#!/usr/bin/env Rscript ######################################################### # TASK: Lineage dist plots: ggridges # Output: 2 SVGs for PS stability # 1) all muts # 2) dr_muts ########################################################## # Installing and loading required packages ########################################################## getwd() setwd("~/git/LSHTM_analysis/scripts/plotting/") getwd() source("Header_TT.R") library(ggridges) source("combining_dfs_plotting.R") # PS combined: # 1) merged_df2 # 2) merged_df2_comp # 3) merged_df3 # 4) merged_df3_comp # LIG combined: # 5) merged_df2_lig # 6) merged_df2_comp_lig # 7) merged_df3_lig # 8) merged_df3_comp_lig # 9) my_df_u # 10) my_df_u_lig cat("Directories imported:" , "\n====================" , "\ndatadir:", datadir , "\nindir:", indir , "\noutdir:", outdir , "\nplotdir:", plotdir) cat("Variables imported:" , "\n=====================" , "\ndrug:", drug , "\ngene:", gene , "\ngene_match:", gene_match , "\nAngstrom symbol:", angstroms_symbol , "\nNo. of duplicated muts:", dup_muts_nu , "\nNA count for ORs:", na_count , "\nNA count in df2:", na_count_df2 , "\nNA count in df3:", na_count_df3 , "\ndr_muts_col:", dr_muts_col , "\nother_muts_col:", other_muts_col , "\ndrtype_col:", resistance_col) cat("cols imported:" , mcsm_red2, mcsm_red1, mcsm_mid, mcsm_blue1, mcsm_blue2) #======= # output #======= lineage_dist_combined_dm_om = "lineage_dist_combined_dm_om_PS.svg" plot_lineage_dist_combined_dm_om = paste0(plotdir,"/", lineage_dist_combined_dm_om) #======================================================================== ########################### # Data for plots # you need merged_df2 or merged_df2_comp # since this is one-many relationship # i.e the same SNP can belong to multiple lineages # using the _comp dataset means # we lose some muts and at this level, we should use # as much info as available, hence use df with NA ########################### # REASSIGNMENT my_df = merged_df2 # delete variables not required rm(my_df_u, merged_df2, merged_df2_comp, merged_df3, merged_df3_comp) # quick checks colnames(my_df) str(my_df) table(my_df$mutation_info) #=================== # Data for plots #=================== table(my_df$lineage); str(my_df$lineage) # select lineages 1-4 sel_lineages = c("lineage1" , "lineage2" , "lineage3" , "lineage4") #, "lineage5" #, "lineage6" #, "lineage7") # works nicely with facet wrap using labeller, but not otherwise #my_labels = c('Lineage 1' # , 'Lineage 2' # , 'Lineage 3' # , 'Lineage 4') # #, 'Lineage 5' # #, 'Lineage 6' # #, 'Lineage 7') #names(my_labels) = c('lineage1' # , 'lineage2' # , 'lineage3' # , 'lineage4') # #, 'lineage5' # #, 'lineage6' # #, 'lineage7') #========================== # subset selected lineages #========================== df_lin = subset(my_df, subset = lineage %in% sel_lineages) table(df_lin$lineage) #{RESULT: Total number of samples for lineage} sum(table(df_lin$lineage)) #{RESULT: No of samples within lineage} table(df_lin$lineage) #{Result: No. of unique mutations the 4 lineages contribute to} length(unique(df_lin$mutationinformation)) u2 = unique(my_df$mutationinformation) u = unique(df_lin$mutationinformation) #{Result:Muts not present within selected lineages} check = u2[!u2%in%u]; print(check) # workaround to make labels appear nicely for in otherwise cases #================== # lineage: labels # from "plyr" #================== table(df_lin$lineage) df_lin$lineage_labels = mapvalues(df_lin$lineage , from = c("lineage1","lineage2", "lineage3", "lineage4") , to = c("Lineage 1", "Lineage 2", "Lineage 3", "Lineage 4")) table(df_lin$lineage_labels) table(df_lin$lineage_labels) == table(df_lin$lineage) #======================== # mutation_info: labels #======================== table(df_lin$mutation_info) df_lin$mutation_info_labels = ifelse(df_lin$mutation_info == dr_muts_col, "DM", "OM") table(df_lin$mutation_info_labels) table(df_lin$mutation_info) == table(df_lin$mutation_info_labels) #======================= # subset dr muts only #======================= my_df_dr = subset(df_lin, mutation_info == dr_muts_col) table(my_df_dr$mutation_info) table(my_df_dr$lineage) #========================= # subset other muts only #========================= my_df_other = subset(df_lin, mutation_info == other_muts_col) table(my_df_other$mutation_info) table(my_df_other$lineage) ######################################################################## # end of data extraction and cleaning for plots # ######################################################################## #========================== # Distribution plots #============================ #%%%%%%%%%%%%%%%%%%%%%%%%% # REASSIGNMENT df <- df_lin #%%%%%%%%%%%%%%%%%%%%%%%%% rm(df_lin) #****************** # generate distribution plot of lineages #****************** # 2 : ggridges (good!) my_ats = 15 # axis text size my_als = 20 # axis label size n_colours = length(unique(df$duet_scaled)) my_palette <- colorRampPalette(c(mcsm_red2, mcsm_red1, mcsm_mid, mcsm_blue1, mcsm_blue2))(n = n_colours+1) #======================================= # Plot 1: lineage dist: geom_density_ridges_gradient (allows aesthetics to vary along ridgeline, no alpha setting!) # else same as geom_density_ridges) # x = duet_scaled # y = lineage_labels # fill = duet_scaled # NO FACET (nf) #======================================= # output individual svg #plot_lineage_dist_duet_nf = paste0(plotdir,"/", "lineage_dist_duet_nf.svg") #plot_lineage_dist_duet_nf #svg(plot_lineage_dist_duet_nf) p1 = ggplot(df, aes(x = duet_scaled , y = lineage_labels))+ geom_density_ridges_gradient(aes(fill = ..x..) #, jittered_points = TRUE , scale = 3 , size = 0.3 ) + coord_cartesian( xlim = c(-1, 1)) + scale_fill_gradientn(colours = my_palette, name = "DUET") + theme(axis.text.x = element_text(size = my_ats , angle = 90 , hjust = 1 , vjust = 0.4) , axis.text.y = element_text(size = my_ats) , axis.title.x = element_text(size = my_ats) , axis.title.y = element_blank() , axis.ticks.y = element_blank() , plot.title = element_blank() , strip.text = element_text(size = my_als) , legend.text = element_text(size = my_als-10) , legend.title = element_text(size = my_als-3) , legend.position = c(0.8, 0.8)) + #, legend.direction = "horizontal")+ #, legend.position = "top", )+ labs(x = "DUET") p1 #p1_copy = p1 + guides(fill = guide_colourbar(label = FALSE)) #p1_copy= p1_copy + guides(size=guide_legend("Source", override.aes=list(shape=15, size = 10))) #p1_copy #======================================= # Plot 2: lineage dist: geom_density_ridges, allows alpha to be set # x = duet_scaled # y = lineage_labels # fill = mutation_info # NO FACET #======================================= # output svg #plot_lineage_dist_duet_dm_om = paste0(plotdir,"/", "lineage_dist_duet_dm_om.svg") #plot_lineage_dist_duet_dm_om #svg(plot_lineage_dist_duet_dm_om) p2 = ggplot(df, aes(x = duet_scaled , y = lineage_labels))+ geom_density_ridges(aes(fill = factor(mutation_info_labels)) , scale = 3 , size = 0.3 , alpha = 0.8) + coord_cartesian( xlim = c(-1, 1)) + scale_fill_manual(values = c("#E69F00", "#999999")) + theme(axis.text.x = element_text(size = my_ats , angle = 90 , hjust = 1 , vjust = 0.4) , axis.text.y = element_text(size = my_ats) , axis.title.x = element_text(size = my_ats) , axis.title.y = element_blank() , axis.ticks.y = element_blank() , plot.title = element_blank() , strip.text = element_text(size = my_als) , legend.text = element_text(size = my_als-2) , legend.title = element_text(size = my_als-3) , legend.position = c(0.8, 0.9)) + labs(x = "DUET" , fill = "Mutation class") # legend title p2 ######################################################################## #============== # combine plot #=============== plot_lineage_dist_combined_dm_om svg(plot_lineage_dist_combined_dm_om, width = 12, height = 6) printFile = cowplot::plot_grid(p1, p2 , rel_widths = c(0.5/2, 0.5/2) , label_size = my_als+10) print(printFile) dev.off() ######################################################################## # alternate combination ######################################################################## #======================= # Plot 3: lineage dist: geom_density_ridges_gradient (allows aesthetics to vary along ridgeline, no alpha setting!) # else same as geom_density_ridges) # x = duet_scaled # y = duet_outcome # FACET (f) = lineage #======================= # output individual svg #plot_lineage_dist_duet = paste0(plotdir,"/", "lineage_dist_duet_f.svg") #plot_lineage_dist_duet #svg(plot_lineage_dist_duet) p3 = ggplot(df, aes(x = duet_scaled , y = duet_outcome))+ geom_density_ridges_gradient(aes(fill = ..x..) , scale = 3 , size = 0.3) + facet_wrap( ~lineage_labels , scales = "free" #, labeller = labeller(lineage = my_labels) # sorted by lineage_labels ) + coord_cartesian( xlim = c(-1, 1)) + scale_fill_gradientn(colours = my_palette, name = "DUET") + theme(axis.text.x = element_text(size = my_ats , angle = 90 , hjust = 1 , vjust = 0.4) , axis.text.y = element_blank() , axis.title.x = element_text(size = my_ats) , axis.title.y = element_blank() , axis.ticks.y = element_blank() , plot.title = element_blank() , strip.text = element_text(size = my_als) , legend.text = element_text(size = my_als-6) , legend.title = element_text(size = my_als-3))+ labs(x = "DUET") print(p3) #dev.off() #============== # combine plot: alt version #=============== plot_lineage_dist_duet_fandnf = paste0(plotdir,"/", "lineage_dist_duet_fandnf.svg") plot_lineage_dist_duet_fandnf svg(plot_lineage_dist_duet_fandnf, width = 12, height = 6) printFile = cowplot::plot_grid(p3, p2 , rel_widths = c(0.5/2, 0.5/2) , label_size = my_als+10) print(printFile) dev.off()