more plot files

scripts/plotting/AFFINITY_TEST.R

@@ -0,0 +1,120 @@
+# find the most "impactful" effect value
+biggest=max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
+
+abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==biggest
+
+abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')]))==c(,biggest)
+
+max(abs((a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])))
+
+
+a2 = (a[c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')])
+a2
+
+biggest = max(abs(a2[1,]))
+
+#hmm
+#which(abs(a2) == biggest)
+#names(a2)[apply(a2, 1:4, function(i) which(i == max()))]
+
+# get row max
+a2$row_maximum = apply(abs(a2[,-1]), 1, max)
+
+# get colname for abs(max_value)
+#https://stackoverflow.com/questions/36960010/get-column-name-that-matches-specific-row-value-in-dataframe
+#names(df)[which(df == 1, arr.ind=T)[, "col"]]
+# yayy
+names(a2)[which(abs(a2) == biggest, arr.ind=T)[, "col"]]
+
+#another:https://statisticsglobe.com/return-column-name-of-largest-value-for-each-row-in-r
+colnames(a2)[max.col(abs(a2), ties.method = "first")]  # Apply colnames & max.col functions
+#################################################
+# use whole df
+#gene_aff_cols = c('affinity_scaled','mmcsm_lig_scaled','mcsm_ppi2_scaled','mcsm_na_scaled')
+
+biggest      = max(abs(a[gene_aff_cols]))
+a$max_es     = biggest
+a$effect     = names(a[gene_aff_cols])[which(abs(a[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
+
+effect_name  = unique(a$effect)
+#get index of value of max effect
+ind = (which(abs(a[effect_name]) == biggest, arr.ind=T))
+a[effect_name][ind]
+# extract sign
+a$effect_sign = sign(a[effect_name][ind])
+########################################################
+# maxn <- function(n) function(x) order(x, decreasing = TRUE)[n]
+# second_big = abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols])]
+# apply(df, 1, function(x)x[maxn(1)(x)])
+# apply(a[gene_aff_cols], 1, function(x) abs(a[gene_aff_cols])[maxn(2)(abs(a[gene_aff_cols]))])
+#########################################################
+# loop
+a2 = df2[df2$position%in%c(167, 423, 427),]
+test <- a2 %>%
+  dplyr::group_by(position) %>%
+  biggest      = max(abs(a2[gene_aff_cols]))
+  a2$max_es    = max(abs(a2[gene_aff_cols]))
+  a2$effect    = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
+  effect_name  = unique(a2$effect)
+  
+  #get index of value of max effect
+  ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
+  a2[effect_name][ind]
+  # extract sign
+  a2$effect_dir = sign(a2[effect_name][ind])
+#################################
+df2_short = df2[df2$position%in%c(167, 423, 427),]
+  
+for (i in unique(df2_short$position) ){
+  #print(i)
+  #print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
+  #cat(length(unique(df2$position)))
+  a2 = df2_short[df2_short$position==i,]
+  biggest     = max(abs(a2[gene_aff_cols]))
+  a2$max_es   = max(abs(a2[gene_aff_cols]))
+  a2$effect   = names(a2[gene_aff_cols])[which(abs(a2[gene_aff_cols]) == biggest, arr.ind=T)[, "col"]]
+  effect_name = unique(a2$effect)
+  
+  #get index of value of max effect
+  ind = (which(abs(a2[effect_name]) == biggest, arr.ind=T))
+  a2[effect_name][ind]
+  # extract sign
+  a2$effect_sign = sign(a2[effect_name][ind])
+}
+
+#========================
+df2_short = df2[df2$position%in%c(167, 423, 427),]
+df2_short = df2[df2$position%in%c(170, 167, 493, 453, 435, 433, 480, 456, 445),]
+df2_short = df2[df2$position%in%c(435, 480),]
+  
+give_col=function(x,y,df=df2_short){
+  df[df$position==x,y]
+}
+  
+for (i in unique(df2_short$position) ){
+  #print(i)
+  #print(paste0("\nNo. of unique positions:", length(unique(df2$position))) )
+  #cat(length(unique(df2$position)))
+  #df2_short[df2_short$position==i,gene_aff_cols]
+  
+  biggest     = max(abs(give_col(i,gene_aff_cols)))
+  
+  df2_short[df2_short$position==i,'abs_max_effect'] = biggest
+  df2_short[df2_short$position==i,'effect_type']= names(
+    give_col(i,gene_aff_cols)[which(
+      abs(
+        give_col(i,gene_aff_cols)
+        ) == biggest, arr.ind=T
+      )[, "col"]])
+  
+  effect_name = unique(df2_short[df2_short$position==i,'effect_type'])
+  
+  # get index/rowname for value of max effect, and then use it to get the original sign
+  # here
+  #df2_short[df2_short$position==i,c(effect_name)]
+  #which(abs(df2_short[df2_short$position==i,c('position',effect_name)][effect_name])==biggest, arr.ind=T)
+  ind = rownames(which(abs(df2_short[df2_short$position==i
+                                     ,c('position',effect_name)][effect_name])== biggest
+                       , arr.ind=T))
+  df2_short[df2_short$position==i,'effect_sign'] = sign(df2_short[effect_name][ind,])
+}

scripts/plotting/mcsm_affinity_data_only.R

@@ -0,0 +1,223 @@
+#source("~/git/LSHTM_analysis/config/pnca.R")
+#source("~/git/LSHTM_analysis/config/alr.R")
+#source("~/git/LSHTM_analysis/config/gid.R")
+#source("~/git/LSHTM_analysis/config/embb.R")
+#source("~/git/LSHTM_analysis/config/katg.R")
+source("~/git/LSHTM_analysis/config/rpob.R")
+
+source("/home/tanu/git/LSHTM_analysis/my_header.R")
+#########################################################
+# TASK: Generate averaged affinity values 
+# across all affinity tools for a given structure
+# as applicable...
+#########################################################
+
+#=======
+# output
+#=======
+outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene))
+
+#OutFile1
+outfile_mean_aff = paste0(outdir_images, "/", tolower(gene)
+                                , "_mean_affinity_all.csv")
+print(paste0("Output file:", outfile_mean_aff))
+
+#OutFile2
+outfile_mean_aff_priorty = paste0(outdir_images, "/", tolower(gene)
+                          , "_mean_affinity_priority.csv")
+print(paste0("Output file:", outfile_mean_aff_priorty))
+
+#%%===============================================================
+
+#=============
+# Input
+#=============
+df3_filename = paste0("/home/tanu/git/Data/", drug, "/output/", tolower(gene), "_merged_df3.csv")
+df3 = read.csv(df3_filename)
+length(df3$mutationinformation)
+
+# mut_info checks
+table(df3$mutation_info)
+table(df3$mutation_info_orig)
+table(df3$mutation_info_labels_orig)
+
+# used in plots and analyses
+table(df3$mutation_info_labels) # different, and matches dst_mode
+table(df3$dst_mode)
+
+# create column based on dst mode with different colname
+table(is.na(df3$dst))
+table(is.na(df3$dst_mode))
+
+#===============
+# Create column: sensitivity mapped to dst_mode
+#===============
+df3$sensitivity = ifelse(df3$dst_mode == 1, "R", "S")
+table(df3$sensitivity)
+
+length(unique((df3$mutationinformation)))
+all_colnames = as.data.frame(colnames(df3))
+common_cols  = c("mutationinformation"
+                 , "X5uhc_position"
+                 , "X5uhc_offset"
+                 , "position"
+                 , "dst_mode"
+                 , "mutation_info_labels"
+                 , "sensitivity"
+                 , "ligand_distance"
+                 , "interface_dist")
+
+all_colnames$`colnames(df3)`[grep("scaled", all_colnames$`colnames(df3)`)]
+all_colnames$`colnames(df3)`[grep("outcome", all_colnames$`colnames(df3)`)]
+
+#===================
+# stability cols
+#===================
+raw_cols_stability =  c("duet_stability_change"
+                        , "deepddg"
+                        , "ddg_dynamut2"
+                        , "ddg_foldx")
+
+scaled_cols_stability = c("duet_scaled"       
+                           , "deepddg_scaled"   
+                           , "ddg_dynamut2_scaled"
+                           , "foldx_scaled")
+
+outcome_cols_stability = c("duet_outcome"
+                           , "deepddg_outcome"
+                           , "ddg_dynamut2_outcome"
+                           , "foldx_outcome")
+
+#===================
+# affinity cols
+#===================
+raw_cols_affinity =  c("ligand_affinity_change"
+                       , "mmcsm_lig"
+                       , "mcsm_ppi2_affinity"
+                       , "mcsm_na_affinity")
+
+scaled_cols_affinity = c("affinity_scaled" 
+                         , "mmcsm_lig_scaled" 
+                         , "mcsm_ppi2_scaled" 
+                         , "mcsm_na_scaled" )
+
+outcome_cols_affinity  = c( "ligand_outcome"
+                            , "mmcsm_lig_outcome"
+                            , "mcsm_ppi2_outcome"
+                            , "mcsm_na_outcome")
+
+#===================
+# conservation cols
+#===================
+# raw_cols_conservation =  c("consurf_score"
+#                            , "snap2_score"
+#                            , "provean_score")
+# 
+# scaled_cols_conservation = c("consurf_scaled"
+#                              , "snap2_scaled"
+#                              , "provean_scaled")
+# 
+# # CANNOT strictly be used, as categories are not identical with conssurf missing altogether
+# outcome_cols_conservation = c("provean_outcome"
+#                               , "snap2_outcome"
+#                               #consurf outcome doesn't exist
+# )
+
+######################################################################
+cols_to_consider = colnames(df3)[colnames(df3)%in%c(common_cols
+                                                    , raw_cols_affinity
+                                                    , scaled_cols_affinity
+                                                    , outcome_cols_affinity
+                                                    , raw_cols_stability
+                                                    , scaled_cols_stability
+                                                    , outcome_cols_stability             
+                                                    )]
+
+cols_to_extract  = cols_to_consider[cols_to_consider%in%c(common_cols
+                                                          #, raw_cols_affinity
+                                                          , scaled_cols_stability
+                                                          , scaled_cols_affinity)]
+
+df3_plot = df3[, cols_to_extract]
+
+# FIXME: ADD distance to NA when SP replies
+DistCutOff_colnames = c("ligand_distance", "interface_dist")
+DistCutOff = 10
+
+#df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 | df3_plot$interface_dist <10),]
+df3_affinity_filtered = df3_plot[ (df3_plot$ligand_distance<10 & df3_plot$interface_dist <10),]
+
+c0u = unique(df3_affinity_filtered$position)
+length(c0u)
+
+foo = df3_affinity_filtered[df3_affinity_filtered$ligand_distance<10,]
+bar = df3_affinity_filtered[df3_affinity_filtered$interface_dist<10,]
+
+wilcox.test(foo$mmcsm_lig_scaled~foo$sensitivity)
+wilcox.test(foo$mmcsm_lig~foo$sensitivity)
+
+wilcox.test(foo$affinity_scaled~foo$sensitivity)
+wilcox.test(foo$ligand_affinity_change~foo$sensitivity)
+
+wilcox.test(bar$mcsm_na_scaled~bar$sensitivity)
+wilcox.test(bar$mcsm_na_affinity~bar$sensitivity)
+
+wilcox.test(bar$mcsm_ppi2_scaled~bar$sensitivity)
+wilcox.test(bar$mcsm_ppi2_affinity~bar$sensitivity)
+
+##############################################################
+df = df3_affinity_filtered
+sum(is.na(df))
+df2 = na.omit(df)                           # Apply na.omit function
+
+gene_dist_cols = colnames(df2)[colnames(df2)%in%DistCutOff_colnames]
+gene_aff_cols = colnames(df2)[colnames(df2)%in%scaled_cols_affinity]
+gene_stab_cols = colnames(df2)[colnames(df2)%in%scaled_cols_stability]
+
+sel_cols = c("mutationinformation", "position", gene_stab_cols, gene_dist_cols,  gene_aff_cols)
+df3 = df2[, sel_cols]
+
+give_col=function(x,y,df=df3){
+  df[df$position==x,y]
+}
+
+for (i in unique(df3$position) ){
+  #print(i)
+  biggest     = max(abs(give_col(i,gene_aff_cols)))
+  
+  df3[df3$position==i,'abs_max_effect'] = biggest
+  df3[df3$position==i,'effect_type']= names(
+    give_col(i,gene_aff_cols)[which(
+      abs(
+        give_col(i,gene_aff_cols)
+      ) == biggest, arr.ind=T
+    )[, "col"]])
+  
+  effect_name = unique(df3[df3$position==i,'effect_type'])
+  
+  # get index/rowname for value of max effect, and then use it to get the original sign
+  # here
+  #df3[df3$position==i,c(effect_name)]
+  #which(abs(df3[df3$position==i,c('position',effect_name)][effect_name])==biggest, arr.ind=T)
+  ind = rownames(which(abs(df3[df3$position==i
+                                     ,c('position', effect_name)][effect_name])== biggest
+                       , arr.ind=T))
+  df3[df3$position==i,'effect_sign'] = sign(df3[effect_name][ind,])
+}
+
+
+
+
+
+
+#%%============================================================
+# output
+write.csv(combined_df, outfile_mean_ens_st_aff
+          , row.names = F)
+cat("Finished writing file:\n"
+    , outfile_mean_ens_st_aff
+    , "\nNo. of rows:", nrow(combined_df)
+    , "\nNo. of cols:", ncol(combined_df))
+
+# end of script
+#===============================================================

scripts/plotting/plotting_thesis/alr/basic_barplots_alr.R

@@ -0,0 +1,364 @@
+#!/usr/bin/env Rscript   
+#########################################################
+# TASK: Barplots
+# basic barplots with outcome
+# basic barplots with frequency of count of mutations
+#########################################################
+#=============
+# Data: Input
+#==============
+#source("~/git/LSHTM_analysis/config/katg.R")
+#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
+
+#cat("\nSourced plotting cols as well:", length(plotting_cols))
+
+####################################################
+class(merged_df3)
+
+df3 = subset(merged_df3, select = -c(pos_count))
+
+#=======
+# output
+#=======
+outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
+cat("plots will output to:", outdir_images)
+
+##########################################################
+# blue, red bp
+sts = 8
+lts = 8
+ats = 8
+als = 8
+ltis = 8
+geom_ls = 2.2
+
+#pos_count
+subtitle_size = 8
+geom_ls_pc = 2.2
+leg_text_size = 8
+axis_text_size = 8
+axis_label_size = 8
+
+###########################################################
+#------------------------------
+# plot default sizes
+#------------------------------
+#=========================
+# Affinity outcome
+# check this var: outcome_cols_affinity
+# get from preformatting or put in globals
+#==========================
+DistCutOff
+LigDist_colname  # = "ligand_distance" # from globals 
+ppi2Dist_colname
+naDist_colname
+
+###########################################################
+# get plotting data within the distance
+df3_lig  = df3[df3[[LigDist_colname]]<DistCutOff,]
+df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
+df3_na   = df3[df3[[naDist_colname]]<DistCutOff,]
+common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
+
+#------------------------------
+# barplot for ligand affinity:
+# <10 Ang of ligand
+#------------------------------
+mLigP = stability_count_bp(plotdf = df3_lig
+               , df_colname = "ligand_outcome"
+               #, leg_title  = "mCSM-lig"
+               #, bp_plot_title = paste(common_bp_title, "ligand")
+               , yaxis_title = "Number of nsSNPs"
+               , leg_position = "none"
+               , subtitle_text = "mCSM\nLig"
+               , bar_fill_values = c("#F8766D", "#00BFC4")
+               , subtitle_colour= "black"
+               , sts = sts
+               , lts = lts
+               , ats = ats
+               , als = als
+               , ltis = ltis
+               , geom_ls = geom_ls
+               )
+mLigP
+#------------------------------
+# barplot for ligand affinity:
+# <10 Ang of ligand
+# mmCSM-lig: will be the same no. of sites but the effect will be different
+#------------------------------
+mmLigP = stability_count_bp(plotdf = df3_lig
+                   , df_colname = "mmcsm_lig_outcome"
+                   #, leg_title  = "mmCSM-lig"
+                   #, label_categories = labels_mmlig
+                   #, bp_plot_title = paste(common_bp_title, "ligand")
+                   
+                   , yaxis_title = ""
+                   , leg_position = "none"
+                   , subtitle_text = "mmCSM\nLig"
+                   , bar_fill_values = c("#F8766D", "#00BFC4")
+                   , subtitle_colour= "black"
+                   , sts = sts
+                   , lts = lts
+                   , ats = ats
+                   , als = als
+                   , ltis = ltis
+                   , geom_ls = geom_ls
+                   )
+mmLigP
+#------------------------------
+# barplot for ppi2 affinity
+#  <10 Ang of interface
+#------------------------------
+if (tolower(gene)%in%geneL_ppi2){
+    ppi2P = stability_count_bp(plotdf = df3_ppi2
+                     , df_colname = "mcsm_ppi2_outcome"
+                     #, leg_title  = "mCSM-ppi2"
+                     #, label_categories = labels_ppi2
+                     #, bp_plot_title = paste(common_bp_title, "PP-interface")
+                     
+                     , yaxis_title = "Number of nsSNPs"
+                     , leg_position = "none"
+                     , subtitle_text = "mCSM\nPPI2"
+                     , bar_fill_values = c("#F8766D", "#00BFC4")
+                     , subtitle_colour= "black"
+                     , sts = sts
+                     , lts = lts
+                     , ats = ats
+                     , als = als
+                     , ltis = ltis
+                     , geom_ls = geom_ls
+                     )
+  ppi2P
+}
+#----------------------------
+# barplot for ppi2 affinity
+#  <10 Ang of interface
+#------------------------------
+if (tolower(gene)%in%geneL_na){
+  nca_distP = stability_count_bp(plotdf = df3_na
+                             , df_colname = "mcsm_na_outcome"
+                             #, leg_title  = "mCSM-NA"
+                             #, label_categories = 
+                             #, bp_plot_title = paste(common_bp_title, "Dist to NA")
+                             
+                             , yaxis_title = "Number of nsSNPs"
+                             , leg_position = "none"
+                             , subtitle_text = "mCSM\nNA"
+                             , bar_fill_values = c("#F8766D", "#00BFC4")
+                             , subtitle_colour= "black"
+                             , sts = sts
+                             , lts = lts
+                             , ats = ats
+                             , als = als
+                             , ltis = ltis
+                             , geom_ls = geom_ls
+  )
+  nca_distP
+}
+
+#####################################################################
+# ------------------------------
+# bp site site count: mCSM-lig
+# < 10 Ang ligand
+# ------------------------------
+common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
+
+posC_lig = site_snp_count_bp(plotdf = df3_lig
+                  , df_colname = "position"
+                  , xaxis_title = "Number of nsSNPs"
+                  , yaxis_title = "Number of Sites"
+                  , subtitle_colour = "chocolate4"
+                  , subtitle_text = ""
+                  , subtitle_size = subtitle_size
+                  , geom_ls = geom_ls_pc
+                  , leg_text_size = leg_text_size
+                  , axis_text_size = axis_text_size
+                  , axis_label_size = axis_label_size)
+
+posC_lig
+#------------------------------
+# bp site site count: ppi2
+# < 10 Ang interface
+#------------------------------
+if (tolower(gene)%in%geneL_ppi2){
+  posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
+                    , df_colname = "position"
+                    , xaxis_title = "Number of nsSNPs"
+                    , yaxis_title = "Number of Sites"
+                    , subtitle_colour = "chocolate4"
+                    , subtitle_text = ""
+                    , subtitle_size = subtitle_size
+                    , geom_ls = geom_ls_pc
+                    , leg_text_size = leg_text_size
+                    , axis_text_size = axis_text_size
+                    , axis_label_size = axis_label_size)
+  posC_ppi2
+}
+
+#------------------------------
+# bp site site count: NCA dist
+# < 10 Ang nca
+#------------------------------
+if (tolower(gene)%in%geneL_na){
+  posC_nca = site_snp_count_bp(plotdf = df3_na
+                                , df_colname = "position"
+                                , xaxis_title = "Number of nsSNPs"
+                                , yaxis_title = "Number of Sites"
+                                , subtitle_colour = "chocolate4"
+                                , subtitle_text = ""
+                               , subtitle_size = subtitle_size
+                               , geom_ls = geom_ls_pc
+                               , leg_text_size = leg_text_size
+                               , axis_text_size = axis_text_size
+                               , axis_label_size = axis_label_size)
+  posC_nca
+}
+#===============================================================
+#------------------------------
+# bp site site count: ALL
+# <10 Ang ligand
+#------------------------------
+posC_all = site_snp_count_bp(plotdf = df3
+                             , df_colname = "position"
+                             , xaxis_title = "Number of nsSNPs"
+                             , yaxis_title = "Number of Sites"
+                             , subtitle_colour = "chocolate4"
+                             , subtitle_text = "All mutations sites"
+                             , subtitle_size = subtitle_size
+                             , geom_ls = geom_ls_pc
+                             , leg_text_size = leg_text_size
+                             , axis_text_size = axis_text_size
+                             , axis_label_size = axis_label_size)
+posC_all
+##################################################################
+consurfP = stability_count_bp(plotdf = df3
+                              , df_colname = "consurf_outcome"
+                              #, leg_title = "ConSurf"
+                              #, label_categories = labels_consurf
+                              , yaxis_title = "Number of nsSNPs"
+                              , leg_position = "top"
+                              , subtitle_text = "ConSurf"
+                              , bar_fill_values = consurf_colours # from globals
+                              , subtitle_colour= "black"
+                              , sts = sts
+                              , lts = lts
+                              , ats = ats
+                              , als = als
+                              , ltis = ltis
+                              , geom_ls = geom_ls)
+
+consurfP
+
+##############################################################
+sts_so = 10
+lts_so = 10
+ats_so = 10
+als_so = 10
+ltis_so = 10
+geom_ls_so = 2.5
+#===================
+# Stability
+#===================
+# duetP
+duetP = stability_count_bp(plotdf = df3
+                           , df_colname = "duet_outcome"
+                           , leg_title = "mCSM-DUET"
+                           #, label_categories = labels_duet
+                           , yaxis_title = "Number of nsSNPs"
+                           , leg_position = "none"
+                           , subtitle_text = "mCSM-DUET"
+                           , bar_fill_values = c("#F8766D", "#00BFC4")
+                           , subtitle_colour= "black"
+                           , sts = sts_so
+                           , lts = lts_so
+                           , ats = ats_so
+                           , als = als_so
+                           , ltis = ltis_so
+                           , geom_ls = geom_ls_so)
+duetP
+
+# foldx
+foldxP = stability_count_bp(plotdf = df3
+                            , df_colname = "foldx_outcome"
+                            #, leg_title = "FoldX"
+                            #, label_categories = labels_foldx
+                            , yaxis_title = ""
+                            , leg_position = "none"
+                            , subtitle_text = "FoldX"
+                            , bar_fill_values = c("#F8766D", "#00BFC4")
+                            , sts = sts_so
+                            , lts = lts_so
+                            , ats = ats_so
+                            , als = als_so
+                            , ltis = ltis_so
+                            , geom_ls = geom_ls_so)
+foldxP
+
+# deepddg
+deepddgP = stability_count_bp(plotdf = df3
+                              , df_colname = "deepddg_outcome"
+                              #, leg_title = "DeepDDG"
+                              #, label_categories = labels_deepddg
+                              , yaxis_title = ""
+                              , leg_position = "none"
+                              , subtitle_text = "DeepDDG"
+                              , bar_fill_values = c("#F8766D", "#00BFC4")
+                              , sts = sts_so
+                              , lts = lts_so
+                              , ats = ats_so
+                              , als = als_so
+                              , ltis = ltis_so
+                              , geom_ls = geom_ls_so)
+deepddgP
+
+# deepddg
+dynamut2P = stability_count_bp(plotdf = df3
+                               , df_colname = "ddg_dynamut2_outcome"
+                               #, leg_title = "Dynamut2"
+                               #, label_categories = labels_ddg_dynamut2_outcome
+                               , yaxis_title = ""
+                               , leg_position = "none"
+                               , subtitle_text = "Dynamut2"
+                               , bar_fill_values = c("#F8766D", "#00BFC4")
+                               , sts = sts_so
+                               , lts = lts_so
+                               , ats = ats_so
+                               , als = als_so
+                               , ltis = ltis_so
+                               , geom_ls = geom_ls_so)
+dynamut2P
+
+# provean
+proveanP = stability_count_bp(plotdf = df3
+                              , df_colname = "provean_outcome"
+                              #, leg_title = "PROVEAN"
+                              #, label_categories = labels_provean
+                              , yaxis_title = "Number of nsSNPs"
+                              , leg_position = "none" # top
+                              , subtitle_text = "PROVEAN"
+                              , bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
+                              , sts = sts_so
+                              , lts = lts_so
+                              , ats = ats_so
+                              , als = als_so
+                              , ltis = ltis_so
+                              , geom_ls = geom_ls_so)
+proveanP
+
+# snap2
+snap2P = stability_count_bp(plotdf = df3
+                            , df_colname = "snap2_outcome"
+                            #, leg_title = "SNAP2"
+                            #, label_categories = labels_snap2
+                            , yaxis_title = ""
+                            , leg_position = "none" # top
+                            , subtitle_text = "SNAP2"
+                            , bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
+                            , sts = sts_so
+                            , lts = lts_so
+                            , ats = ats_so
+                            , als = als_so
+                            , ltis = ltis_so
+                            , geom_ls = geom_ls_so)
+snap2P
+#####################################################################################

scripts/plotting/plotting_thesis/alr/basic_barplots_alr_layout.R

@@ -0,0 +1,310 @@
+#=============
+# Data: Input
+#==============
+source("~/git/LSHTM_analysis/config/alr.R")
+source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
+
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/alr/basic_barplots_alr.R")
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/alr/pe_sens_site_count_alr.R")
+
+if ( tolower(gene)%in%c("alr") ){
+  cat("\nPlots available for layout are:")
+  
+  duetP
+  foldxP
+  deepddgP
+  dynamut2P
+  proveanP
+  snap2P
+  
+  mLigP
+  mmLigP
+  posC_lig
+  
+  ppi2P
+  posC_ppi2
+  
+  peP2
+  sens_siteP
+  peP   # not used
+  sensP # not used
+}
+
+
+#========================
+# Common title settings
+#=========================
+theme_georgia <- function(...) {
+  theme_gray(base_family = "sans", ...) + 
+    theme(plot.title = element_text(face = "bold"))
+}
+title_theme <- calc_element("plot.title", theme_georgia())
+
+###############################################################
+common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
+
+# extract common legends
+# lig affinity
+common_legend_outcome = get_legend(mLigP +
+                                     guides(color = guide_legend(nrow = 1)) +
+                                     theme(legend.position = "top"))
+
+# stability
+common_legend_outcome = get_legend(duetP +
+                                     guides(color = guide_legend(nrow = 1)) +
+                                     theme(legend.position = "top"))
+# conservation
+cons_common_legend_outcome = get_legend(snap2P +
+                                          guides(color = guide_legend(nrow = 1)) +
+                                          theme(legend.position = "top"))
+###################################################################
+#==================================
+# Stability+Conservation: COMBINE
+#==================================
+tt_size = 10
+#----------------------------
+# stability and consv title
+#----------------------------
+tt_stab = ggdraw() + 
+  draw_label(
+    paste0("Stability outcome"),
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    #size = title_theme$size
+    size = tt_size
+  )
+
+tt_cons = ggdraw() + 
+  draw_label(
+    paste0("Conservation outcome"),
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    size = tt_size
+  )
+
+#----------------------
+# Output plot
+#-----------------------
+stab_cons_CLP =  paste0(outdir_images
+                        ,tolower(gene)
+                        ,"_stab_cons_BP_CLP.png")
+
+print(paste0("plot filename:", stab_cons_CLP))
+png(stab_cons_CLP, units = "in", width = 10, height = 5, res = 300 )
+
+cowplot::plot_grid(
+  cowplot::plot_grid(
+    cowplot::plot_grid(
+      tt_stab,
+      common_legend_outcome,
+      nrow = 2
+    ),
+    cowplot::plot_grid(
+      duetP, 
+      foldxP, 
+      deepddgP, 
+      dynamut2P,
+      nrow = 1,
+      labels = c("A", "B", "C", "D"),
+      label_size = 12),
+    nrow = 2,
+    rel_heights=c(1,10)
+  ),
+  NULL,
+  cowplot::plot_grid(
+    cowplot::plot_grid(
+      cowplot::plot_grid(
+        tt_cons,
+        cons_common_legend_outcome,
+        nrow = 2
+      ),
+      cowplot::plot_grid(
+        proveanP,
+        snap2P,
+        nrow=1, 
+        labels = c("E", "F"),
+        align = "hv"),
+      nrow = 2,
+      rel_heights = c(1, 10),
+      label_size = 12),
+    nrow=1
+  ),
+  rel_widths = c(2,0.15,1),
+  nrow=1
+)
+
+dev.off()
+
+#################################################################
+#=======================================
+# Affinity barplots: COMBINE ALL four
+#========================================
+ligT = paste0(common_bp_title, " ligand")
+lig_affT = ggdraw() +
+  draw_label(
+    ligT,
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    #size = title_theme$size
+    size = 8
+  )
+
+p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
+                                           , common_legend_outcome
+                                           , nrow=2),
+                        cowplot::plot_grid(mLigP, mmLigP, posC_lig
+                                           , nrow = 1
+                                           , rel_widths = c(1,0.65,1.8)
+                                           , align = "h"),
+                        nrow = 2,
+                        rel_heights = c(1,8)
+                        
+)
+p1
+
+###########################################################
+ppi2T = paste0(common_bp_title, " PP-interface")
+ppi2_affT = ggdraw() +
+  draw_label(
+    ppi2T,
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    size = 8
+  )
+
+p3 = cowplot::plot_grid(cowplot::plot_grid(ppi2_affT, common_legend_outcome, nrow=2),
+                        cowplot::plot_grid(ppi2P, posC_ppi2
+                                           , nrow = 1
+                                           , rel_widths = c(1,1.9)
+                                           , align = "h"),
+                        nrow = 2,
+                        rel_heights = c(1,8)
+)
+p3
+
+# PE + All position count
+peT_allT = ggdraw() +
+  draw_label(
+    paste0("All mutation sites"),
+    fontfamily = title_theme$family,
+    fontface = title_theme$face,
+    #size = title_theme$size
+    size = 8
+  )
+
+p4 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
+                                           , rel_widths = c(1,3),axis = "lr"),
+                        cowplot::plot_grid(
+                          peP2, posC_all,
+                          nrow = 2,
+                          rel_widths = c(1,1),
+                          align = "v",
+                          axis = "lr",
+                          rel_heights = c(1,8)
+                        ),
+                        rel_heights = c(1,18),
+                        nrow = 2,axis = "lr")
+p4
+
+
+#### Combine p1+p3+p4 ####
+w = 11.79
+h = 3.5
+mut_impact_CLP =  paste0(outdir_images
+                         ,tolower(gene)
+                         ,"_mut_impactCLP.png")
+
+#svg(affP, width = 20, height = 5.5)
+print(paste0("plot filename:", mut_impact_CLP))
+png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
+
+cowplot::plot_grid(p1,
+                   p3,
+                   p4,
+                   nrow = 1,
+                   labels = "AUTO",
+                   label_size = 12,
+                   rel_widths = c(2.5,2,2)
+                   #, rel_heights = c(1)
+)
+
+dev.off()
+w = 11.79
+h = 3.5
+mut_impact_CLP =  paste0(outdir_images
+                         ,tolower(gene)
+                         ,"_mut_impactCLP.png")
+
+#svg(affP, width = 20, height = 5.5)
+print(paste0("plot filename:", mut_impact_CLP))
+png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
+
+cowplot::plot_grid(p1,
+                   p3,
+                   p4,
+                   nrow = 1,
+                   labels = "AUTO",
+                   label_size = 12,
+                   rel_widths = c(2.5,2,2)
+                   #, rel_heights = c(1)
+)
+
+dev.off()
+
+##################################################
+sensP
+consurfP
+#=================
+#### Combine sensitivity + ConSurf ####
+# or ConSurf
+#=================
+w = 3
+h = 3
+# sens_conP =  paste0(outdir_images
+#                          ,tolower(gene)
+#                          ,"_sens_cons_CLP.png")
+# 
+# print(paste0("plot filename:", sens_conP))
+# png(sens_conP, units = "in", width = w, height = h, res = 300 )
+# 
+# cowplot::plot_grid(sensP, consurfP,
+#                    nrow = 2,
+#                    rel_heights = c(1, 1.5)
+#                    )
+# 
+# dev.off()
+
+conCLP =  paste0(outdir_images
+                    ,tolower(gene)
+                    ,"_consurf_BP.png")
+
+print(paste0("plot filename:", conCLP))
+png(conCLP, units = "in", width = w, height = h, res = 300 )
+consurfP
+
+dev.off()
+#================================
+# Sensitivity mutation numbers: geom_tile
+#================================
+sensCLP =  paste0(outdir_images
+                 ,tolower(gene)
+                 ,"_sensN_tile.png")
+
+print(paste0("plot filename:", sensCLP))
+png(sensCLP, units = "in", width = 1, height = 1, res = 300 )
+sensP
+dev.off()
+#================================
+# Sensitivity SITE numbers: geom_tile
+#================================
+sens_siteCLP =  paste0(outdir_images
+                  ,tolower(gene)
+                  ,"_sens_siteC_tile.png")
+
+print(paste0("plot filename:", sens_siteCLP))
+png(sens_siteCLP, units = "in", width = 1.2, height = 1, res = 300 )
+sens_siteP
+dev.off()
+
+###########################################################
+

scripts/plotting/plotting_thesis/alr/dm_om_plots_layout_alr.R

@@ -1,164 +1,177 @@
-# source dm_om_plots.R
+# # source dm_om_plots.R
-#============
+# source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/dm_om_plots.R")
-# Plot labels
+# 
-#============
+# ##### plots to combine ####
-tit1 = "Stability changes"
+# duetP
-tit2 = "Genomic measure"
+# foldxP
-tit3 = "Distance to partners"
+# deepddgP
-tit4 = "Evolutionary Conservation"
+# dynamut2P
-tit5 = "Affinity changes"
+# genomicsP
-pt_size = 30
+# consurfP
-
+# proveanP
-theme_georgia <- function(...) {
+# snap2P
-  theme_gray(base_family = "sans", ...) + 
+# mcsmligP
-    theme(plot.title = element_text(face = "bold"))
+# mcsmlig2P
-}
+# mcsmppi2P
-
+# 
-
+# 
-title_theme <- calc_element("plot.title", theme_georgia())
+# # Plot labels
-
+# tit1 = "Stability changes"
-pt1 = ggdraw() + 
+# tit2 = "Genomic measure"
-  draw_label(
+# tit3 = "Distance to partners"
-    tit1,
+# tit4 = "Evolutionary Conservation"
-    fontfamily = title_theme$family,
+# tit5 = "Affinity changes"
-    fontface = title_theme$face,
+# pt_size = 30
-    #size = title_theme$size
+# 
-    size = pt_size
+# theme_georgia <- function(...) {
-  )
+#   theme_gray(base_family = "sans", ...) + 
-
+#     theme(plot.title = element_text(face = "bold"))
-pt2 = ggdraw() + 
+# }
-  draw_label(
+# 
-    tit2,
+# 
-    fontfamily = title_theme$family,
+# title_theme <- calc_element("plot.title", theme_georgia())
-    fontface = title_theme$face,
+# 
-    size = pt_size
+# pt1 = ggdraw() + 
-  )
+#   draw_label(
-
+#     tit1,
-pt3 = ggdraw() + 
+#     fontfamily = title_theme$family,
-  draw_label(
+#     fontface = title_theme$face,
-    tit3,
+#     #size = title_theme$size
-    fontfamily = title_theme$family,
+#     size = pt_size
-    fontface = title_theme$face,
+#   )
-    size = pt_size
+# 
-  )
+# pt2 = ggdraw() + 
-
+#   draw_label(
-pt4 = ggdraw() + 
+#     tit2,
-  draw_label(
+#     fontfamily = title_theme$family,
-    tit4,
+#     fontface = title_theme$face,
-    fontfamily = title_theme$family,
+#     size = pt_size
-    fontface = title_theme$face,
+#   )
-    size = pt_size
+# 
-  )
+# pt3 = ggdraw() + 
-
+#   draw_label(
-
+#     tit3,
-pt5 = ggdraw() + 
+#     fontfamily = title_theme$family,
-  draw_label(
+#     fontface = title_theme$face,
-    tit5,
+#     size = pt_size
-    fontfamily = title_theme$family,
+#   )
-    fontface = title_theme$face,
+# 
-    size = pt_size
+# pt4 = ggdraw() + 
-  )
+#   draw_label(
-
+#     tit4,
-#======================
+#     fontfamily = title_theme$family,
-# Output plot function
+#     fontface = title_theme$face,
-#======================
+#     size = pt_size
-OutPlot_dm_om = function(x){
+#   )
-  
+# 
-  # dist b/w plot title and plot
+# 
-  relH_tp = c(0.08, 0.92)
+# pt5 = ggdraw() + 
-  
+#   draw_label(
-  my_label_size = 25
+#     tit5,
-  #----------------
+#     fontfamily = title_theme$family,
-  # Top panel
+#     fontface = title_theme$face,
-  #----------------
+#     size = pt_size
-  top_panel = cowplot::plot_grid(
+#   )
-    cowplot::plot_grid(pt1, 
+# 
-                       cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
+# #======================
-                                          , nrow = 1
+# # Output plot function
-                                          , labels = c("A", "B", "C", "D")
+# #======================
-                                          , label_size = my_label_size)
+# OutPlot_dm_om = function(x){
-                       , ncol = 1
+#   
-                       , rel_heights = relH_tp
+#   # dist b/w plot title and plot
-    ),
+#   relH_tp = c(0.08, 0.92)
-    NULL,
+#   
-    cowplot::plot_grid(pt2, 
+#   my_label_size = 25
-                       cowplot::plot_grid(genomicsP
+#   #----------------
-                                          , nrow = 1
+#   # Top panel
-                                          , labels = c("E")
+#   #----------------
-                                          , label_size = my_label_size)
+#   top_panel = cowplot::plot_grid(
-                       , ncol = 1
+#     cowplot::plot_grid(pt1, 
-                       , rel_heights = relH_tp
+#                        cowplot::plot_grid(duetP, foldxP, deepddgP, dynamut2P
-    ),
+#                                           , nrow = 1
-    NULL,
+#                                           , labels = c("A", "B", "C", "D")
-    cowplot::plot_grid(pt3, 
+#                                           , label_size = my_label_size)
-                       cowplot::plot_grid( #distanceP
+#                        , ncol = 1
-                         distanceP_lig
+#                        , rel_heights = relH_tp
-                         , distanceP_ppi2
+#     ),
-                         #, distanceP_na
+#     NULL,
-                         , nrow = 1
+#     cowplot::plot_grid(pt2, 
-                         , labels = c("F", "G")
+#                        cowplot::plot_grid(genomicsP
-                         , label_size = my_label_size)
+#                                           , nrow = 1
-                       , ncol = 1
+#                                           , labels = c("E")
-                       , rel_heights = relH_tp
+#                                           , label_size = my_label_size)
-    ),
+#                        , ncol = 1
-    nrow = 1,
+#                        , rel_heights = relH_tp
-    rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7,  1/7)
+#     ),
-  )
+#     NULL,
-  
+#     cowplot::plot_grid(pt3, 
-  #----------------
+#                        cowplot::plot_grid( #distanceP
-  # Bottom panel
+#                          distanceP_lig
-  #----------------
+#                          , distanceP_ppi2
-  bottom_panel = cowplot::plot_grid(
+#                          , nrow = 1
-    cowplot::plot_grid(pt4, 
+#                          , labels = c("F", "G")
-                       cowplot::plot_grid(consurfP, proveanP, snap2P
+#                          , label_size = my_label_size)
-                                          , nrow = 1
+#                        , ncol = 1
-                                          , labels = c("H", "I", "J")
+#                        , rel_heights = relH_tp
-                                          , label_size = my_label_size)
+#     ),
-                       , ncol = 1
+#     nrow = 1,
-                       , rel_heights =relH_tp
+#     rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7,  1/7)
-    ),NULL,
+#   )
-    cowplot::plot_grid(pt5, 
+#   
-                       cowplot::plot_grid(mcsmligP, mcsmlig2P
+#   #----------------
-                                          , mcsmppi2P
+#   # Bottom panel
-                                          #, mcsmnaP
+#   #----------------
-                                          , nrow = 1
+#   bottom_panel = cowplot::plot_grid(
-                                          , labels = c("K", "L", "M")
+#     cowplot::plot_grid(pt4, 
-                                          , label_size = my_label_size)
+#                        cowplot::plot_grid(consurfP, proveanP, snap2P
-                       , ncol = 1
+#                                           , nrow = 1
-                       , rel_heights = relH_tp
+#                                           , labels = c("H", "I", "J")
-    ),NULL,
+#                                           , label_size = my_label_size)
-    nrow = 1, 
+#                        , ncol = 1
-    rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
+#                        , rel_heights =relH_tp
-  )
+#     ),NULL,
-  
+#     cowplot::plot_grid(pt5, 
-  #-------------------------------
+#                        cowplot::plot_grid(mcsmligP
-  # combine: Top and Bottom panel
+#                                           , mcsmlig2P
-  #-------------------------------
+#                                           , mcsmppi2P
-  cowplot::plot_grid (top_panel, bottom_panel
+#                                           , nrow = 1
-                      , nrow =2
+#                                           , labels = c("K", "L", "M")
-                      , rel_widths = c(1, 1)
+#                                           , label_size = my_label_size)
-                      , align = "hv")
+#                        , ncol = 1
-}
+#                        , rel_heights = relH_tp
-
+#     ),NULL,
-#=====================
+#     nrow = 1, 
-# OutPlot: svg and png
+#     rel_widths = c(3/6,0.1/6,3/6, 0.1/6 )
-#======================
+#   )
-dm_om_combinedP =  paste0(outdir_images
+#   
-                          ,tolower(gene)
+#   #-------------------------------
-                          ,"_dm_om_all.svg")
+#   # combine: Top and Bottom panel
-
+#   #-------------------------------
-cat("DM OM plots with stats:", dm_om_combinedP)
+#   cowplot::plot_grid (top_panel, bottom_panel
-svg(dm_om_combinedP, width = 32, height = 18)
+#                       , nrow =2
-
+#                       , rel_widths = c(1, 1)
-OutPlot_dm_om()
+#                       , align = "hv")
-dev.off()
+# }
-
+# 
-
+# #=====================
-dm_om_combinedP_png =  paste0(outdir_images
+# # OutPlot: svg and png
-                              ,tolower(gene)
+# #======================
-                              ,"_dm_om_all.png")
+# dm_om_combinedP =  paste0(outdir_images
-cat("DM OM plots with stats:", dm_om_combinedP_png)
+#                           ,tolower(gene)
-png(dm_om_combinedP_png, width = 32, height = 18, units = "in", res = 300)
+#                           ,"_dm_om_all.svg")
-
+# 
-OutPlot_dm_om()
+# cat("DM OM plots with stats:", dm_om_combinedP)
-dev.off()
+# 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()

scripts/plotting/plotting_thesis/alr/gg_pairs_all_alr.R

@@ -0,0 +1,209 @@
+#source("~/git/LSHTM_analysis/config/embb.R")
+#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.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
+                     , "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()
+

scripts/plotting/plotting_thesis/alr/pe_sens_site_count_alr.R

@@ -0,0 +1,173 @@
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/alr/prominent_effects_alr.R")
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/alr/sensitivity_count_alr.R")
+
+##############################################################
+#                         PE count
+#lig-- na--ppi2--stab
+# pe_colour_map = c("DD_lig"         = "#ffd700"   # gold
+#                   , "SS_lig"         = "#f0e68c"  # khaki
+#                   
+#                   , "DD_nucleic_acid"= "#a0522d"  # sienna
+#                   , "SS_nucleic_acid"= "#d2b48c"  # tan
+#                   
+#                   , "DD_ppi2"        = "#da70d6"  # orchid
+#                   , "SS_ppi2"        = "#ff1493"  # deeppink
+#                   
+#                   , "DD_stability"   = "#f8766d"  # red
+#                   , "SS_stability"   = "#00BFC4") # blue
+table(str_df_plot_cols$pe_effect_outcome)
+##############################################################
+#===========
+#PE count
+#===========
+rects <- data.frame(x=1:6, 
+                     colors = c("#ffd700" ,
+                                "#f0e68c" ,
+
+                                "#da70d6" ,
+                                "#ff1493" ,
+                                
+                                "#f8766d" ,
+                                "#00BFC4") 
+                     )
+
+rects$text =  c("-ve Lig"
+                , "+ve Lig"
+                
+                , "-ve PPI2"
+                , "+ve PPI2"
+
+                , "-ve stability"
+                , "+ve stability"
+)
+
+cell1 = table(str_df_plot_cols$pe_effect_outcome)[["DD_lig"]]
+cell2 = 0
+
+#cell3 = table(str_df_plot_cols$pe_effect_outcome)[["DD_nucleic_acid"]]
+#cell4 = table(str_df_plot_cols$pe_effect_outcome)[["SS_nucleic_acid"]]
+
+cell5 = table(str_df_plot_cols$pe_effect_outcome)[["DD_ppi2"]]
+cell6 = table(str_df_plot_cols$pe_effect_outcome)[["SS_ppi2"]]
+
+cell7 = table(str_df_plot_cols$pe_effect_outcome)[["DD_stability"]]
+cell8 = table(str_df_plot_cols$pe_effect_outcome)[["SS_stability"]]
+
+
+#rects$numbers = c(38, 0, 22, 9, 108, 681) #for embb
+rects$numbers = c(cell1, cell2,
+                  #cell3, cell4,
+                  cell5, cell6,
+                  cell7, cell8)
+
+rects$num_labels = paste0("n=", rects$numbers)
+
+rects
+#------
+# Plot
+#------
+#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
+peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
+  geom_tile(width = 1, height = 1) + # make square tiles
+  geom_text(color = "black", size = 1.7) + # add white text in the middle
+  scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
+  coord_fixed() + # make sure tiles are square
+  coord_flip()+ scale_x_reverse() +
+  # theme_void() # remove any axis markings
+  theme_nothing() # remove any axis markings
+peP
+
+#------
+# Plot: this one is better
+#------
+peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
+  geom_tile() + # make square tiles
+  geom_text(color = "black", size = 1.6) + # add white text in the middle
+  scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
+  coord_fixed() + # make sure tiles are square
+  theme_nothing() # remove any axis markings
+peP2
+
+########################################################
+# From: script sensitivity_count per gene
+#===============================
+# Sensitivity count: SITE
+#===============================
+#--------
+# embb
+#--------
+#rsc = 54
+#ccc = 46
+#ssc = 470
+
+rsc = site_Rc; rsc
+ccc = site_Cc; ccc
+ssc = site_Sc; ssc
+
+rect_rs_siteC <- data.frame(x=1:3,
+                    colors = c("red",
+                               "purple",
+                               "blue")
+                    )
+                               
+rect_rs_siteC                          
+rect_rs_siteC$text = c("Resistant",
+                       "Common",
+                       "Sensitive")
+
+rect_rs_siteC$numbers = c(rsc,ccc,ssc)
+rect_rs_siteC$num_labels = paste0("n=", rect_rs_siteC$numbers)
+rect_rs_siteC
+
+#------
+# Plot
+#------
+sens_siteP = ggplot(rect_rs_siteC, aes(x, y = 0,
+                                       fill = colors,
+                                       label = num_labels
+                                       #,label = paste0(text,"\n", num_labels)
+                                       )) +
+  geom_tile(width = 1, height = 1) +
+  #geom_text(color = "black", size = 1.7) +
+  geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) + 
+  scale_fill_identity(guide = "none") + 
+  coord_fixed()+
+  theme_nothing() # remove any axis markings
+sens_siteP
+
+################################################################
+#===============================
+# Sensitivity count: Mutations
+#===============================
+table(sensP_df$sensitivity)
+muts_Rc = table(sensP_df$sensitivity)[["R"]]
+muts_Sc = table(sensP_df$sensitivity)[["S"]]
+rect_sens <- data.frame(x=1:2,
+                              colors = c("red",
+                                         "blue")
+                        )
+
+rect_sens$text = c("Resistant",
+                       "Sensitive")
+rect_sens$numbers = c(muts_Rc,muts_Sc)
+rect_sens$num_labels = paste0("n=", rect_sens$numbers)
+rect_sens
+#------
+# Plot
+#------
+sensP = ggplot(rect_sens, aes(x, y = 0,
+                              fill = colors,
+                              label = paste0(text,"\n", num_labels))) +
+  geom_tile(width = 1, height = 1) +
+  #geom_text(color = "black", size = 1.7) +
+  geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) + 
+  scale_fill_identity(guide = "none") + 
+  coord_fixed()+
+  theme_nothing() # remove any axis markings
+sensP
+
+sensP2 = sensP + 
+   coord_flip() + scale_x_reverse()
+sensP2
+
+

scripts/plotting/plotting_thesis/alr/prominent_effects_alr.R

@@ -0,0 +1,331 @@
+########################################################
+pos_colname = "position"
+
+#-------------
+# from ~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R
+#-------------
+length(all_stability_cols); length(raw_stability_cols)
+length(scaled_stability_cols); length(outcome_stability_cols)
+length(affinity_dist_colnames)
+
+
+static_cols =  c("mutationinformation",
+                 #"position",
+                 pos_colname,
+                 "sensitivity")
+
+other_cols_all = c(scaled_stability_cols, scaled_affinity_cols, affinity_dist_colnames)
+
+#omit avg cols and foldx_scaled_signC cols
+other_cols =  other_cols_all[grep("avg", other_cols_all, invert = T)]
+other_cols = other_cols[grep("foldx_scaled_signC",other_cols, invert = T )]
+other_cols
+
+cols_to_extract =  c(static_cols, other_cols)
+cat("\nExtracting cols:", cols_to_extract)
+expected_ncols = length(static_cols) + length(other_cols)
+expected_ncols
+
+str_df = merged_df3[, cols_to_extract]
+
+if (ncol(str_df) == expected_ncols){
+  cat("\nPASS: successfully extracted cols for calculating prominent effects")
+}else{
+  stop("\nAbort: Could not extract cols for calculating prominent effects")
+}
+
+#=========================
+# Masking affinity columns
+#=========================
+# First make values for affinity cols 0 when their corresponding dist >10
+head(str_df)
+
+# replace in place affinity values >10
+str_df[str_df["ligand_distance"]>10,"affinity_scaled"]=0
+str_df[str_df["ligand_distance"]>10,"mmcsm_lig_scaled"]=0
+
+#ppi2 gene: replace in place ppi2 affinity values where ppi2 dist >10
+if (tolower(gene)%in%geneL_ppi2){
+  str_df[str_df["interface_dist"]>10,"mcsm_ppi2_scaled"]=0
+} 
+  
+# na gene: replace in place na affinity values where na dist >10
+if (tolower(gene)%in%geneL_na){
+  str_df[str_df["nca_distance"]>10,"mcsm_na_scaled"]=0
+}
+
+colnames(str_df)
+head(str_df)
+
+scaled_cols_tc = other_cols[grep("scaled", other_cols)]
+
+
+################################################
+#===============
+# whole df
+#===============
+give_col=function(x,y,df=str_df){
+  df[df[[pos_colname]]==x,y]
+}
+
+for (i in unique(str_df[[pos_colname]]) ){
+  print(i)
+  #cat(length(unique(str_df[[pos_colname]])))
+  
+  biggest     = max(abs(give_col(i,scaled_cols_tc)))
+  
+  str_df[str_df[[pos_colname]]==i,'abs_max_effect'] = biggest
+  str_df[str_df[[pos_colname]]==i,'effect_type']= names(
+    give_col(i,scaled_cols_tc)[which(
+      abs(
+        give_col(i,scaled_cols_tc)
+      ) == biggest, arr.ind=T
+    )[, "col"]])[1]
+  
+  effect_name = unique(str_df[str_df[[pos_colname]]==i,'effect_type'])#[1] # pick first one in case we have multiple exact values
+  
+  # get index/rowname for value of max effect, and then use it to get the original sign
+  # here
+  #ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
+  ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c(pos_colname,effect_name)][effect_name])== biggest, arr.ind=T))
+  
+  str_df[str_df[[pos_colname]]==i,'effect_sign'] = sign(str_df[effect_name][ind,])[1]
+}
+
+# ends with suffix 2 if dups
+str_df$effect_type = sub("\\.[0-9]+", "", str_df$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
+colnames(str_df)
+table(str_df$effect_type)
+
+# check
+str_df_check = str_df[str_df[[pos_colname]]%in%c(24, 32, 160, 303, 334),]
+
+#================
+# for Plots
+#================
+str_df_short = str_df[, c("mutationinformation",
+                          #"position",
+                          pos_colname,
+                          "sensitivity"
+                          , "effect_type"
+                          , "effect_sign")]
+
+table(str_df_short$effect_type)
+table(str_df_short$effect_sign)
+str(str_df_short)
+
+# assign pe outcome
+str_df_short$pe_outcome = ifelse(str_df_short$effect_sign<0, "DD", "SS")
+table(str_df_short$pe_outcome )
+table(str_df_short$effect_sign)
+
+#==============
+# group effect type:
+# lig, ppi2, nuc. acid, stability
+#==============
+affcols = c("affinity_scaled",  "mmcsm_lig_scaled")
+ppi2_cols = c("mcsm_ppi2_scaled")
+
+#lig 
+table(str_df_short$effect_type)
+str_df_short$effect_grouped = ifelse(str_df_short$effect_type%in%affcols
+                                     , "lig"
+                                     , str_df_short$effect_type)
+table(str_df_short$effect_grouped)
+
+#ppi2
+str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%ppi2_cols
+                                     , "ppi2"
+                                     , str_df_short$effect_grouped)
+table(str_df_short$effect_grouped)
+
+
+#stability
+str_df_short$effect_grouped = ifelse(!str_df_short$effect_grouped%in%c("lig",
+                                                                       "ppi2" 
+                                                                       )
+                                     , "stability"
+                                     , str_df_short$effect_grouped)
+
+table(str_df_short$effect_grouped)
+
+# create a sign as well
+str_df_short$pe_effect_outcome = paste0(str_df_short$pe_outcome, "_"
+                                     , str_df_short$effect_grouped)
+
+table(str_df_short$pe_effect_outcome)
+
+#####################################################################
+# Chimera: for colouring
+####################################################################
+
+#-------------------------------------
+# get df with unique position
+#--------------------------------------
+#data[!duplicated(data$x), ]   
+str_df_plot = str_df_short[!duplicated(str_df[[pos_colname]]),]
+
+if (nrow(str_df_plot) == length(unique(str_df[[pos_colname]]))){
+  cat("\nPASS: successfully extracted df with unique positions")
+}else{
+  stop("\nAbort: Could not extract df with unique positions")
+}
+
+#-------------------------------------
+# generate colours for effect types
+#--------------------------------------
+str_df_plot_cols = str_df_plot[, c(pos_colname,
+                                   "sensitivity",
+                                   "pe_outcome",
+                                   "effect_grouped",
+                                   "pe_effect_outcome")]
+head(str_df_plot_cols)
+
+# colour intensity based on sign
+#str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$effect_sign<0, "bright", "light")
+str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$pe_outcome=="DD", "bright", "light")
+
+table(str_df_plot_cols$colour_hue); table(str_df_plot$pe_outcome)
+head(str_df_plot_cols)
+
+# colour based on effect
+table(str_df_plot_cols$pe_effect_outcome)
+
+# colors = c("#ffd700" #gold
+#            , "#f0e68c" #khaki
+#            , "#da70d6"# orchid
+#            , "#ff1493"# deeppink
+#            , "#a0522d" #sienna
+#            , "#d2b48c" # tan
+#            , "#00BFC4" #, "#007d85" #blue
+#            , "#F8766D" )# red
+
+pe_colour_map = c("DD_lig"         = "#ffd700"   # gold
+                , "SS_lig"         = "#f0e68c"  # khaki
+             
+                , "DD_nucleic_acid"= "#a0522d"  # sienna
+                , "SS_nucleic_acid"= "#d2b48c"  # tan
+                
+                , "DD_ppi2"        = "#da70d6"  # orchid
+                , "SS_ppi2"        = "#ff1493"  # deeppink
+
+                , "DD_stability"   = "#f8766d"  # red
+                , "SS_stability"   = "#00BFC4") # blue
+        
+#unlist(d[c('a', 'a', 'c', 'b')], use.names=FALSE)
+
+#map the colours
+str_df_plot_cols$colour_map= unlist(map(str_df_plot_cols$pe_effect_outcome
+                                 ,function(x){pe_colour_map[[x]]}
+                                 ))
+head(str_df_plot_cols$colour_map)
+table(str_df_plot_cols$colour_map)
+table(str_df_plot_cols$pe_effect_outcome)
+
+# str_df_plot_cols$colours = paste0(str_df_plot_cols$colour_hue
+#                                   , "_"
+#                                   , str_df_plot_cols$colour_map)
+# head(str_df_plot_cols$colours)
+# table(str_df_plot_cols$colours)
+# 
+# 
+# class(str_df_plot_cols$colour_map)
+# str(str_df_plot_cols)
+
+# sort by colour
+head(str_df_plot_cols)
+str_df_plot_cols = str_df_plot_cols[order(str_df_plot_cols$colour_map), ]
+head(str_df_plot_cols)
+
+#======================================
+# write file with prominent effects
+#======================================
+outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
+write.csv(str_df_plot_cols, paste0(outdir_images, tolower(gene), "_prominent_effects.csv"))
+
+################################
+# printing for chimera
+###############################
+chain_suffix = ".A"
+str_df_plot_cols$pos_chain = paste0(str_df_plot_cols[[pos_colname]], chain_suffix)
+table(str_df_plot_cols$colour_map)
+table(str_df_plot_cols$pe_effect_outcome)
+
+#===================================================
+#-------------------
+# Ligand Affinity
+#-------------------
+# -ve Lig Aff
+dd_lig        = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_lig",]
+if (nrow(dd_lig) == table(str_df_plot_cols$pe_effect_outcome)[['DD_lig']]){
+  dd_lig_pos  = dd_lig[[pos_colname]]
+}else{
+  stop("Abort: DD affinity colour numbers mismtatch")
+}
+
+# +ve Lig Aff
+ss_lig            = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_lig",]
+if (!empty(ss_lig)){
+  if (nrow(ss_lig)  == table(str_df_plot_cols$pe_effect_outcome)[['SS_lig']]){
+    ss_lig_pos      = ss_lig[[pos_colname]]
+  }else{
+    stop("Abort: SS affinity colour numbers mismtatch")
+  } 
+  #put in chimera cmd
+  paste0(dd_lig_pos, chain_suffix)
+  paste0(ss_lig_pos, chain_suffix)
+}
+
+
+
+
+#===================================================
+#-------------------
+# PPI2 Affinity
+#-------------------
+# -ve PPI2
+dd_ppi2           =  str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_ppi2",]
+if (nrow(dd_ppi2) == table(str_df_plot_cols$pe_effect_outcome)[['DD_ppi2']]){
+  dd_ppi2_pos     =  dd_ppi2[[pos_colname]]
+}else{
+  stop("Abort: DD PPI2 colour numbers mismtatch")
+}
+
+# +ve PPI2
+ss_ppi2            = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_ppi2",]
+if (nrow(ss_ppi2)  == table(str_df_plot_cols$pe_effect_outcome)[['SS_ppi2']]){
+  ss_ppi2_pos      = ss_ppi2[[pos_colname]]
+}else{
+  stop("Abort: SS PPI2 colour numbers mismtatch")
+}
+
+#put in chimera cmd
+paste0(dd_ppi2_pos,chain_suffix)
+paste0(ss_ppi2_pos,chain_suffix)
+
+#=========================================================
+#------------------------
+# Stability
+#------------------------
+# -ve Stability
+dd_stability           =  str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_stability",]
+if (nrow(dd_stability) == table(str_df_plot_cols$pe_effect_outcome)[['DD_stability']]){
+  dd_stability_pos     =  dd_stability[[pos_colname]]
+}else{
+  stop("Abort: DD Stability colour numbers mismtatch")
+}
+
+# +ve Stability
+ss_stability            = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_stability",]
+if (nrow(ss_stability)  == table(str_df_plot_cols$pe_effect_outcome)[['SS_stability']]){
+  ss_stability_pos      = ss_stability[[pos_colname]]
+}else{
+  stop("Abort: SS Stability colour numbers mismtatch")
+}
+
+#put in chimera cmd
+# stabiliting first as it has less numbers
+paste0(ss_stability_pos, chain_suffix)
+paste0(dd_stability_pos, chain_suffix)
+####################################################################
+

scripts/plotting/plotting_thesis/alr/sensitivity_count_alr.R

@@ -0,0 +1,65 @@
+#=========================
+# Count Sensitivity
+# Mutations and positions
+#=========================
+pos_colname_c ="position"
+
+sensP_df = merged_df3[,c("mutationinformation",
+                         #"position",
+                         pos_colname_c,
+                         "sensitivity")]
+
+head(sensP_df)
+table(sensP_df$sensitivity)
+
+#---------------
+# Total unique positions
+#----------------
+tot_mut_pos = length(unique(sensP_df[[pos_colname_c]]))
+cat("\nNo of Tot muts sites:", tot_mut_pos)
+
+# resistant mut pos
+sens_site_allR = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="R"]
+sens_site_UR   = unique(sens_site_allR)
+length(sens_site_UR)
+
+# Sensitive mut pos
+sens_site_allS = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="S"]
+sens_site_US = unique(sens_site_allS)
+length(sens_site_UR)
+
+#---------------
+# Common Sites
+#----------------
+common_pos = intersect(sens_site_UR,sens_site_US)
+site_Cc = length(common_pos)
+cat("\nNo of Common sites:", site_Cc
+    , "\nThese are:", common_pos)
+
+#---------------
+# Resistant muts
+#----------------
+site_R = sens_site_UR[!sens_site_UR%in%common_pos]
+site_Rc = length(site_R)
+
+if ( length(sens_site_allR) == table(sensP_df$sensitivity)[['R']] ){
+  cat("\nNo of R muts:", length(sens_site_allR) 
+      , "\nNo. of R sites:",site_Rc
+      , "\nThese are:", site_R
+)
+}
+
+#---------------
+# Sensitive muts
+#----------------
+site_S = sens_site_US[!sens_site_US%in%common_pos]
+site_Sc = length(site_S)
+
+if ( length(sens_site_allS) == table(sensP_df$sensitivity)[['S']] ){
+  cat("\nNo of S muts:", length(sens_site_allS) 
+      , "\nNo. of S sites:", site_Sc
+      , "\nThese are:", site_S)
+}
+
+#########################
+

scripts/plotting/plotting_thesis/gid/basic_barplots_gid.R

@@ -0,0 +1,363 @@
+#!/usr/bin/env Rscript   
+#########################################################
+# TASK: Barplots
+# basic barplots with outcome
+# basic barplots with frequency of count of mutations
+#########################################################
+#=============
+# Data: Input
+#==============
+#source("~/git/LSHTM_analysis/config/gid.R")
+#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
+#cat("\nSourced plotting cols as well:", length(plotting_cols))
+
+####################################################
+class(merged_df3)
+
+df3 = subset(merged_df3, select = -c(pos_count))
+
+#=======
+# output
+#=======
+outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
+cat("plots will output to:", outdir_images)
+
+##########################################################
+# blue, red bp
+sts = 8
+lts = 8
+ats = 8
+als = 8
+ltis = 8
+geom_ls = 2.2
+
+#pos_count
+subtitle_size = 8
+geom_ls_pc = 2.2
+leg_text_size = 8
+axis_text_size = 8
+axis_label_size = 8
+
+###########################################################
+#------------------------------
+# plot default sizes
+#------------------------------
+#=========================
+# Affinity outcome
+# check this var: outcome_cols_affinity
+# get from preformatting or put in globals
+#==========================
+DistCutOff
+LigDist_colname  # = "ligand_distance" # from globals 
+ppi2Dist_colname
+naDist_colname
+
+###########################################################
+# get plotting data within the distance
+df3_lig  = df3[df3[[LigDist_colname]]<DistCutOff,]
+df3_ppi2 = df3[df3[[ppi2Dist_colname]]<DistCutOff,]
+df3_na   = df3[df3[[naDist_colname]]<DistCutOff,]
+common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
+
+#------------------------------
+# barplot for ligand affinity:
+# <10 Ang of ligand
+#------------------------------
+mLigP = stability_count_bp(plotdf = df3_lig
+               , df_colname = "ligand_outcome"
+               #, leg_title  = "mCSM-lig"
+               #, bp_plot_title = paste(common_bp_title, "ligand")
+               , yaxis_title = "Number of nsSNPs"
+               , leg_position = "none"
+               , subtitle_text = "mCSM\nLig"
+               , bar_fill_values = c("#F8766D", "#00BFC4")
+               , subtitle_colour= "black"
+               , sts = sts
+               , lts = lts
+               , ats = ats
+               , als = als
+               , ltis = ltis
+               , geom_ls = geom_ls
+               )
+mLigP
+#------------------------------
+# barplot for ligand affinity:
+# <10 Ang of ligand
+# mmCSM-lig: will be the same no. of sites but the effect will be different
+#------------------------------
+mmLigP = stability_count_bp(plotdf = df3_lig
+                   , df_colname = "mmcsm_lig_outcome"
+                   #, leg_title  = "mmCSM-lig"
+                   #, label_categories = labels_mmlig
+                   #, bp_plot_title = paste(common_bp_title, "ligand")
+                   
+                   , yaxis_title = ""
+                   , leg_position = "none"
+                   , subtitle_text = "mmCSM\nLig"
+                   , bar_fill_values = c("#F8766D", "#00BFC4")
+                   , subtitle_colour= "black"
+                   , sts = sts
+                   , lts = lts
+                   , ats = ats
+                   , als = als
+                   , ltis = ltis
+                   , geom_ls = geom_ls
+                   )
+mmLigP
+#------------------------------
+# barplot for ppi2 affinity
+#  <10 Ang of interface
+#------------------------------
+if (tolower(gene)%in%geneL_ppi2){
+    ppi2P = stability_count_bp(plotdf = df3_ppi2
+                     , df_colname = "mcsm_ppi2_outcome"
+                     #, leg_title  = "mCSM-ppi2"
+                     #, label_categories = labels_ppi2
+                     #, bp_plot_title = paste(common_bp_title, "PP-interface")
+                     
+                     , yaxis_title = "Number of nsSNPs"
+                     , leg_position = "none"
+                     , subtitle_text = "mCSM\nPPI2"
+                     , bar_fill_values = c("#F8766D", "#00BFC4")
+                     , subtitle_colour= "black"
+                     , sts = sts
+                     , lts = lts
+                     , ats = ats
+                     , als = als
+                     , ltis = ltis
+                     , geom_ls = geom_ls
+                     )
+  ppi2P
+}
+#----------------------------
+# barplot for ppi2 affinity
+#  <10 Ang of interface
+#------------------------------
+if (tolower(gene)%in%geneL_na){
+  nca_distP = stability_count_bp(plotdf = df3_na
+                             , df_colname = "mcsm_na_outcome"
+                             #, leg_title  = "mCSM-NA"
+                             #, label_categories = 
+                             #, bp_plot_title = paste(common_bp_title, "Dist to NA")
+                             
+                             , yaxis_title = "Number of nsSNPs"
+                             , leg_position = "none"
+                             , subtitle_text = "mCSM\nNA"
+                             , bar_fill_values = c("#F8766D", "#00BFC4")
+                             , subtitle_colour= "black"
+                             , sts = sts
+                             , lts = lts
+                             , ats = ats
+                             , als = als
+                             , ltis = ltis
+                             , geom_ls = geom_ls
+  )
+  nca_distP
+}
+
+#####################################################################
+# ------------------------------
+# bp site site count: mCSM-lig
+# < 10 Ang ligand
+# ------------------------------
+common_bp_title = paste0("Sites <", DistCutOff, angstroms_symbol)
+
+posC_lig = site_snp_count_bp(plotdf = df3_lig
+                  , df_colname = "position"
+                  , xaxis_title = "Number of nsSNPs"
+                  , yaxis_title = "Number of Sites"
+                  , subtitle_colour = "chocolate4"
+                  , subtitle_text = ""
+                  , subtitle_size = subtitle_size
+                  , geom_ls = geom_ls_pc
+                  , leg_text_size = leg_text_size
+                  , axis_text_size = axis_text_size
+                  , axis_label_size = axis_label_size)
+
+posC_lig
+#------------------------------
+# bp site site count: ppi2
+# < 10 Ang interface
+#------------------------------
+if (tolower(gene)%in%geneL_ppi2){
+  posC_ppi2 = site_snp_count_bp(plotdf = df3_ppi2
+                    , df_colname = "position"
+                    , xaxis_title = "Number of nsSNPs"
+                    , yaxis_title = "Number of Sites"
+                    , subtitle_colour = "chocolate4"
+                    , subtitle_text = ""
+                    , subtitle_size = subtitle_size
+                    , geom_ls = geom_ls_pc
+                    , leg_text_size = leg_text_size
+                    , axis_text_size = axis_text_size
+                    , axis_label_size = axis_label_size)
+  posC_ppi2
+}
+
+#------------------------------
+# bp site site count: NCA dist
+# < 10 Ang nca
+#------------------------------
+if (tolower(gene)%in%geneL_na){
+  posC_nca = site_snp_count_bp(plotdf = df3_na
+                                , df_colname = "position"
+                                , xaxis_title = "Number of nsSNPs"
+                                , yaxis_title = "Number of Sites"
+                                , subtitle_colour = "chocolate4"
+                                , subtitle_text = ""
+                               , subtitle_size = subtitle_size
+                               , geom_ls = geom_ls_pc
+                               , leg_text_size = leg_text_size
+                               , axis_text_size = axis_text_size
+                               , axis_label_size = axis_label_size)
+  posC_nca
+}
+#===============================================================
+#------------------------------
+# bp site site count: ALL
+# <10 Ang ligand
+#------------------------------
+posC_all = site_snp_count_bp(plotdf = df3
+                             , df_colname = "position"
+                             , xaxis_title = "Number of nsSNPs"
+                             , yaxis_title = "Number of Sites"
+                             , subtitle_colour = "chocolate4"
+                             , subtitle_text = "All mutations sites"
+                             , subtitle_size = subtitle_size
+                             , geom_ls = geom_ls_pc
+                             , leg_text_size = leg_text_size
+                             , axis_text_size = axis_text_size
+                             , axis_label_size = axis_label_size)
+posC_all
+##################################################################
+consurfP = stability_count_bp(plotdf = df3
+                              , df_colname = "consurf_outcome"
+                              #, leg_title = "ConSurf"
+                              #, label_categories = labels_consurf
+                              , yaxis_title = "Number of nsSNPs"
+                              , leg_position = "top"
+                              , subtitle_text = "ConSurf"
+                              , bar_fill_values = consurf_colours # from globals
+                              , subtitle_colour= "black"
+                              , sts = sts
+                              , lts = lts
+                              , ats = ats
+                              , als = als
+                              , ltis = ltis
+                              , geom_ls = geom_ls)
+
+consurfP
+
+##############################################################
+sts_so = 10
+lts_so = 10
+ats_so = 10
+als_so = 10
+ltis_so = 10
+geom_ls_so = 2.5
+#===================
+# Stability
+#===================
+# duetP
+duetP = stability_count_bp(plotdf = df3
+                           , df_colname = "duet_outcome"
+                           , leg_title = "mCSM-DUET"
+                           #, label_categories = labels_duet
+                           , yaxis_title = "Number of nsSNPs"
+                           , leg_position = "none"
+                           , subtitle_text = "mCSM-DUET"
+                           , bar_fill_values = c("#F8766D", "#00BFC4")
+                           , subtitle_colour= "black"
+                           , sts = sts_so
+                           , lts = lts_so
+                           , ats = ats_so
+                           , als = als_so
+                           , ltis = ltis_so
+                           , geom_ls = geom_ls_so)
+duetP
+
+# foldx
+foldxP = stability_count_bp(plotdf = df3
+                            , df_colname = "foldx_outcome"
+                            #, leg_title = "FoldX"
+                            #, label_categories = labels_foldx
+                            , yaxis_title = ""
+                            , leg_position = "none"
+                            , subtitle_text = "FoldX"
+                            , bar_fill_values = c("#F8766D", "#00BFC4")
+                            , sts = sts_so
+                            , lts = lts_so
+                            , ats = ats_so
+                            , als = als_so
+                            , ltis = ltis_so
+                            , geom_ls = geom_ls_so)
+foldxP
+
+# deepddg
+deepddgP = stability_count_bp(plotdf = df3
+                              , df_colname = "deepddg_outcome"
+                              #, leg_title = "DeepDDG"
+                              #, label_categories = labels_deepddg
+                              , yaxis_title = ""
+                              , leg_position = "none"
+                              , subtitle_text = "DeepDDG"
+                              , bar_fill_values = c("#F8766D", "#00BFC4")
+                              , sts = sts_so
+                              , lts = lts_so
+                              , ats = ats_so
+                              , als = als_so
+                              , ltis = ltis_so
+                              , geom_ls = geom_ls_so)
+deepddgP
+
+# deepddg
+dynamut2P = stability_count_bp(plotdf = df3
+                               , df_colname = "ddg_dynamut2_outcome"
+                               #, leg_title = "Dynamut2"
+                               #, label_categories = labels_ddg_dynamut2_outcome
+                               , yaxis_title = ""
+                               , leg_position = "none"
+                               , subtitle_text = "Dynamut2"
+                               , bar_fill_values = c("#F8766D", "#00BFC4")
+                               , sts = sts_so
+                               , lts = lts_so
+                               , ats = ats_so
+                               , als = als_so
+                               , ltis = ltis_so
+                               , geom_ls = geom_ls_so)
+dynamut2P
+
+# provean
+proveanP = stability_count_bp(plotdf = df3
+                              , df_colname = "provean_outcome"
+                              #, leg_title = "PROVEAN"
+                              #, label_categories = labels_provean
+                              , yaxis_title = "Number of nsSNPs"
+                              , leg_position = "none" # top
+                              , subtitle_text = "PROVEAN"
+                              , bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
+                              , sts = sts_so
+                              , lts = lts_so
+                              , ats = ats_so
+                              , als = als_so
+                              , ltis = ltis_so
+                              , geom_ls = geom_ls_so)
+proveanP
+
+# snap2
+snap2P = stability_count_bp(plotdf = df3
+                            , df_colname = "snap2_outcome"
+                            #, leg_title = "SNAP2"
+                            #, label_categories = labels_snap2
+                            , yaxis_title = ""
+                            , leg_position = "none" # top
+                            , subtitle_text = "SNAP2"
+                            , bar_fill_values = c("#D01C8B", "#F1B6DA") # light pink and deep
+                            , sts = sts_so
+                            , lts = lts_so
+                            , ats = ats_so
+                            , als = als_so
+                            , ltis = ltis_so
+                            , geom_ls = geom_ls_so)
+snap2P
+#####################################################################################

scripts/plotting/plotting_thesis/gid/basic_barplots_gid_layout.R

@@ -1,17 +1,38 @@
-duetP
+#=============
-foldxP
+# Data: Input
-deepddgP
+#==============
-dynamut2P
+source("~/git/LSHTM_analysis/config/gid.R")
-proveanP
+source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
-snap2P
+#cat("\nSourced plotting cols as well:", length(plotting_cols))
+
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/basic_barplots_gid.R")
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/pe_sens_site_count_gid.R")
+
+
+if ( tolower(gene)%in%c("gid") ){
+  cat("\nPlots available for layout are:")
+  
+  duetP
+  foldxP
+  deepddgP
+  dynamut2P
+  proveanP
+  snap2P
+  
+  mLigP
+  mmLigP
+  posC_lig
+
+  nca_distP
+  posC_nca
+  
+  peP2
+  sens_siteP
+  peP   # not used
+  sensP # not used
+}
+
 
-mLigP
-mmLigP
-posC_lig
-ppi2P
-posC_nca
-peP
-sensP
 #========================
 # Common title settings
 #=========================
@@ -118,9 +139,8 @@ dev.off()
 
 #################################################################
 #=======================================
-# Affinity barplots: COMBINE ALL three
+# Affinity barplots: COMBINE ALL four
 #========================================
-
 ligT = paste0(common_bp_title, " ligand")
 lig_affT = ggdraw() +
   draw_label(
@@ -136,7 +156,7 @@ p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
                                            , nrow=2),
                         cowplot::plot_grid(mLigP, mmLigP, posC_lig
                                            , nrow = 1
-                                           , rel_widths = c(1,1,1.8)
+                                           , rel_widths = c(1,0.65,1.8)
                                            , align = "h"),
                         nrow = 2,
                         rel_heights = c(1,8)
@@ -144,7 +164,7 @@ p1 = cowplot::plot_grid(cowplot::plot_grid(lig_affT
 )
 p1
 ###########################################################
-ncaT = paste0(common_bp_title, " Dist-NA")
+ncaT = paste0(common_bp_title, " Nucleic Acid")
 nca_affT = ggdraw() +
   draw_label(
     ncaT,
@@ -159,7 +179,7 @@ p2 = cowplot::plot_grid(cowplot::plot_grid(nca_affT
                                            , nrow=2),
                         cowplot::plot_grid(nca_distP, posC_nca
                                              , nrow = 1
-                                             , rel_widths = c(1.2,1.8)
+                                             , rel_widths = c(1,1.8)
                                              , align = "h"),
                         nrow = 2,
                         rel_heights = c(1,8)
@@ -176,7 +196,7 @@ peT_allT = ggdraw() +
     size = 8
   )
 
-p3 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
+p4 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
                                            , rel_widths = c(1,3),axis = "lr"),
                         cowplot::plot_grid(
                           peP2, posC_all,
@@ -185,13 +205,12 @@ p3 = cowplot::plot_grid(cowplot::plot_grid(peT_allT, nrow = 2
                           align = "v",
                           axis = "lr",
                           rel_heights = c(1,8)
-                          ),
+                        ),
                         rel_heights = c(1,18),
                         nrow = 2,axis = "lr")
-p3
+p4
-#===============
+
-# Final combine
+#### Combine p1+p2+p4 ####
-#===============
 w = 11.79
 h = 3.5
 mut_impact_CLP =  paste0(outdir_images
@@ -202,21 +221,46 @@ mut_impact_CLP =  paste0(outdir_images
 print(paste0("plot filename:", mut_impact_CLP))
 png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
 
-cowplot::plot_grid(p1, p2
+cowplot::plot_grid(p1,
-                   #, p3
+                   p2,
-                   , nrow = 1
+                   p4,
-                   , labels = "AUTO"
+                   nrow = 1,
-                   , label_size = 12
+                   labels = "AUTO",
-                   , rel_widths = c(3,2,2)
+                   label_size = 12,
+                   rel_widths = c(2.5,2,2)
                    #, rel_heights = c(1)
-                   )
+)
 
 dev.off()
+w = 11.79
+h = 3.5
+mut_impact_CLP =  paste0(outdir_images
+                         ,tolower(gene)
+                         ,"_mut_impactCLP.png")
+
+#svg(affP, width = 20, height = 5.5)
+print(paste0("plot filename:", mut_impact_CLP))
+png(mut_impact_CLP, units = "in", width = w, height = h, res = 300 )
+
+cowplot::plot_grid(p1,
+                   p2,
+                   p4,
+                   nrow = 1,
+                   labels = "AUTO",
+                   label_size = 12,
+                   rel_widths = c(2.5,2,2)
+                   #, rel_heights = c(1)
+)
+
+dev.off()
+
+
+
 ##################################################
 sensP
 consurfP
 #=================
-# Combine sensitivity + ConSurf
+#### Combine sensitivity + ConSurf ####
 # or ConSurf
 #=================
 w = 3
@@ -263,7 +307,7 @@ sens_siteCLP =  paste0(outdir_images
                   ,"_sens_siteC_tile.png")
 
 print(paste0("plot filename:", sens_siteCLP))
-png(sens_siteCLP, units = "in", width = 1, height = 1, res = 300 )
+png(sens_siteCLP, units = "in", width = 1.2, height = 1, res = 300 )
 sens_siteP
 dev.off()
 

scripts/plotting/plotting_thesis/gid/dm_om_plots_gid.R

@@ -1,320 +0,0 @@
-#################
-# Numbers
-##################
-#all_dm_om_df = dm_om_wf_lf_data(df = merged_df3, gene = gene)
-all_dm_om_df = dm_om_wf_lf_data(df = 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$param_type)
-
-genomicsP = lf_bp2(genomics_dataP
-                   #, p_title = ""
-                   , violin_quantiles = c(0.5), monochrome = F)
-
-genomicsP
-
-#check
-wilcox.test(wf_dist_genP$`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)
-
-
-wilcox.test(wf_dist_genP$`NA Dist(Å)`[wf_dist_genP$mutation_info_labels=="R"]
-            , wf_dist_genP$`NA Dist(Å)`[wf_dist_genP$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), 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$param_type)
-
-mcsmligP = lf_bp2(lf_mcsm_ligP
-                  #, p_title = ""
-                  , violin_quantiles = c(0.5), monochrome = F
-                  , dot_transparency = 1)
-
-
-#=================
-# Plot: mmCSM-lig2
-#=================
-lf_mmcsm_lig2P   = all_dm_om_df[['lf_mmcsm_lig2']]
-#lf_mmcsm_lig2P  = lf_mmcsm_lig2P[!lf_mmcsm_lig2P$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 = 1)
-
-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$param_type)
-  
-  mcsmppi2P = lf_bp2(lf_mcsm_ppi2P
-                     #, p_title = ""
-                     , violin_quantiles = c(0.5), monochrome = F
-                     , dot_transparency = 1)
-  
-}
-
-#==============
-# Plot: mCSM-NA
-#==============
-if (tolower(gene)%in%geneL_na){
-  lf_mcsm_naP   = all_dm_om_df[['lf_mcsm_na']]
-  #lf_mcsm_naP  = lf_mcsm_na[!lf_mcsm_na$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 = 1)
-  
-}
-
-######################################
-# 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()
-
-

scripts/plotting/plotting_thesis/gid/dm_om_plots_gid_ayout.R

@@ -0,0 +1,176 @@
+# source dm_om_plots.R
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/dm_om_plots.R")
+
+##### plots to combine ####
+duetP
+foldxP
+deepddgP
+dynamut2P
+genomicsP
+consurfP
+proveanP
+snap2P
+mcsmligP
+mcsmlig2P
+mcsmnaP
+
+# 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_na
+                         , nrow = 1
+                         , labels = c("F", "G")
+                         , label_size = my_label_size)
+                       , ncol = 1
+                       , rel_heights = relH_tp
+    ),
+    nrow = 1,
+    rel_widths = c(2/7, 0.1/7, 0.5/7, 0.1/7,  1/7)
+  )
+  
+  #----------------
+  # 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
+                                          , mcsmnaP
+                                          , nrow = 1
+                                          , labels = c("K", "L", "M")
+                                          , 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()

scripts/plotting/plotting_thesis/gid/gg_pairs_all_gid.R

@@ -14,9 +14,8 @@ my_gg_pairs=function(plot_df, plot_title
                               title="ρ",
                               digits=2,
                               justify_labels = "centre",
-                              #title_args=c(colour="black"),
+                              title_args=list(size=tt_args_size, colour="black"),#2.5
-                              title_args=c(size=tt_args_size),#2.5
+                              group_args=list(size=gp_args_size)#2.5
-                              group_args=c(size=gp_args_size)#2.5
             )
           ),
           lower = list(
@@ -73,7 +72,7 @@ if (tolower(gene)%in%geneL_ppi2){
 }
 
 if (tolower(gene)%in%geneL_na){
-  corr_affinity_df[corr_affinity_df["NCA-Dist"]>DistCutOff,"mCSM-NA"]=0
+  corr_affinity_df[corr_affinity_df["NA-Dist"]>DistCutOff,"mCSM-NA"]=0
 }
 
 # count 0
@@ -97,6 +96,7 @@ 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
@@ -142,37 +142,62 @@ 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_corr_df_aff = my_gg_pairs(corr_df_aff
-                               plot_title="Affinity estimates",
+                               , plot_title="Affinity estimates"
-                               tt_args_size = 4,
+                               #, tt_args_size = 4
-                               gp_args_size = 4)
+                               #, gp_args_size = 4
+                               )
 
-#=============
+#### Combine plots #####
-# combine
+# #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()
 
-#png("/home/tanu/tmp/gg_pairs_all.png", height = 6, width=11.75, unit="in",res=300)
+#### Combine A ####
 png(paste0(outdir_images
            ,tolower(gene)
-           ,"_CorrAB.png"), height = 7, width=11.75, unit="in",res=300)
+           ,"_CorrA.png"), height =8, width=8, unit="in",res=300)
 
 cowplot::plot_grid(ggmatrix_gtable(plot_corr_df_ps),
-                   ggmatrix_gtable(plot_corr_df_cons),
+                   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_widths = c(1.5,1),
+                   #rel_heights = 1,1
-                   labels = "AUTO",
+                   labels = c("B", "C"),
                    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()

scripts/plotting/plotting_thesis/gid/gid_other_plots.R

@@ -0,0 +1,2 @@
+
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp_dist_layout.R")

scripts/plotting/plotting_thesis/gid/pe_sens_site_count_gid.R

@@ -0,0 +1,173 @@
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/prominent_effects_gid.R")
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/gid/sensitivity_count_gid.R")
+
+##############################################################
+#                         PE count
+#lig-- na--ppi2--stab
+# pe_colour_map = c("DD_lig"         = "#ffd700"   # gold
+#                   , "SS_lig"         = "#f0e68c"  # khaki
+#                   
+#                   , "DD_nucleic_acid"= "#a0522d"  # sienna
+#                   , "SS_nucleic_acid"= "#d2b48c"  # tan
+#                   
+#                   , "DD_ppi2"        = "#da70d6"  # orchid
+#                   , "SS_ppi2"        = "#ff1493"  # deeppink
+#                   
+#                   , "DD_stability"   = "#f8766d"  # red
+#                   , "SS_stability"   = "#00BFC4") # blue
+table(str_df_plot_cols$pe_effect_outcome)
+##############################################################
+#===========
+#PE count
+#===========
+rects <- data.frame(x=1:6, 
+                     colors = c("#ffd700" ,
+                                "#f0e68c" ,
+                                
+                                "#a0522d" ,
+                                "#d2b48c" ,
+                                
+                                "#f8766d" ,
+                                "#00BFC4") 
+                     )
+
+rects$text =  c("-ve Lig"
+                , "+ve Lig"
+                
+                , "-ve\nNuc.Acid"
+                , "+ve\nNuc.Acid"
+
+                , "-ve stability"
+                , "+ve stability"
+)
+
+cell1 = table(str_df_plot_cols$pe_effect_outcome)[["DD_lig"]]
+cell2 = 0
+
+cell3 = table(str_df_plot_cols$pe_effect_outcome)[["DD_nucleic_acid"]]
+cell4 = table(str_df_plot_cols$pe_effect_outcome)[["SS_nucleic_acid"]]
+
+#cell5 = table(str_df_plot_cols$pe_effect_outcome)[["DD_ppi2"]]
+#cell6 = table(str_df_plot_cols$pe_effect_outcome)[["SS_ppi2"]]
+
+cell7 = table(str_df_plot_cols$pe_effect_outcome)[["DD_stability"]]
+cell8 = table(str_df_plot_cols$pe_effect_outcome)[["SS_stability"]]
+
+
+#rects$numbers = c(38, 0, 22, 9, 108, 681) #for embb
+rects$numbers = c(cell1, cell2,
+                  cell3, cell4,
+                 # cell5, cell6,
+                  cell7, cell8)
+
+rects$num_labels = paste0("n=", rects$numbers)
+
+rects
+#------
+# Plot
+#------
+#https://stackoverflow.com/questions/47986055/create-a-rectangle-filled-with-text
+peP = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
+  geom_tile(width = 1, height = 1) + # make square tiles
+  geom_text(color = "black", size = 1.7) + # add white text in the middle
+  scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
+  coord_fixed() + # make sure tiles are square
+  coord_flip()+ scale_x_reverse() +
+  # theme_void() # remove any axis markings
+  theme_nothing() # remove any axis markings
+peP
+
+#------
+# Plot: this one is better
+#------
+peP2 = ggplot(rects, aes(x, y = 0, fill = colors, label = paste0(text,"\n", num_labels))) +
+  geom_tile() + # make square tiles
+  geom_text(color = "black", size = 1.6) + # add white text in the middle
+  scale_fill_identity(guide = "none") + # color the tiles with the colors in the data frame
+  coord_fixed() + # make sure tiles are square
+  theme_nothing() # remove any axis markings
+peP2
+
+########################################################
+# From: script sensitivity_count per gene
+#===============================
+# Sensitivity count: SITE
+#===============================
+#--------
+# embb
+#--------
+#rsc = 54
+#ccc = 46
+#ssc = 470
+
+rsc = site_Rc; rsc
+ccc = site_Cc; ccc
+ssc = site_Sc; ssc
+
+rect_rs_siteC <- data.frame(x=1:3,
+                    colors = c("red",
+                               "purple",
+                               "blue")
+                    )
+                               
+rect_rs_siteC                          
+rect_rs_siteC$text = c("Resistant",
+                       "Common",
+                       "Sensitive")
+
+rect_rs_siteC$numbers = c(rsc,ccc,ssc)
+rect_rs_siteC$num_labels = paste0("n=", rect_rs_siteC$numbers)
+rect_rs_siteC
+
+#------
+# Plot
+#------
+sens_siteP = ggplot(rect_rs_siteC, aes(x, y = 0,
+                                       fill = colors,
+                                       label = num_labels
+                                       #,label = paste0(text,"\n", num_labels)
+                                       )) +
+  geom_tile(width = 1, height = 1) +
+  #geom_text(color = "black", size = 1.7) +
+  geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) + 
+  scale_fill_identity(guide = "none") + 
+  coord_fixed()+
+  theme_nothing() # remove any axis markings
+sens_siteP
+
+################################################################
+#===============================
+# Sensitivity count: Mutations
+#===============================
+table(sensP_df$sensitivity)
+muts_Rc = table(sensP_df$sensitivity)[["R"]]
+muts_Sc = table(sensP_df$sensitivity)[["S"]]
+rect_sens <- data.frame(x=1:2,
+                              colors = c("red",
+                                         "blue")
+                        )
+
+rect_sens$text = c("Resistant",
+                       "Sensitive")
+rect_sens$numbers = c(muts_Rc,muts_Sc)
+rect_sens$num_labels = paste0("n=", rect_sens$numbers)
+rect_sens
+#------
+# Plot
+#------
+sensP = ggplot(rect_sens, aes(x, y = 0,
+                              fill = colors,
+                              label = paste0(text,"\n", num_labels))) +
+  geom_tile(width = 1, height = 1) +
+  #geom_text(color = "black", size = 1.7) +
+  geom_label(color = "black", size = 1.7,fill = "white", alpha=0.7) + 
+  scale_fill_identity(guide = "none") + 
+  coord_fixed()+
+  theme_nothing() # remove any axis markings
+sensP
+
+sensP2 = sensP + 
+   coord_flip() + scale_x_reverse()
+sensP2
+
+

scripts/plotting/plotting_thesis/gid/prominent_effects_gid.R

@@ -0,0 +1,341 @@
+#!/usr/bin/env Rscript       
+#source("~/git/LSHTM_analysis/config/gid.R")
+# get plotting dfs 
+#source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
+########################################################
+pos_colname = "position"
+
+#-------------
+# from ~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R
+#-------------
+length(all_stability_cols); length(raw_stability_cols)
+length(scaled_stability_cols); length(outcome_stability_cols)
+length(affinity_dist_colnames)
+
+
+static_cols =  c("mutationinformation",
+                 #"position",
+                 pos_colname,
+                 "sensitivity")
+
+other_cols_all = c(scaled_stability_cols, scaled_affinity_cols, affinity_dist_colnames)
+
+#omit avg cols and foldx_scaled_signC cols
+other_cols =  other_cols_all[grep("avg", other_cols_all, invert = T)]
+other_cols = other_cols[grep("foldx_scaled_signC",other_cols, invert = T )]
+other_cols
+
+cols_to_extract =  c(static_cols, other_cols)
+cat("\nExtracting cols:", cols_to_extract)
+expected_ncols = length(static_cols) + length(other_cols)
+expected_ncols
+
+str_df = merged_df3[, cols_to_extract]
+
+if (ncol(str_df) == expected_ncols){
+  cat("\nPASS: successfully extracted cols for calculating prominent effects")
+}else{
+  stop("\nAbort: Could not extract cols for calculating prominent effects")
+}
+
+#=========================
+# Masking affinity columns
+#=========================
+# First make values for affinity cols 0 when their corresponding dist >10
+head(str_df)
+
+# replace in place affinity values >10
+str_df[str_df["ligand_distance"]>10,"affinity_scaled"]=0
+str_df[str_df["ligand_distance"]>10,"mmcsm_lig_scaled"]=0
+
+#ppi2 gene: replace in place ppi2 affinity values where ppi2 dist >10
+if (tolower(gene)%in%geneL_ppi2){
+  str_df[str_df["interface_dist"]>10,"mcsm_ppi2_scaled"]=0
+} 
+  
+# na gene: replace in place na affinity values where na dist >10
+if (tolower(gene)%in%geneL_na){
+  str_df[str_df["nca_distance"]>10,"mcsm_na_scaled"]=0
+}
+
+colnames(str_df)
+head(str_df)
+
+# get names of cols to calculate the prominent effects from
+# scaled_cols_tc = c("duet_scaled",
+#                    "deepddg_scaled",
+#                    "ddg_dynamut2_scaled",
+#                    "foldx_scaled",
+#                    "affinity_scaled",
+#                    "mmcsm_lig_scaled",
+#                    "mcsm_ppi2_scaled", "mcsm_na_scaled")
+
+scaled_cols_tc = other_cols[grep("scaled", other_cols)]
+###############################################
+
+
+#===============
+# whole df
+#===============
+give_col=function(x,y,df=str_df){
+  df[df[[pos_colname]]==x,y]
+}
+
+for (i in unique(str_df[[pos_colname]]) ){
+  print(i)
+  #cat(length(unique(str_df[[pos_colname]])))
+  
+  biggest     = max(abs(give_col(i,scaled_cols_tc)))
+  
+  str_df[str_df[[pos_colname]]==i,'abs_max_effect'] = biggest
+  str_df[str_df[[pos_colname]]==i,'effect_type']= names(
+    give_col(i,scaled_cols_tc)[which(
+      abs(
+        give_col(i,scaled_cols_tc)
+      ) == biggest, arr.ind=T
+    )[, "col"]])[1]
+  
+  effect_name = unique(str_df[str_df[[pos_colname]]==i,'effect_type'])#[1] # pick first one in case we have multiple exact values
+  
+  # get index/rowname for value of max effect, and then use it to get the original sign
+  # here
+  #ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c('position',effect_name)][effect_name])== biggest, arr.ind=T))
+  ind = rownames(which(abs(str_df[str_df[[pos_colname]]==i,c(pos_colname,effect_name)][effect_name])== biggest, arr.ind=T))
+  
+  str_df[str_df[[pos_colname]]==i,'effect_sign'] = sign(str_df[effect_name][ind,])[1]
+}
+
+# ends with suffix 2 if dups
+str_df$effect_type = sub("\\.[0-9]+", "", str_df$effect_type) # cull duplicate effect types that happen when there are exact duplicate values
+colnames(str_df)
+table(str_df$effect_type)
+
+# check
+str_df_check = str_df[str_df[[pos_colname]]%in%c(24, 32, 160, 303, 334),]
+
+#================
+# for Plots
+#================
+str_df_short = str_df[, c("mutationinformation",
+                          #"position",
+                          pos_colname,
+                          "sensitivity"
+                          , "effect_type"
+                          , "effect_sign")]
+
+table(str_df_short$effect_type)
+table(str_df_short$effect_sign)
+str(str_df_short)
+
+# assign pe outcome
+str_df_short$pe_outcome = ifelse(str_df_short$effect_sign<0, "DD", "SS")
+table(str_df_short$pe_outcome )
+table(str_df_short$effect_sign)
+
+#==============
+# group effect type:
+# lig, ppi2, nuc. acid, stability
+#==============
+affcols = c("affinity_scaled",  "mmcsm_lig_scaled")
+nuc_na_cols = c("mcsm_na_scaled")
+
+#lig 
+table(str_df_short$effect_type)
+str_df_short$effect_grouped = ifelse(str_df_short$effect_type%in%affcols
+                                     , "lig"
+                                     , str_df_short$effect_type)
+table(str_df_short$effect_grouped)
+
+
+#na
+str_df_short$effect_grouped = ifelse(str_df_short$effect_grouped%in%nuc_na_cols
+                                     , "nucleic_acid"
+                                     , str_df_short$effect_grouped)
+table(str_df_short$effect_grouped)
+
+#stability
+str_df_short$effect_grouped = ifelse(!str_df_short$effect_grouped%in%c("lig",
+                                                                       "nucleic_acid")
+                                     , "stability"
+                                     , str_df_short$effect_grouped)
+
+table(str_df_short$effect_grouped)
+
+# create a sign as well
+str_df_short$pe_effect_outcome = paste0(str_df_short$pe_outcome, "_"
+                                     , str_df_short$effect_grouped)
+
+table(str_df_short$pe_effect_outcome)
+
+#####################################################################
+# Chimera: for colouring
+####################################################################
+
+#-------------------------------------
+# get df with unique position
+#--------------------------------------
+#data[!duplicated(data$x), ]   
+str_df_plot = str_df_short[!duplicated(str_df[[pos_colname]]),]
+
+if (nrow(str_df_plot) == length(unique(str_df[[pos_colname]]))){
+  cat("\nPASS: successfully extracted df with unique positions")
+}else{
+  stop("\nAbort: Could not extract df with unique positions")
+}
+
+#-------------------------------------
+# generate colours for effect types
+#--------------------------------------
+str_df_plot_cols = str_df_plot[, c(pos_colname,
+                                   "sensitivity",
+                                   "pe_outcome",
+                                   "effect_grouped",
+                                   "pe_effect_outcome")]
+head(str_df_plot_cols)
+
+# colour intensity based on sign
+#str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$effect_sign<0, "bright", "light")
+str_df_plot_cols$colour_hue = ifelse(str_df_plot_cols$pe_outcome=="DD", "bright", "light")
+
+table(str_df_plot_cols$colour_hue); table(str_df_plot$pe_outcome)
+head(str_df_plot_cols)
+
+# colour based on effect
+table(str_df_plot_cols$pe_effect_outcome)
+
+# colors = c("#ffd700" #gold
+#            , "#f0e68c" #khaki
+#            , "#da70d6"# orchid
+#            , "#ff1493"# deeppink
+#            , "#a0522d" #sienna
+#            , "#d2b48c" # tan
+#            , "#00BFC4" #, "#007d85" #blue
+#            , "#F8766D" )# red
+
+pe_colour_map = c("DD_lig"         = "#ffd700"   # gold
+                , "SS_lig"         = "#f0e68c"  # khaki
+             
+                , "DD_nucleic_acid"= "#a0522d"  # sienna
+                , "SS_nucleic_acid"= "#d2b48c"  # tan
+                
+                , "DD_ppi2"        = "#da70d6"  # orchid
+                , "SS_ppi2"        = "#ff1493"  # deeppink
+
+                , "DD_stability"   = "#f8766d"  # red
+                , "SS_stability"   = "#00BFC4") # blue
+        
+#unlist(d[c('a', 'a', 'c', 'b')], use.names=FALSE)
+
+#map the colours
+str_df_plot_cols$colour_map= unlist(map(str_df_plot_cols$pe_effect_outcome
+                                 ,function(x){pe_colour_map[[x]]}
+                                 ))
+head(str_df_plot_cols$colour_map)
+table(str_df_plot_cols$colour_map)
+table(str_df_plot_cols$pe_effect_outcome)
+
+# str_df_plot_cols$colours = paste0(str_df_plot_cols$colour_hue
+#                                   , "_"
+#                                   , str_df_plot_cols$colour_map)
+# head(str_df_plot_cols$colours)
+# table(str_df_plot_cols$colours)
+# 
+# 
+# class(str_df_plot_cols$colour_map)
+# str(str_df_plot_cols)
+
+# sort by colour
+head(str_df_plot_cols)
+str_df_plot_cols = str_df_plot_cols[order(str_df_plot_cols$colour_map), ]
+head(str_df_plot_cols)
+
+#======================================
+# write file with prominent effects
+#======================================
+outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
+write.csv(str_df_plot_cols, paste0(outdir_images, tolower(gene), "_prominent_effects.csv"))
+
+################################
+# printing for chimera
+###############################
+chain_suffix = ".A"
+str_df_plot_cols$pos_chain = paste0(str_df_plot_cols[[pos_colname]], chain_suffix)
+table(str_df_plot_cols$colour_map)
+table(str_df_plot_cols$pe_effect_outcome)
+
+#===================================================
+#-------------------
+# Ligand Affinity
+#-------------------
+# -ve Lig Aff
+dd_lig        = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_lig",]
+if (nrow(dd_lig) == table(str_df_plot_cols$pe_effect_outcome)[['DD_lig']]){
+  dd_lig_pos  = dd_lig[[pos_colname]]
+}else{
+  stop("Abort: DD affinity colour numbers mismtatch")
+}
+
+# +ve Lig Aff
+ss_lig            = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_lig",]
+if (!empty(ss_lig)){
+if (nrow(ss_lig)  == table(str_df_plot_cols$pe_effect_outcome)[['SS_lig']]){
+  ss_lig_pos      = ss_lig[[pos_colname]]
+}else{
+  stop("Abort: SS affinity colour numbers mismtatch")
+}
+  #put in chimera cmd
+  paste0(dd_lig_pos,chain_suffix)
+  paste0(ss_lig_pos,chain_suffix)
+
+}
+
+#===================================================
+#------------------------
+# Nucleic Acid Affinity
+#------------------------
+# -ve NA aff
+dd_nca           =  str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_nucleic_acid",]
+if (nrow(dd_nca) == table(str_df_plot_cols$pe_effect_outcome)[['DD_nucleic_acid']]){
+  dd_nca_pos     =  dd_nca[[pos_colname]]
+}else{
+  stop("Abort: DD nucleic_acid colour numbers mismtatch")
+}
+
+# +ve NA aff
+ss_nca            = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_nucleic_acid",]
+if (nrow(ss_nca)  == table(str_df_plot_cols$pe_effect_outcome)[['SS_nucleic_acid']]){
+  ss_nca_pos      = ss_nca[[pos_colname]]
+}else{
+  stop("Abort: SS nucleic_acid colour numbers mismtatch")
+}
+
+#put in chimera cmd
+paste0(dd_nca_pos, chain_suffix)
+paste0(ss_nca_pos, chain_suffix)
+
+#=========================================================
+#------------------------
+# Stability
+#------------------------
+# -ve Stability
+dd_stability           =  str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="DD_stability",]
+if (nrow(dd_stability) == table(str_df_plot_cols$pe_effect_outcome)[['DD_stability']]){
+  dd_stability_pos     =  dd_stability[[pos_colname]]
+}else{
+  stop("Abort: DD Stability colour numbers mismtatch")
+}
+
+# +ve Stability
+ss_stability            = str_df_plot_cols[str_df_plot_cols$pe_effect_outcome=="SS_stability",]
+if (nrow(ss_stability)  == table(str_df_plot_cols$pe_effect_outcome)[['SS_stability']]){
+  ss_stability_pos      = ss_stability[[pos_colname]]
+}else{
+  stop("Abort: SS Stability colour numbers mismtatch")
+}
+
+#put in chimera cmd
+# stabiliting first as it has less numbers
+paste0(ss_stability_pos, chain_suffix)
+paste0(dd_stability_pos, chain_suffix)
+####################################################################
+

scripts/plotting/plotting_thesis/gid/sensitivity_count_gid.R

@@ -0,0 +1,65 @@
+#=========================
+# Count Sensitivity
+# Mutations and positions
+#=========================
+pos_colname_c ="position"
+
+sensP_df = merged_df3[,c("mutationinformation",
+                         #"position",
+                         pos_colname_c,
+                         "sensitivity")]
+
+head(sensP_df)
+table(sensP_df$sensitivity)
+
+#---------------
+# Total unique positions
+#----------------
+tot_mut_pos = length(unique(sensP_df[[pos_colname_c]]))
+cat("\nNo of Tot muts sites:", tot_mut_pos)
+
+# resistant mut pos
+sens_site_allR = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="R"]
+sens_site_UR   = unique(sens_site_allR)
+length(sens_site_UR)
+
+# Sensitive mut pos
+sens_site_allS = sensP_df[[pos_colname_c]][sensP_df$sensitivity=="S"]
+sens_site_US = unique(sens_site_allS)
+length(sens_site_UR)
+
+#---------------
+# Common Sites
+#----------------
+common_pos = intersect(sens_site_UR,sens_site_US)
+site_Cc = length(common_pos)
+cat("\nNo of Common sites:", site_Cc
+    , "\nThese are:", common_pos)
+
+#---------------
+# Resistant muts
+#----------------
+site_R = sens_site_UR[!sens_site_UR%in%common_pos]
+site_Rc = length(site_R)
+
+if ( length(sens_site_allR) == table(sensP_df$sensitivity)[['R']] ){
+  cat("\nNo of R muts:", length(sens_site_allR) 
+      , "\nNo. of R sites:",site_Rc
+      , "\nThese are:", site_R
+)
+}
+
+#---------------
+# Sensitive muts
+#----------------
+site_S = sens_site_US[!sens_site_US%in%common_pos]
+site_Sc = length(site_S)
+
+if ( length(sens_site_allS) == table(sensP_df$sensitivity)[['S']] ){
+  cat("\nNo of S muts:", length(sens_site_allS) 
+      , "\nNo. of S sites:", site_Sc
+      , "\nThese are:", site_S)
+}
+
+#########################
+

scripts/plotting/plotting_thesis/katg/katg_other_plots.R

@@ -0,0 +1,2 @@
+
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp_dist_layout.R")

scripts/plotting/plotting_thesis/linage_bp_dist_layout.R

@@ -7,7 +7,6 @@ source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp
 #linage_dist_ens_stability.R
 ###########################################
 # svg
-# my_label_size = 12
 # linPlots_combined = paste0(outdir_images
 #                         , tolower(gene)
 #                         ,"_linP_combined.svg")
@@ -31,6 +30,8 @@ source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp
 # dev.off()
 
 # png
+my_label_size = 12
+
 linPlots_combined = paste0(outdir_images
                            , tolower(gene)
                            ,"_linP_combined.png")

scripts/plotting/plotting_thesis/rpob/basic_barplots_rpob.R

@@ -7,16 +7,9 @@
 #=============
 # Data: Input
 #==============
-#source("~/git/LSHTM_analysis/config/pnca.R")
-#source("~/git/LSHTM_analysis/config/embb.R")
-#source("~/git/LSHTM_analysis/config/gid.R")
-
-#source("~/git/LSHTM_analysis/config/alr.R")
-#source("~/git/LSHTM_analysis/config/katg.R")
 #source("~/git/LSHTM_analysis/config/rpob.R")
 
 #source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
-#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R") sourced by above
 
 #cat("\nSourced plotting cols as well:", length(plotting_cols))
 

scripts/plotting/plotting_thesis/rpob/basic_barplots_rpob_layout.R

@@ -1,4 +1,18 @@
-#source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/pe_sens_site_count_rpob.R")
+#=============
+# Data: Input
+#==============
+source("~/git/LSHTM_analysis/config/rpob.R")
+source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.R")
+#cat("\nSourced plotting cols as well:", length(plotting_cols))
+
+#=======
+# output
+#=======
+outdir_images = paste0("~/git/Writing/thesis/images/results/", tolower(gene), "/")
+cat("plots will output to:", outdir_images)
+
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/rpob/basic_barplots_rpob.R")
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/rpob/pe_sens_site_count_rpob.R")
 
 if ( tolower(gene)%in%c("rpob") ){
   cat("\nPlots available for layout are:")

scripts/plotting/plotting_thesis/rpob/gg_pairs_all_rpob.R

@@ -1,6 +1,11 @@
-#source("~/git/LSHTM_analysis/config/embb.R")
+source("~/git/LSHTM_analysis/config/rpob.R")
-#source("~/git/LSHTM_analysis/scripts/plotting/plotting_colnames.R")
+source("~/git/LSHTM_analysis/scripts/plotting/get_plotting_dfs.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
@@ -72,7 +77,7 @@ if (tolower(gene)%in%geneL_ppi2){
 }
 
 if (tolower(gene)%in%geneL_na){
-  corr_affinity_df[corr_affinity_df["NCA-Dist"]>DistCutOff,"mCSM-NA"]=0
+  corr_affinity_df[corr_affinity_df["NA-Dist"]>DistCutOff,"mCSM-NA"]=0
 }
 
 # count 0

scripts/plotting/plotting_thesis/rpob/linage_bp_dist.R

@@ -1,172 +0,0 @@
-#!/usr/bin/env Rscript  
-
-#########################################################
-# TASK: Lineage plots [merged_df2]
-# Count
-# Diversity
-# Average stability dist
-# Avergae affinity dist: optional
-#########################################################
-#=======
-# output
-#=======
-# outdir_images = paste0("~/git/Writing/thesis/images/results/"
-#                        , tolower(gene), "/")
-# cat("plots will output to:", outdir_images)
-#########################################################
-
-#===============
-#Quick numbers checks
-#===============
-nsample_lin = merged_df2[merged_df2$lineage%in%c("L1", "L2", "L3", "L4"),]
-
-if ( all(table(nsample_lin$sensitivity)== table(nsample_lin$mutation_info_labels)) ){
-  cat("\nTotal no. of samples belonging to L1-l4 for", gene,":", nrow(nsample_lin)
-      , "\nCounting R and S samples")
-  if( sum(table(nsample_lin$sensitivity)) ==  nrow(nsample_lin) ){
-    cat("\nPASSNumbers cross checked:")
-    print(table(nsample_lin$sensitivity))
-  }
-}else{
-  stop("Abort: Numbers mismatch. Please check")
-}
-########################################################################
-###################################################
-#                  Lineage barplots               #
-###################################################
-
-#===============================
-# lineage sample and SNP count
-#===============================
-lin_countP = lin_count_bp(lf_data = lineage_dfL[['lin_lf']]
-             , all_lineages = F
-             , x_categ = "sel_lineages"
-             , y_count = "p_count"
-             , use_lineages = c("L1", "L2", "L3", "L4")
-             , bar_fill_categ = "count_categ"
-             , display_label_col = "p_count"
-             , bar_stat_stype = "identity"
-             , d_lab_size = 8
-             , d_lab_col = "black"
-             , my_xats = 25 # x axis text size
-             , my_yats = 25 # y axis text sized_lab_size
-             , my_xals = 25 # x axis label size
-             , my_yals = 25 # y axis label size
-             , my_lls  = 25 # legend label size
-             , bar_col_labels =  c("SNPs", "Total Samples")
-             , bar_col_values = c("grey50", "gray75")
-             , bar_leg_name = ""
-             , leg_location = "top"
-             , y_log10 = F
-             , y_scale_percent = FALSE
-             , y_label = c("Count")
-             )
-lin_countP
-#===============================
-# lineage SNP diversity count
-#===============================
-lin_diversityP = lin_count_bp_diversity(lf_data = lineage_dfL[['lin_wf']]
-                              , x_categ = "sel_lineages"
-                              , y_count = "snp_diversity"
-                              #, all_lineages = F
-                              , use_lineages = c("L1", "L2", "L3", "L4")
-                              , display_label_col = "snp_diversity_f"
-                              , bar_stat_stype = "identity"
-                              , x_lab_angle = 90
-                              , d_lab_size =9
-                              , my_xats = 25 # x axis text size
-                              , my_yats = 25 # y axis text size
-                              , my_xals = 25 # x axis label size
-                              , my_yals = 25 # y axis label size
-                              , my_lls  = 25 # legend label size
-                              , y_log10 = F
-                              , y_scale_percent = F
-                              , leg_location = "top"
-                              , y_label = "Percent" #"SNP diversity"
-                              , bp_plot_title = "nsSNP diversity"
-                              , title_colour = "black" #"chocolate4"
-                              , subtitle_text = NULL
-                              , sts = 20
-                              , subtitle_colour = "#350E20FF")
-lin_diversityP
-#=============================================
-# Output plots: Lineage count and Diversity
-#=============================================
-# lineage_bp_CL =  paste0(outdir_images
-#                         ,tolower(gene)
-#                         ,"_lineage_bp_CL_Tall.svg")
-# 
-# cat("Lineage barplots:", lineage_bp_CL)
-# svg(lineage_bp_CL, width = 8, height = 15)
-# 
-# cowplot::plot_grid(lin_countP, lin_diversityP
-#                      #, labels = c("(a)", "(b)", "(c)", "(d)")
-#                       , nrow = 2
-#                      # , ncols = 2
-#                       , labels = "AUTO"
-#                       , label_size = 25)
-# 
-# dev.off()
-########################################################################
-
-
-###################################################
-#                   Stability dist                #
-###################################################
-# scaled_cols_stability = c("duet_scaled"       
-#                           , "deepddg_scaled"   
-#                           , "ddg_dynamut2_scaled"
-#                           , "foldx_scaled"
-#                           , "avg_stability_scaled")
-
-my_xlabel = paste0("Average stability ", "(", stability_suffix, ")"); my_xlabel
-#plotdf = merged_df2[merged_df2$lineage%in%c("L1", "L2", "L3", "L4"),]
-
-linP_dm_om = lineage_distP(merged_df2
-                           , with_facet = F
-                           , x_axis = "avg_stability_scaled"
-                           , y_axis = "lineage_labels"
-                           , x_lab = my_xlabel
-                           , use_lineages = c("L1", "L2", "L3", "L4")
-                           #, fill_categ = "mutation_info_orig", fill_categ_cols = c("#E69F00", "#999999")
-                           , fill_categ = "sensitivity"
-                           , fill_categ_cols = c("red", "blue")
-                           , label_categories = c("Resistant", "Sensitive")
-                           , leg_label = "Mutation group"
-                           , my_ats = 22 # axis text size
-                           , my_als = 22 # axis label size
-                           , my_leg_ts = 22
-                           , my_leg_title = 22
-                           , my_strip_ts = 22
-                           , alpha = 0.56
-)
-
-linP_dm_om
-
-###################################################
-#                 Affinity dist [OPTIONAL]        #
-###################################################
-# scaled_cols_affinity = c("affinity_scaled" 
-#                          , "mmcsm_lig_scaled" 
-#                          , "mcsm_ppi2_scaled" 
-#                          , "mcsm_na_scaled"
-#                          , "avg_lig_affinity_scaled")
-
-# lineage_distP(merged_df2
-#               , with_facet = F
-#               , x_axis = "avg_lig_affinity_scaled"
-#               , y_axis = "lineage_labels"
-#               , x_lab = my_xlabel
-#               , use_lineages = c("L1", "L2", "L3", "L4")
-#               #, fill_categ = "mutation_info_orig", fill_categ_cols = c("#E69F00", "#999999")
-#               , fill_categ = "sensitivity"
-#               , fill_categ_cols = c("red", "blue")
-#               , label_categories = c("Resistant", "Sensitive")
-#               , leg_label = "Mutation group"
-#               , my_ats = 22 # axis text size
-#               , my_als = 22 # axis label size
-#               , my_leg_ts = 22
-#               , my_leg_title = 22
-#               , my_strip_ts = 22
-#               , alpha = 0.56
-# )

scripts/plotting/plotting_thesis/rpob/linage_bp_dist_layout.R

@@ -1,30 +0,0 @@
-#!/usr/bin/env Rscript  
-
-###########################################
-# TASK: generate plots for lineage
-# Individual plots in 
-#lineage_bp_both.R
-#linage_dist_ens_stability.R
-###########################################
-my_label_size = 25
-
-linPlots_combined = paste0(outdir_images
-                        , tolower(gene)
-                        ,"_linP_combined.svg")
-
-cat("\nOutput plot:", linPlots_combined)
-svg(linPlots_combined, width = 18, height = 12)
-
-cowplot::plot_grid(
-  cowplot::plot_grid(lin_countP, lin_diversityP
-                     , nrow = 2
-                     , labels = "AUTO"
-                     , label_size = my_label_size),
-  NULL,
-  linP_dm_om,
-  nrow = 1,
-  labels = c("", "", "C"),
-  label_size = my_label_size,
-  rel_widths = c(35, 3, 52)
-)
-dev.off()

scripts/plotting/plotting_thesis/rpob/pe_sens_site_count_rpob.R

@@ -1,5 +1,5 @@
-source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/prominent_effects_rpob.R")
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/rpob/prominent_effects_rpob.R")
-source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/sensitivity_count_rpob.R")
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/rpob/sensitivity_count_rpob.R")
 
 ##############################################################
 #                         PE count

scripts/plotting/plotting_thesis/rpob/rpob_other_plots.R

@@ -0,0 +1,2 @@
+
+source("/home/tanu/git/LSHTM_analysis/scripts/plotting/plotting_thesis/linage_bp_dist_layout.R")