diff --git a/src/06_compute_tdd_index.R b/src/06_compute_tdd_index.R
index 2f9a82adfe5157a9576bddeccdb9895c857a899d..622ee1a3da6492ff30919cfac833e30818ad3886 100644
--- a/src/06_compute_tdd_index.R
+++ b/src/06_compute_tdd_index.R
@@ -513,14 +513,16 @@ prepare_df_for_cor <- function(mean_gene_count = 10, ercc = F,
     tdd_full[tdd_full$gene %in% cc_peaks, "peak"] <- "UP_CC_peaks"
     tdd_full$sig <- rep("", times = nrow(tdd_full))
     tdd_full[tdd_full$p_value <= 0.05, "sig"] <- "_sig"
-    tdd_full <- tdd_full %>% mutate(kind = paste0(peak, sig),
-    gkind = paste0(group, sig)) # nolint
+    tdd_full <- tdd_full %>% mutate(
+        kind = paste0(peak, sig),
+        gkind = paste0(group, sig)
+    ) # nolint
     tdd_full <- tdd_full %>%
         arrange(kind)
     write_tsv(tdd_full, paste0(output_folder, "/TDD_correlation_table.txt")) # nolint
     mean_tdd <- tdd_full %>%
         select(-time_point) %>%
-        group_by(gene, group, peak, sig, kind) %>%
+        group_by(gene, group, peak, sig, kind, gkind) %>%
         summarize_all(mean) %>%
         ungroup()
     write_tsv(mean_tdd, paste0(output_folder, "/TDD_correlation_table_avg.txt")) # nolint
@@ -555,12 +557,12 @@ prepare_df_for_cor <- function(mean_gene_count = 10, ercc = F,
 filter_on_tp <- function(tdd_table, tp = 5) {
     if (tp == "3_5") {
         tmp3 <- tdd_table %>% filter(time_point == 3) %>% # nolint
-            dplyr::select(gene, mean_DMSO_TDD, mean_BRAF_TDD, group, kind, peak)
+            dplyr::select(gene, mean_DMSO_TDD, mean_BRAF_TDD, group, kind, peak, gkind)
         tmp5 <- tdd_table %>% filter(time_point == 5) %>% # nolint
-            dplyr::select(gene, mean_DMSO_TDD, mean_BRAF_TDD, group, kind, peak)
+            dplyr::select(gene, mean_DMSO_TDD, mean_BRAF_TDD, group, kind, peak, gkind)
         tdd_full <- left_join(
             tmp3, tmp5,
-            by = c("gene", "group", "kind", "peak"),
+            by = c("gene", "group", "kind", "peak", "gkind"),
             suffix = c("_3", "_5")
         ) %>%
             rowwise() %>%
@@ -583,69 +585,43 @@ filter_on_tp <- function(tdd_table, tp = 5) {
 produce_tdd_cor_figure <- function(mean_gene_count = 10, ercc = F,
                                    output_folder = "./results/TDD_analysis/correlation") {
     tdd_complete <- prepare_df_for_cor(mean_gene_count, ercc, output_folder)
-     for (tp in c(3, 5, "3_5")) {
-        tdd_full <- filter_on_tp(tdd_complete, tp)
-        downv <- get_cor_value(tdd_full, my_group = "BRAF_DOWN", my_peak = "DOWN_TC_peaks")
-        upv <- get_cor_value(tdd_full, my_group = "BRAF_UP", my_peak = "UP_CC_peaks")
-        p_down <- ggplot(tdd_full %>%
-            filter(peak == "DOWN_TC_peaks"),
-        mapping = aes(x = mean_DMSO_TDD, y = mean_BRAF_TDD, color = kind)
-        ) +
-            geom_point(aes(fill = kind), colour = "white", pch = 21, size = 5) +
-            scale_fill_manual(values = c("grey", "black")) +
-            coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) +
-            # geom_abline(
-            #     intercept = downv$intercept, slope = downv$slope,
-            #     color = "#4169e1"
-            # ) +
-                        theme_classic() +
-            theme(
-                text = element_text(size = 26),
-                panel.grid.major.x = element_line(),
-                panel.grid.major.y = element_line(),
-            ) +
-            geom_abline(slope = 1, color = "black") +
-            ggtitle(paste0(
-                "TDD of genes down-regulated w/ TC peaks by BRAF",
-                "\n(R gene BRAF_down = ", downv$cor, ")"
-            ))
-        p_up <- ggplot(tdd_full %>% filter(peak == "UP_CC_peaks"),
-            mapping = aes(
-                x = mean_DMSO_TDD, y = mean_BRAF_TDD,
-                color = kind
-            )
-        ) +
-            geom_point(aes(fill = kind), colour = "white", pch = 21, size = 5) +
+    tdd_full <- filter_on_tp(tdd_complete, "3_5")
+    tmp_df <- tibble(
+        col = c("group", "group", "peak", "peak"),
+        names = c(
+            "BRAF_DOWN", "BRAF_UP",
+            "DOWN_TC_peaks", "UP_CC_peaks"
+        ),
+        color_col = c("gkind", "gkind", "kind", "kind")
+    )
+    for (i in seq_len(nrow(tmp_df))) {
+        cnames <- tmp_df[i, ]$names
+        ccol <- tmp_df[i, ]$color_col
+        tdd_tmp <- tdd_full %>%
+            filter(!!as.symbol(tmp_df[i, ]$col) == cnames)
+        p <- ggplot(tdd_tmp, mapping = aes_string(
+            x = "mean_DMSO_TDD",
+            y = "mean_BRAF_TDD", color = ccol
+        )) +
+            geom_point(mapping = aes_string(fill = ccol), colour = "white", pch = 21, size = 5) +
             scale_fill_manual(values = c("grey", "black")) +
             coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) +
-                        theme_classic() +
+            theme_classic() +
             theme(
                 text = element_text(size = 26),
                 panel.grid.major.x = element_line(),
                 panel.grid.major.y = element_line(),
             ) +
-            # geom_abline(
-            #     intercept = upv$intercept,
-            #     slope = upv$slope, color = "#CD5C5C"
-            # ) +
             geom_abline(slope = 1, color = "black") +
-            ggtitle(paste0(
-                "TDD of genes up-regulated by BRAF w/ CC peaks",
-                "\n(R gene BRAF_up = ", upv$cor, ")"
-            ))
-        pdf(paste0(output_folder,
-            "/TDD_correlation_figures_downtc_", tp, "h.pdf"),
-            width = 15,
-            height = 10
-        )
-        print(p_down)
-        dev.off()
-        pdf(paste0(output_folder,
-            "/TDD_correlation_figures_upcc_", tp, "h.pdf"),
-            width = 15,
-            height = 10
+            ggtitle(paste("TDD of", cnames, "genes"))
+        pdf(paste0(
+            output_folder,
+            "/TDD_correlation_figures_", cnames, "_3_5_h.pdf"
+        ),
+        width = 15,
+        height = 10
         )
-        print(p_up)
+        print(p)
         dev.off()
     }
 }
@@ -667,13 +643,17 @@ glm_nb_pvalue <- function(tab, pic_col,
     ptype_name <- tab$ptype[1]
     output_fig <- paste0(output_diag, "/glm_nb_", pic_col, "_", ptype_name, ".pdf")
     if (pic_col == "peak") {
-        mod <- glmmTMB(peak ~ group, ziformula = ~1, data = tab,
-                       family="nbinom2") # nolint
-        pval <- sprintf(summary(mod)$coeff$cond[2, 4], fmt = '%#.2e')
+        mod <- glmmTMB(peak ~ group,
+            ziformula = ~1, data = tab,
+            family = "nbinom2"
+        ) # nolint
+        pval <- sprintf(summary(mod)$coeff$cond[2, 4], fmt = "%#.2e")
     } else {
-        mod <- glmmTMB(log1p(peak_surface) ~ group, data = tab, ziformula = ~1,
-                       family = "gaussian") # nolint
-        pval <- sprintf(summary(mod)$coeff$cond[2, 4], fmt = '%#.2e')
+        mod <- glmmTMB(log1p(peak_surface) ~ group,
+            data = tab, ziformula = ~1,
+            family = "gaussian"
+        ) # nolint
+        pval <- sprintf(summary(mod)$coeff$cond[2, 4], fmt = "%#.2e")
     }
     pdf(output_fig, width = 12, height = 8)
     plot(simulateResiduals(mod)) # nolint
@@ -699,12 +679,14 @@ glm_log_pvalue <- function(tab, pic_col,
     output_diag <- paste0(output_folder, "/diagnotics")
     dir.create(output_diag, showWarnings = F)
     ptype_name <- tab$ptype[1]
-    output_fig <- paste0(output_diag, "/glm_nb_", pic_col, "_",
-        ptype_name, ".pdf")
+    output_fig <- paste0(
+        output_diag, "/glm_nb_", pic_col, "_",
+        ptype_name, ".pdf"
+    )
     pdf(output_fig, width = 12, height = 8)
     plot(simulateResiduals(mod)) # nolint
     dev.off()
-    return(sprintf(summary(mod)$coeff[2, 4], fmt = '%#.2e'))
+    return(sprintf(summary(mod)$coeff[2, 4], fmt = "%#.2e"))
 }
 
 
@@ -753,7 +735,7 @@ create_peaks_barplots <- function(tdd_filt, pval, output_fig,
         )
     }
     if (gene_type == "TDD_BRAF") {
-         valcol <- c("dimgrey", blue_tdd)
+        valcol <- c("dimgrey", blue_tdd)
     } else {
         valcol <- c("dimgrey", red_tdd)
     }
@@ -765,10 +747,14 @@ create_peaks_barplots <- function(tdd_filt, pval, output_fig,
         ggtitle(title) +
         labs(x = "peak type")
     if (data_col == "peak") {
-        pval_log <- list(pbraf = glm_log_pvalue(
-            tdd_filt %>% filter(ptype == "BRAF"), has_col, dirname(output_fig)),
+        pval_log <- list(
+            pbraf = glm_log_pvalue(
+                tdd_filt %>% filter(ptype == "BRAF"), has_col, dirname(output_fig)
+            ),
             pdmso = glm_log_pvalue(
-            tdd_filt %>% filter(ptype == "DMSO"), has_col, dirname(output_fig)))
+                tdd_filt %>% filter(ptype == "DMSO"), has_col, dirname(output_fig)
+            )
+        )
         ntitle <- paste0(
             "Average proportion of genes ", gene_type,
             " and control genes habing at least one BRAF/DMSO peaks\n",
@@ -780,8 +766,7 @@ create_peaks_barplots <- function(tdd_filt, pval, output_fig,
         )) +
             geom_bar(stat = "identity", position = "dodge") +
             scale_fill_manual(values = valcol) +
-            ggtitle(ntitle) +
-            labs(x = "peak type")
+            as_
         pdf(output_fig, width = 12, height = 12)
         grid.arrange(p1, p2, ncol = 1) # nolint
         dev.off()
@@ -815,7 +800,7 @@ create_peaks_density <- function(tdd_filt, pval, outfile, data_col,
         xcol <- paste0("log1p(", data_col, ")")
     }
     if (gene_type == "TDD_BRAF") {
-         valcol <- c("dimgrey", blue_tdd) # nolint
+        valcol <- c("dimgrey", blue_tdd) # nolint
     } else {
         valcol <- c("dimgrey", red_tdd) # nolint
     }
@@ -853,7 +838,8 @@ create_peaks_density <- function(tdd_filt, pval, outfile, data_col,
             "(p_BRAF_peak = ", pval$peak_braf, ")"
         )
         p1 <- ggplot(tdd_filt %>% filter(ptype == "BRAF"),
-                    mapping = aes_string(x = xcol, fill = "group")) +
+            mapping = aes_string(x = xcol, fill = "group")
+        ) +
             geom_density(mapping = aes(fill = group), bw = bw, alpha = 0.5) +
             scale_fill_manual(values = valcol) +
             ggtitle(title) +
@@ -864,7 +850,8 @@ create_peaks_density <- function(tdd_filt, pval, outfile, data_col,
             "(p_DMSO_peak = ", pval$peak_dmso, ")"
         )
         p2 <- ggplot(tdd_filt %>% filter(ptype == "DMSO"),
-                    mapping = aes_string(x = xcol, fill = "group")) +
+            mapping = aes_string(x = xcol, fill = "group")
+        ) +
             geom_density(mapping = aes(fill = group), bw = bw, alpha = 0.5) +
             scale_fill_manual(values = valcol) +
             ggtitle(title) +
@@ -887,15 +874,20 @@ create_peaks_density <- function(tdd_filt, pval, outfile, data_col,
 create_pic_figs <- function(tdd_full, gene_type = "TDD_BRAF",
                             output_folder = "./results/TDD_analysis",
                             kind = "peak") {
-
     data_col <- kind
     res_col <- paste0("mean_", data_col)
     has_col <- paste0("has_", data_col)
     has_res_col <- paste0("mean_", has_col)
-    pval <- list(peak_braf = glm_nb_pvalue(tdd_full %>% filter(ptype == "BRAF"),
-                                           data_col, output_folder),
-                peak_dmso = glm_nb_pvalue(tdd_full %>% filter(ptype == "DMSO"),
-                                           data_col, output_folder))
+    pval <- list(
+        peak_braf = glm_nb_pvalue(
+            tdd_full %>% filter(ptype == "BRAF"),
+            data_col, output_folder
+        ),
+        peak_dmso = glm_nb_pvalue(
+            tdd_full %>% filter(ptype == "DMSO"),
+            data_col, output_folder
+        )
+    )
     output_fig <- paste0(
         output_folder, "/barplot_peak_analysis_", gene_type, "_",
         kind, ".pdf"
@@ -926,7 +918,8 @@ get_peak_df <- function(kind = "peak", table_gene) {
     }
     pfiles <- c(
         "./data/gene_lists/BRAF_vs_DMSO.merged.bed",
-        "./data/gene_lists/DMSO_vs_BRAF.merged.bed")
+        "./data/gene_lists/DMSO_vs_BRAF.merged.bed"
+    )
 
     if (kind == "peak") {
         peak_table <- NULL
@@ -935,10 +928,12 @@ get_peak_df <- function(kind = "peak", table_gene) {
             cname <- str_replace(str_extract(peak_file, "(BRAF|DMSO)_"), "_", "")
             tpeak_table <- tibble(
                 gene = names(table(peak_list)),
-                peak = as.vector(table(peak_list)))
+                peak = as.vector(table(peak_list))
+            )
             tpeak_table <- tpeak_table %>% mutate(ptype = cname)
-            tpeak_table <- table_gene %>% left_join(tpeak_table, by = "gene"
-                ) %>% replace_na(list(peak = 0, ptype = cname))
+            tpeak_table <- table_gene %>%
+                left_join(tpeak_table, by = "gene") %>%
+                replace_na(list(peak = 0, ptype = cname))
             peak_table <- rbind(peak_table, tpeak_table)
         }
     } else {
@@ -954,8 +949,9 @@ get_peak_df <- function(kind = "peak", table_gene) {
                 group_by(gene) %>%
                 summarise(peak_surface = sum(size))
             tpeak_table <- tpeak_table %>% mutate(ptype = cname)
-            tpeak_table <- table_gene %>% left_join(tpeak_table, by = "gene"
-                    ) %>% replace_na(list(peak_surface = 0, ptype = cname))
+            tpeak_table <- table_gene %>%
+                left_join(tpeak_table, by = "gene") %>%
+                replace_na(list(peak_surface = 0, ptype = cname))
             peak_table <- rbind(peak_table, tpeak_table)
         }
     }
@@ -1016,7 +1012,8 @@ build_peaks_fig <- function(mean_gene_count = 10, ercc = F,
 }
 
 #' Create TDD violin figure
-create_tdd_violin <- function(kind = "UP_CC_peaks",
+create_tdd_violin <- function(mkind = "UP_CC_peaks",
+                              col = "peak",
                               tdd_file = paste0(
                                   "./results/TDD_analysis/correlation/",
                                   "TDD_correlation_table.txt"
@@ -1024,34 +1021,45 @@ create_tdd_violin <- function(kind = "UP_CC_peaks",
                               output = "./results/TDD_analysis/tdd_violin") {
     tdd_table <- read_tsv(tdd_file) # nolint
     tdd_table <- filter_on_tp(tdd_table, "3_5")
-    tdd_table[tdd_table$peak != kind, "peak"] <- "CTRL"
+    if (col == "peak") {
+        tdd_table[tdd_table$peak != mkind, "peak"] <- "CTRL"
+        tdd_table$peak <- relevel(as.factor(tdd_table$peak), "CTRL")
+        tdd_table <- tdd_table %>% arrange(peak)
+    } else {
+        tdd_table[tdd_table$group != mkind, "group"] <- "CTRL"
+        tdd_table$group <- relevel(as.factor(tdd_table$group), "CTRL")
+        tdd_table <- tdd_table %>% arrange(group)
+    }
     tdd_table <- tdd_table %>% # nolint
         dplyr::select(
             gene, mean_DMSO_TDD, mean_BRAF_TDD,
-            mean_BRAF_TDD, peak
+            mean_BRAF_TDD, peak, group
         ) %>%
         rename(DMSO = mean_DMSO_TDD, BRAF = mean_BRAF_TDD) %>%
-        pivot_longer(c(-gene, -peak),
+        pivot_longer(c(-gene, -peak, -group),
             names_to = "condition",
             values_to = "TDD"
         )
-    if (kind == "UP_CC_peaks") {
+    if (mkind == "UP_CC_peaks" | mkind == "BRAF_UP") {
+        test_color <- "#fb7b7b"
         tdd_table <- tdd_table %>% filter(condition == "DMSO")
-        title <- paste0("Average TDD index comparisons (at 3 and 5h) in DMSO condition\n",
-        "between TCpeak-UP genes in DMSO conditions and CTRL genes"
+        title <- paste0(
+            "Average TDD index comparisons (at 3 and 5h) in DMSO condition\n",
+            "between TCpeak-UP genes in DMSO conditions and CTRL genes"
         )
     } else {
+        test_color <- "#7b7bfb"
         tdd_table <- tdd_table %>% filter(condition == "BRAF")
-        title <- paste0("Average TDD index comparisons (at 3 and 5h) in BRAF condition\n",
-        "between TCpeak-DOWN genes in BRAF conditions and CTRL genes"
+        title <- paste0(
+            "Average TDD index comparisons (at 3 and 5h) in BRAF condition\n",
+            "between TCpeak-DOWN genes in BRAF conditions and CTRL genes"
         )
     }
-    tdd_table$peak <- factor(tdd_table$peak, level=c(kind, "CTRL"))
     p <- ggplot(
         data = tdd_table,
-        mapping = aes(x = peak, y = TDD, fill = peak)
+        mapping = aes_string(x = col, y = "TDD", fill = col)
     ) +
-        scale_fill_manual(values = c("grey", "white")) +
+        scale_fill_manual(values = c("white", test_color)) +
         geom_violin(position = position_dodge(width = 0.8)) +
         geom_boxplot(width = 0.05, position = position_dodge(width = 0.8)) +
         theme_classic() +
@@ -1063,11 +1071,15 @@ create_tdd_violin <- function(kind = "UP_CC_peaks",
         coord_cartesian(ylim = c(-0.5, 1.5)) +
         ggtitle(title)
     dir.create(output, showWarnings = F)
-    outname <- paste0("Peak_", kind, "_3_5h")
+    outname <- paste0("Peak_", mkind, "_3_5h")
     pdf(paste0(output, "/", outname, ".pdf"), height = 10, width = 17)
     print(p)
     dev.off()
-    fit <- lm(TDD ^ 0.5 ~ peak, data = tdd_table)
+    if (col == "peak") {
+        fit <- lm(TDD^0.5 ~ peak, data = tdd_table)
+    } else {
+        fit <- lm(TDD^0.5 ~ group, data = tdd_table)
+    }
     simulationOutput <- simulateResiduals(fit, n = 250, plot = F) # nolint
     dir.create(paste0(output, "/diag"), showWarnings = F)
     pdf(paste0(output, "/diag/", outname, ".pdf"), width = 17, height = 10)
@@ -1080,8 +1092,10 @@ create_tdd_violin <- function(kind = "UP_CC_peaks",
 
 #' Launch create_tdd_violins for all possible values
 create_tdd_violins <- function() {
-    create_tdd_violin(kind = "UP_CC_peaks")
-    create_tdd_violin(kind = "DOWN_TC_peaks")
+    create_tdd_violin(mkind = "UP_CC_peaks", col = "peak")
+    create_tdd_violin(mkind = "DOWN_TC_peaks", col = "peak")
+    create_tdd_violin(mkind = "BRAF_DOWN", col = "group")
+    create_tdd_violin(mkind = "BRAF_UP", col = "group")
 }