes(y = OR_train_rank, x = full_db_rank))+
     ggplot2::geom_jitter(size = 0.5,width = 0.2, height = 0.2, alpha = 0.3) + 
     ggplot2::geom_boxplot(data = top_miRNA_rank, mapping= ggplot2::aes(y = OR_train_rank, x = 0))+
@@ -110,9 +121,30 @@ for (miRNA in unique(full_ranking$miRNA_n)) {
     ggplot2::geom_vline(xintercept  = 1.5, linetype="dashed")
         plot_objects[[miRNA]] <- pp
 }
-
+# save(train_rank_dist, file = "/mnt/scratch/users/bio_267_uma/josecordoba/NGS_projects/LaforaRNAseq/analysis/target_wf_pearson/ctrl_vs_mut_deff/coRmiT.R_0000/test2.Rdata")
 gridExtra::grid.arrange(grobs = plot_objects, ncol = 3, nrow = ceiling(length(plot_objects)/3),
   left = "Ranking position in 75% of DB", bottom = "Ranking position in 100% of DB")
 ```
 
 
+## Rank of the best strategy 2
+
+Ranking values of the best strategy in corMiT but using random sections of databases
+
+```{r ranking_dist_train2, echo = FALSE, warning =FALSE, message=FALSE, results = 'asis'} 
+
+train_rank_dist2$miRNA_n <- mirna_names[match(train_rank_dist2$miRNA, mirna_names$Accession), "TargetName"]   
+
+ggplot2::ggplot(train_rank_dist2, ggplot2::aes(x = miRNA_n, y = OR_train_rank)) + 
+ggplot2::geom_boxplot()+
+
+
+ ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 60,hjust=1,vjust=1, size = 18),
+                 axis.text.y = ggplot2::element_text(size = 18), 
+                 axis.title.y = ggplot2::element_text(size = 15, face = "bold")) +
+ ggplot2::labs(y= "Ranking position in 75% of DB", x = NULL)+
+ ggplot2::scale_y_continuous(limits = c(0, 25))
+
+
+
+```
\ No newline at end of file