breakage

scripts/functions/bp_lineage_diversity.R

@@ -23,14 +23,19 @@ lin_count_bp_diversity <- function( lf_data = lin_wf
                           , my_xals = 22 # x axis label size
                           , my_yals = 22 # y axis label size
                           , my_lls   = 22 # legend label size
-                          , bar_col_labels =  c("Mutations", "Total Samples")
+                          , bar_col_labels =  "" #c("Mutations", "Total Samples")
-                          , bar_col_values = c("grey50", "gray75")
+                          , bar_col_values = c("gray50", "gray75")
                           , bar_leg_name = ""
                           , leg_location = "top"
                           , y_log10 = FALSE
                           , y_scale_percent = FALSE
                           #, y_label = c("Count", "SNP diversity")
                           , y_label = c("SNP diversity")
+                          , bp_plot_title = ""
+                          , title_colour = "chocolate4"
+                          , subtitle_text = NULL
+                          , sts = 20
+                          , subtitle_colour = "#350E20FF" #brown
                           ) {
   if(!all_lineages){
     lf_data = lf_data[lf_data[[x_categ]]%in%use_lineages,]
@@ -58,7 +63,13 @@ lin_count_bp_diversity <- function( lf_data = lin_wf
           , axis.title.y = element_text(size     = my_yals
                                         , colour = "black")
           , legend.position = leg_location
-          , legend.text = element_text(size = my_lls)) + 
+          , legend.text = element_text(size = my_lls)
+          , plot.title = element_text(size =  my_lls
+                                      , colour = title_colour
+                                      , hjust = 0.5)
+          , plot.subtitle = element_text(size = sts
+                                         , hjust = 0.5
+                                         , colour = subtitle_colour)) + 
     
     geom_label(aes(label = eval(parse(text = display_label_col)))
                , size    = d_lab_size
@@ -72,10 +83,16 @@ lin_count_bp_diversity <- function( lf_data = lin_wf
     scale_fill_manual(values   = bar_col_values
                       , name   = bar_leg_name
                       , labels = bar_col_labels) +
-    labs(title    = ""
+    # labs(title    = ""
-         , x      = ""
+    #      , x      = ""
-         , y      = y_label
+    #      , y      = y_label
-         , colour = "black")
+    #      , colour = "black")
+    # 
+    labs(title      = bp_plot_title
+         , subtitle = subtitle_text
+         , x = ""
+         , y        = y_label
+         , colour   = "black") 
     
   if (y_log10){
     
@@ -90,10 +107,11 @@ lin_count_bp_diversity <- function( lf_data = lin_wf
       scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
       #scale_y_continuous(labels = scales::percent) +
       
-      labs(title    = ""
+      labs(title      = bp_plot_title
-           , x      = ""
+           , subtitle = subtitle_text
-           , y      = y_label
+           , x = ""
-           , colour = "black")
+           , y        = y_label
+           , colour   = "black") 
   }
   
   return(OutPlot)

scripts/functions/combining_dfs_plotting.R

@@ -343,6 +343,29 @@ combining_dfs_plotting <- function(  my_df_u
         , "\nNo. of rows merged_df3: ", nrow(merged_df3))
     quit()
   }
+  #---------------------------------------------
+  # add columns that are needed to generate plots with revised colnames and strings
+  #----------------------------------------------
+  merged_df3['sensitivity'] = ifelse(merged_df3['dst_mode'] == 1, "R", "S")
+  merged_df3['mutation_info_labels'] = ifelse(merged_df3['mutation_info_labels'] == "DM", "R", "S")
+
+  merged_df2['sensitivity'] = ifelse(merged_df2['dst_mode'] == 1, "R", "S")
+  merged_df2['mutation_info_labels'] = ifelse(merged_df2['mutation_info_labels'] == "DM", "R", "S")
+  
+  #check1 = all(table(merged_df3["mutation_info_labels"]) == table(merged_df3['sensitivity']))
+  #check2 = all(table(merged_df2["mutation_info_labels"]) == table(merged_df2['sensitivity']))
+
+  check1 = all(merged_df3["mutation_info_labels"] == merged_df3['sensitivity'])
+  check2 = all(merged_df2["mutation_info_labels"] == merged_df2['sensitivity'])
+                              
+  if(check1 && check2){
+    cat("PASS: merged_df3 and merged_df2 have mutation info labels as R and S" 
+        , "\nIt also has sensitivity column"
+        , "\nThese are identical")
+  }else{
+    stop("Abort: merged_df3 or merged_df2 can't be created because of lable mismatch")
+  }
+  
   return(list(  merged_df2
                 , merged_df3
   ))

scripts/functions/dm_om_data.R

@@ -37,6 +37,11 @@
 #1) df to choose (merged_df3 or merged_df2)
 #2)
 ##################################################################
+DistCutOff = 10
+LigDist_colname  # = "ligand_distance" # from globals 
+ppi2Dist_colname  = "interface_dist"
+naDist_colname    = "TBC"
+
 dm_om_wf_lf_data <- function(df
                           , gene_name               = gene # from globals
                           , colnames_to_extract
@@ -51,6 +56,15 @@ dm_om_wf_lf_data <- function(df
                           , dr_other_muts_labels    = c("DM", "OM") # only used if ^^ = ""
                           , categ_cols_to_factor){
   
+  df = as.data.frame(df)
+ 
+  df['sensitivity'] = ifelse(df['dst_mode'] == 1, "R", "S")
+  table(df['sensitivity'])
+  
+  df[[mut_info_label_colname]] = ifelse(df[[mut_info_label_colname]] == "DM", "R", "S")
+  table(df[[mut_info_label_colname]])
+  
+  
   # Initialise the required dfs based on gene name
   geneL_normal  = c("pnca")
   #geneL_na_dy   = c("gid")
@@ -124,6 +138,7 @@ dm_om_wf_lf_data <- function(df
         , mut_colname, mut_info_colname, mut_info_label_colname
         , aa_pos_colname
         ,  LigDist_colname
+        , ppi2Dist_colname, naDist_colname
         , "duet_stability_change" , "duet_scaled"        , "duet_outcome"
         , "ligand_affinity_change", "affinity_scaled"    , "ligand_outcome"
         , "ddg_foldx"             , "foldx_scaled"       , "foldx_outcome"

scripts/plotting/get_plotting_dfs.R

@@ -144,6 +144,7 @@ cat(s3)
 #          location: scripts/functions/corr_plot_data.R
 ####################################################################
 # make sure the above script works because merged_df2_combined is needed
+merged_df3 = as.data.frame(merged_df3)
 
 corr_df_m3_f = corr_data_extract(merged_df3, extract_scaled_cols = F)
 head(corr_df_m3_f)

scripts/plotting/plotting_thesis/lineage_barplots_combined.R

@@ -48,7 +48,12 @@ lin_diversityP = lin_count_bp_diversity(lf_data = lineage_dfL[['lin_wf']]
                               , y_log10 = F
                               , y_scale_percent = F
                               , leg_location = "top"
-                              , y_label = "SNP diversity")
+                              , y_label = "Percent" #"SNP diversity"
+                              , bp_plot_title = "SNP diversity"
+                              , title_colour = "black" #"chocolate4"
+                              , subtitle_text = NULL
+                              , sts = 20
+                              , subtitle_colour = "#350E20FF")
 
 #=============================================
 # Output plots: Lineage count and Diversity