diff --git a/scripts/plotting/plotting_thesis/pnca/gg_pairs_all_pnca.R b/scripts/plotting/plotting_thesis/pnca/gg_pairs_all_pnca.R
new file mode 100644
index 0000000..4a5c0d7
--- /dev/null
+++ b/scripts/plotting/plotting_thesis/pnca/gg_pairs_all_pnca.R
@@ -0,0 +1,210 @@
+source("~/git/LSHTM_analysis/config/pnca.R")
+source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
+
+#=======
+# output
+#=======
+outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
+cat("plots will output to:", outdir_images)
+
+
+
+my_gg_pairs=function(plot_df, plot_title
+                     , tt_args_size = 2.5
+                     , gp_args_size = 2.5){
+  ggpairs(plot_df,
+          columns = 1:(ncol(plot_df)-1),
+          upper = list(
+            continuous = wrap('cor', # ggally_cor()
+                              method = "spearman",
+                              use = "pairwise.complete.obs",
+                              title="ρ",
+                              digits=2,
+                              justify_labels = "centre",
+                              title_args=list(size=tt_args_size, colour="black"),#2.5
+                              group_args=list(size=gp_args_size)#2.5
+            )
+          ),
+          lower = list(
+            continuous = wrap("points",
+                              alpha = 0.7,
+                              size=0.125),
+            combo     = wrap("dot",
+                             alpha = 0.7,
+                             size=0.125)
+          ),
+          aes(colour = factor(ifelse(dst_mode==0,
+                                     "S",
+                                     "R") ),
+              alpha = 0.5),
+          title=plot_title) +
+    
+    scale_colour_manual(values = c("red", "blue")) +
+    scale_fill_manual(values = c("red", "blue")) #+
+  # theme(text = element_text(size=7,
+  #                           face="bold"))
+}
+
+DistCutOff = 10
+###########################################################################
+geneL_normal  = c("pnca")
+geneL_na      = c("gid", "rpob")
+geneL_ppi2    = c("alr", "embb", "katg", "rpob")
+
+merged_df3 = as.data.frame(merged_df3)
+
+corr_plotdf = corr_data_extract(merged_df3
+                                , gene = gene
+                                , drug = drug
+                                , extract_scaled_cols = F)
+
+aff_dist_cols = colnames(corr_plotdf)[grep("Dist", colnames(corr_plotdf))]
+static_cols =  c("Log10(MAF)"
+                 #, "Log10(OR)"
+)
+############################################################
+#=============================================
+# Creating masked df for affinity data
+#=============================================
+corr_affinity_df = corr_plotdf
+
+#----------------------
+# Mask affinity columns
+#-----------------------
+corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mCSM-lig"]=0
+corr_affinity_df[corr_affinity_df["Lig-Dist"]>DistCutOff,"mmCSM-lig"]=0
+
+if (tolower(gene)%in%geneL_ppi2){
+  corr_affinity_df[corr_affinity_df["PPI-Dist"]>DistCutOff,"mCSM-PPI2"]=0
+}
+
+if (tolower(gene)%in%geneL_na){
+  corr_affinity_df[corr_affinity_df["NA-Dist"]>DistCutOff,"mCSM-NA"]=0
+}
+
+# count 0
+#res <- colSums(corr_affinity_df==0)/nrow(corr_affinity_df)*100
+unmasked_vals <- nrow(corr_affinity_df) - colSums(corr_affinity_df==0) 
+unmasked_vals
+
+##########################################################
+#================
+# Stability
+#================
+corr_ps_colnames = c(static_cols
+                     , "mCSM-DUET"
+                     , "FoldX"
+                     , "DeepDDG"
+                     , "Dynamut2"
+                     , aff_dist_cols
+                     , "dst_mode")
+
+corr_df_ps = corr_plotdf[, corr_ps_colnames]
+
+# Plot #1
+plot_corr_df_ps = my_gg_pairs(corr_df_ps, plot_title="Stability estimates")
+
+##########################################################
+#================
+# Conservation
+#================
+corr_conservation_cols = c( static_cols
+                            , "ConSurf"
+                            , "SNAP2"
+                            , "PROVEAN"
+                            #, aff_dist_cols
+                            , "dst_mode"
+)
+
+corr_df_cons = corr_plotdf[, corr_conservation_cols]
+
+# Plot #2
+plot_corr_df_cons = my_gg_pairs(corr_df_cons, plot_title="Conservation estimates")
+
+##########################################################
+#================
+# Affinity: lig, ppi and na as applicable
+#================
+#corr_df_lig = corr_plotdf[corr_plotdf["Lig-Dist"]<DistCutOff,]
+common_aff_colnames = c("mCSM-lig"
+                        , "mmCSM-lig")
+
+if (tolower(gene)%in%geneL_normal){
+  aff_colnames = common_aff_colnames
+}
+if (tolower(gene)%in%geneL_ppi2){
+  aff_colnames = c(common_aff_colnames, "mCSM-PPI2")
+}
+
+if (tolower(gene)%in%geneL_na){
+  aff_colnames = c(common_aff_colnames, "mCSM-NA")
+}
+
+# building ffinal affinity colnames for correlation
+corr_aff_colnames = c(static_cols
+                      , aff_colnames
+                      , "dst_mode") # imp
+
+corr_df_aff = corr_affinity_df[, corr_aff_colnames]
+colnames(corr_df_aff)
+
+# Plot #3
+plot_corr_df_aff = my_gg_pairs(corr_df_aff
+                               , plot_title="Affinity estimates"
+                               #, tt_args_size = 4
+                               #, gp_args_size = 4
+                               )
+
+#### Combine plots #####
+# #png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
+# png(paste0(outdir_images
+#            ,tolower(gene)
+#            ,"_CorrAB.png"), height = 6, width=11.75, unit="in",res=300)
+# 
+# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
+#                    ggmatrix_gtable(plot_corr_df_cons),
+#                    #                  ggmatrix_gtable(plot_corr_df_aff),
+#                    #                   nrow=1, ncol=3, rel_heights = 7,7,3
+#                    nrow=1,
+#                    #rel_heights = 1,1
+#                    labels = "AUTO",
+#                    label_size = 12)
+# dev.off()
+# 
+# # affinity corr
+# #png("/home/tanu/tmp/gg_pairs_affinity.png", height =7, width=7, unit="in",res=300)
+# png(paste0(outdir_images
+#            ,tolower(gene)
+#            ,"_CorrC.png"), height =7, width=7, unit="in",res=300)
+# 
+# cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_aff),
+#                    labels = "C",
+#                    label_size = 12)
+# dev.off()
+
+#### Combine A ####
+png(paste0(outdir_images
+           ,tolower(gene)
+           ,"_CorrA.png"), height =8, width=8, unit="in",res=300)
+
+cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
+                   labels = "A",
+                   label_size = 12)
+dev.off()
+
+#### Combine B+C ####
+# B + C
+png(paste0(outdir_images
+           ,tolower(gene)
+           ,"_CorrBC.png"), height = 6, width=11.75, unit="in",res=300)
+
+cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_cons),
+                   ggmatrix_gtable(plot_corr_df_aff),
+                   #                  ggmatrix_gtable(plot_corr_df_aff),
+                   #                   nrow=1, ncol=3, rel_heights = 7,7,3
+                   nrow=1,
+                   #rel_heights = 1,1
+                   labels = c("B", "C"),
+                   label_size = 12)
+dev.off()
+
diff --git a/scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots.R b/scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots.R
new file mode 100644
index 0000000..4c8853a
--- /dev/null
+++ b/scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots.R
@@ -0,0 +1,318 @@
+#################
+# Numbers
+##################
+all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
+# 
+# lf_duet   = all_dm_om_df[['lf_duet']]
+# table(lf_duet$param_type)
+################################################################
+
+#======================
+# Data: Dist+Genomics
+#======================
+lf_dist_genP   = all_dm_om_df[['lf_dist_gen']]
+wf_dist_genP   = all_dm_om_df[['wf_dist_gen']]
+
+levels(lf_dist_genP$param_type)
+#lf_dist_genP$param_type <- factor(lf_dist_genP$param_type, levels=c("Log10(MAF)", "Lig Dist(Å)", "PPI Dist(Å)"))
+table(lf_dist_genP$param_type)
+
+genomics_param = c("Log10(MAF)")
+
+dist_genP = lf_bp2(lf_dist_genP
+               #, p_title
+               , violin_quantiles = c(0.5), monochrome = F)
+#dist_genP
+#-------------------
+# Genomics data plot
+#-------------------
+genomics_dataP = lf_dist_genP[lf_dist_genP$param_type%in%genomics_param,]
+genomics_dataP$param_type = factor(genomics_dataP$param_type)
+table(genomics_dataP$param_type)
+
+genomicsP = lf_bp2(genomics_dataP
+                   #, p_title = ""
+                   , dot_transparency = 0.3 #0.3 default
+                   , violin_quantiles = c(0.5), monochrome = F)
+
+genomicsP
+
+# #check
+# wilcox.test(wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="R"]
+#             , wf_dist_genP$`Log10(MAF)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
+# 
+# tapply(wf_dist_genP$`Log10(MAF)`, wf_dist_genP$mutation_info_labels, summary)
+
+#-------------------
+# Distance data plot: 
+#--------------------
+# not genomics
+dist_dataP = lf_dist_genP[!lf_dist_genP$param_type%in%genomics_param,]
+dist_dataP$param_type = factor(dist_dataP$param_type)
+table(dist_dataP$param_type)
+levels(dist_dataP$param_type)
+# relevel factor to control ordering of appearance of plot
+dist_dataP$param_type <-relevel(dist_dataP$param_type, "Lig Dist(Å)" )
+table(dist_dataP$param_type)
+levels(dist_dataP$param_type)
+
+distanceP = lf_bp2(dist_dataP
+                   #, p_title = ""
+                   , violin_quantiles = c(0.5), monochrome = F)
+
+distanceP
+
+# # check
+# wilcox.test(wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
+#             , wf_dist_genP$`PPI Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
+# 
+# wilcox.test(wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
+#             , wf_dist_genP$`Lig Dist(Å)`[wf_dist_genP$mutation_info_labels=="S"], paired = FALSE)
+# 
+# tapply(wf_dist_genP$`PPI Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
+# 
+# tapply(wf_dist_genP$`Lig Dist(Å)`, wf_dist_genP$mutation_info_labels, summary)
+
+
+#-------------------
+# Distance data plot: LigDist
+#--------------------
+levels(dist_dataP$param_type)[[1]]
+#Lig Dist(Å), PPI Dist(Å)
+dist_data_lig = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[1]]),]
+dist_data_lig$param_type = factor(dist_data_lig$param_type)
+table(dist_data_lig$param_type)
+levels(dist_data_lig$param_type)
+distanceP_lig = lf_bp2(dist_data_lig
+                       #, p_title = ""
+                       , violin_quantiles = c(0.5), monochrome = F)
+
+distanceP_lig
+
+if (tolower(gene)%in%geneL_ppi2){
+  #-------------------
+  # Distance data plot: LigDist
+  #--------------------
+  levels(dist_dataP$param_type)[[2]]
+  #Lig Dist(Å), PPI Dist(Å)
+  dist_data_ppi2 = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[2]]),]
+  dist_data_ppi2$param_type = factor(dist_data_ppi2$param_type)
+  table(dist_data_ppi2$param_type)
+  levels(dist_data_ppi2$param_type)
+  distanceP_ppi2 = lf_bp2(dist_data_ppi2
+                          #, p_title = ""
+                          , violin_quantiles = c(0.5)
+                          , dot_transparency = 0.2
+                          , monochrome = F)
+  
+  distanceP_ppi2
+}
+
+if (tolower(gene)%in%geneL_na){
+  #-------------------
+  # Distance data plot: NADist
+  #--------------------
+  levels(dist_dataP$param_type)[[2]]
+  #Lig Dist(Å), PPI Dist(Å)
+  dist_data_na = dist_dataP[dist_dataP$param_type%in%c(levels(dist_dataP$param_type)[[2]]),]
+  dist_data_na$param_type = factor(dist_data_na$param_type)
+  table(dist_data_na$param_type)
+  levels(dist_data_na$param_type)
+  distanceP_na = lf_bp2(dist_data_na
+                        #, p_title = ""
+                        , violin_quantiles = c(0.5), monochrome = F)
+  
+  distanceP_na
+}
+#==============
+# Plot:DUET
+#==============
+lf_duetP   = all_dm_om_df[['lf_duet']]
+#lf_duetP  = lf_duet[!lf_duet$param_type%in%c(static_colsP),]
+table(lf_duetP$param_type)
+lf_duetP$param_type = factor(lf_duetP$param_type)
+table(lf_duetP$param_type)
+
+duetP = lf_bp2(lf_duetP
+               #, p_title = ""
+               , violin_quantiles = c(0.5), monochrome = F
+               , dot_transparency = 0.2)
+
+#==============
+# Plot:FoldX
+#==============
+lf_foldxP   = all_dm_om_df[['lf_foldx']]
+#lf_foldxP  = lf_foldx[!lf_foldx$param_type%in%c(static_colsP),]
+table(lf_foldxP$param_type)
+lf_foldxP$param_type = factor(lf_foldxP$param_type)
+table(lf_foldxP$param_type)
+
+foldxP = lf_bp2(lf_foldxP
+                #, p_title = ""
+                , violin_quantiles = c(0.5), monochrome = F
+                , dot_transparency = 0.1)
+
+#==============
+# Plot:DeepDDG
+#==============
+lf_deepddgP   = all_dm_om_df[['lf_deepddg']]
+#lf_deepddgP  = lf_deepddg[!lf_deepddg$param_type%in%c(static_colsP),]
+table(lf_deepddgP$param_type)
+lf_deepddgP$param_type = factor(lf_deepddgP$param_type)
+table(lf_deepddgP$param_type)
+
+deepddgP = lf_bp2(lf_deepddgP
+                  #, p_title = ""
+                  , violin_quantiles = c(0.5), monochrome = F
+                  , dot_transparency = 0.2)
+
+deepddgP
+
+#==============
+# Plot: Dynamut2
+#==============
+lf_dynamut2P   = all_dm_om_df[['lf_dynamut2']]
+#lf_dynamut2P  = lf_dynamut2[!lf_dynamut2$param_type%in%c(static_colsP),]
+table(lf_dynamut2P$param_type)
+lf_dynamut2P$param_type = factor(lf_dynamut2P$param_type)
+table(lf_dynamut2P$param_type)
+
+dynamut2P = lf_bp2(lf_dynamut2P
+                   #, p_title = ""
+                   , violin_quantiles = c(0.5), monochrome = F
+                   , dot_transparency = 0.2)
+
+
+#==============
+# Plot:ConSurf
+#==============
+lf_consurfP   = all_dm_om_df[['lf_consurf']]
+#lf_consurfP  = lf_consurf[!lf_consurf$param_type%in%c(static_colsP),]
+table(lf_consurfP$param_type)
+lf_consurfP$param_type = factor(lf_consurfP$param_type)
+table(lf_consurfP$param_type)
+
+consurfP = lf_bp2(lf_consurfP
+                  #, p_title = ""
+                  , violin_quantiles = c(0.5), monochrome = F)
+
+#==============
+# Plot:PROVEAN
+#==============
+lf_proveanP   = all_dm_om_df[['lf_provean']]
+#lf_proveanP  = lf_provean[!lf_provean$param_type%in%c(static_colsP),]
+table(lf_proveanP$param_type)
+lf_proveanP$param_type = factor(lf_proveanP$param_type)
+table(lf_proveanP$param_type)
+
+proveanP = lf_bp2(lf_proveanP
+                  #, p_title = ""
+                  , violin_quantiles = c(0.5), monochrome = F)
+
+#==============
+# Plot:SNAP2
+#==============
+lf_snap2P  = all_dm_om_df[['lf_snap2']]
+#lf_snap2P  = lf_snap2[!lf_snap2$param_type%in%c(static_colsP),]
+table(lf_snap2P$param_type)
+lf_snap2P$param_type = factor(lf_snap2P$param_type)
+table(lf_snap2P$param_type)
+
+snap2P = lf_bp2(lf_snap2P
+                #, p_title = ""
+                , violin_quantiles = c(0.5), monochrome = F)
+
+
+############################################################################
+#================
+# Plot: mCSM-lig
+#================
+lf_mcsm_ligP   = all_dm_om_df[['lf_mcsm_lig']]
+#lf_mcsm_ligP  = lf_mcsm_lig[!lf_mcsm_lig$param_type%in%c(static_colsP),]
+table(lf_mcsm_ligP$param_type)
+lf_mcsm_ligP$param_type = factor(lf_mcsm_ligP$param_type)
+table(lf_mcsm_ligP$param_type)
+
+mcsmligP = lf_bp2(lf_mcsm_ligP
+                  #, p_title = ""
+                  , violin_quantiles = c(0.5), monochrome = F
+                  , dot_transparency = 0.8)
+
+mcsmligP
+#=================
+# Plot: mmCSM-lig2
+#=================
+lf_mmcsm_lig2P   = all_dm_om_df[['lf_mmcsm_lig2']]
+#lf_mmcsm_lig2P  = lf_mmcsm_lig2P[!lf_mmcsm_lig2P$param_type%in%c(static_colsP),]
+table(lf_mmcsm_lig2P$param_type)
+lf_mmcsm_lig2P$param_type = factor(lf_mmcsm_lig2P$param_type)
+table(lf_mmcsm_lig2P$param_type)
+
+mcsmlig2P = lf_bp2(lf_mmcsm_lig2P
+                   #, p_title = ""
+                   , violin_quantiles = c(0.5), monochrome = F
+                   , dot_transparency = 0.8)
+
+mcsmlig2P
+
+#================
+# Plot: mCSM-ppi2
+#================
+if (tolower(gene)%in%geneL_ppi2){
+  lf_mcsm_ppi2P   = all_dm_om_df[['lf_mcsm_ppi2']]
+  #lf_mcsm_ppi2P  = lf_mcsm_ppi2[!lf_mcsm_ppi2$param_type%in%c(static_colsP),]
+  table(lf_mcsm_ppi2P$param_type)
+  lf_mcsm_ppi2P$param_type = factor(lf_mcsm_ppi2P$param_type)
+  table(lf_mcsm_ppi2P$param_type)
+  
+  mcsmppi2P = lf_bp2(lf_mcsm_ppi2P
+                     #, p_title = ""
+                     , violin_quantiles = c(0.5), monochrome = F
+                     , dot_transparency = 0.3)
+  
+}
+
+#==============
+# Plot: mCSM-NA
+#==============
+if (tolower(gene)%in%geneL_na){
+  lf_mcsm_naP   = all_dm_om_df[['lf_mcsm_na']]
+  #lf_mcsm_naP  = lf_mcsm_na[!lf_mcsm_na$param_type%in%c(static_colsP),]
+  table(lf_mcsm_naP$param_type)
+  lf_mcsm_naP$param_type = factor(lf_mcsm_naP$param_type)
+  table(lf_mcsm_naP$param_type)
+  
+  mcsmnaP = lf_bp2(lf_mcsm_naP
+                   #, p_title = ""
+                   , violin_quantiles = c(0.5), monochrome = F
+                   , dot_transparency = 0.4)
+  
+}
+
+######################################
+# Outplot  with stats
+######################################
+# outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
+# 
+# dm_om_combinedP =  paste0(outdir_images
+#                    ,tolower(gene)
+#                    ,"_dm_om_all.svg" )
+# 
+# cat("DM OM plots with stats:", dm_om_combinedP)
+# svg(dm_om_combinedP, width = 32, height = 18)
+# cowplot::plot_grid(
+#   cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P, genomicsP, distanceP
+#                    , nrow=1
+#                    , rel_widths = c(1/7, 1/7,1/7,1/7, 1/7, 1.75/7)),
+#                    #, rel_widths = c(1/8, 1/8,1/8,1/8, 1/8, 2.75/8)), # for 3 distances
+#   cowplot::plot_grid(consurfP, proveanP, snap2P
+#                    , mcsmligP
+#                    , mcsmlig2P
+#                    , mcsmppi2P
+#                    #, mcsmnaP
+#                    , nrow=1),
+#   nrow=2)
+# 
+# dev.off()
+
+
diff --git a/scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots_layout.R b/scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots_layout.R
new file mode 100644
index 0000000..d0ca67e
--- /dev/null
+++ b/scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots_layout.R
@@ -0,0 +1,176 @@
+# source dm_om_plots.R
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/pnca/pnca_dm_om_plots.R")
+
+##### plots to combine ####
+duetP
+foldxP
+deepddgP
+dynamut2P
+genomicsP
+consurfP
+proveanP
+snap2P
+mcsmligP
+mcsmlig2P
+#mcsmppi2P
+
+# Plot labels
+tit1 = "Stability changes"
+tit2 = "Genomic measure"
+tit3 = "Distance to partners"
+tit4 = "Evolutionary Conservation"
+tit5 = "Affinity changes"
+pt_size = 30
+
+theme_georgia <- function(...) {
+  theme_gray(base_family = "sans", ...) + 
+    theme(plot.title = element_text(face = "bold"))
+}
+
+
+title_theme <- calc_element("plot.title", theme_georgia())
+
+pt1 = ggdraw() + 
+  draw_label(
+    tit1,
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    #size = title_theme$size
+    size = pt_size
+  )
+
+pt2 = ggdraw() + 
+  draw_label(
+    tit2,
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    size = pt_size
+  )
+
+pt3 = ggdraw() + 
+  draw_label(
+    tit3,
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    size = pt_size
+  )
+
+pt4 = ggdraw() + 
+  draw_label(
+    tit4,
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    size = pt_size
+  )
+
+
+pt5 = ggdraw() + 
+  draw_label(
+    tit5,
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    size = pt_size
+  )
+
+#======================
+# Output plot function
+#======================
+OutPlot_dm_om = function(x){
+  
+  # dist b/w plot title and plot
+  relH_tp = c(0.08, 0.92)
+  
+  my_label_size = 25
+  #----------------
+  # Top panel
+  #----------------
+  top_panel = cowplot::plot_grid(
+    cowplot::plot_grid(pt1, 
+                       cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
+                                          , nrow = 1
+                                          , labels = c("A", "B", "C", "D")
+                                          , label_size = my_label_size)
+                       , ncol = 1
+                       , rel_heights = relH_tp
+    ),
+    NULL,
+    cowplot::plot_grid(pt2, 
+                       cowplot::plot_grid(genomicsP
+                                          , nrow = 1
+                                          , labels = c("E")
+                                          , label_size = my_label_size)
+                       , ncol = 1
+                       , rel_heights = relH_tp
+    ),
+    NULL,
+    cowplot::plot_grid(pt3, 
+                       cowplot::plot_grid( #distanceP
+                         distanceP_lig
+                         #, distanceP_ppi2
+                         , nrow = 1
+                         , labels = c("F")
+                         , label_size = my_label_size)
+                       , ncol = 1
+                       , rel_heights = relH_tp
+    ),
+    nrow = 1,
+    rel_widths = c(2/6, 0, 0.5/6, 0, 0.5/6)
+  )
+  
+  #----------------
+  # Bottom panel
+  #----------------
+  bottom_panel = cowplot::plot_grid(
+    cowplot::plot_grid(pt4, 
+                       cowplot::plot_grid(consurfP, proveanP, snap2P
+                                          , nrow = 1
+                                          , labels = c("H", "I", "J")
+                                          , label_size = my_label_size)
+                       , ncol = 1
+                       , rel_heights =relH_tp
+    ),NULL,
+    cowplot::plot_grid(pt5, 
+                       cowplot::plot_grid(mcsmligP
+                                          , mcsmlig2P
+                                          #, mcsmppi2P
+                                          , nrow = 1
+                                          , labels = c("K", "L")
+                                          , label_size = my_label_size)
+                       , ncol = 1
+                       , rel_heights = relH_tp
+    ),NULL,
+    nrow = 1, 
+    rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
+  )
+  
+  #-------------------------------
+  # combine: Top and Bottom panel
+  #-------------------------------
+  cowplot::plot_grid (top_panel, bottom_panel
+                      , nrow =2
+                      , rel_widths = c(1, 1)
+                      , align = "hv")
+}
+
+#=====================
+# OutPlot: svg and png
+#======================
+dm_om_combinedP =  paste0(outdir_images
+                          ,tolower(gene)
+                          ,"_dm_om_all.svg")
+
+cat("DM OM plots with stats:", dm_om_combinedP)
+svg(dm_om_combinedP, width = 32, height = 18)
+
+OutPlot_dm_om()
+dev.off()
+
+
+dm_om_combinedP_png =  paste0(outdir_images
+                              ,tolower(gene)
+                              ,"_dm_om_all.png")
+cat("DM OM plots with stats:", dm_om_combinedP_png)
+png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
+
+OutPlot_dm_om()
+dev.off()