generated lineage dist plots combined. needs tweaking
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af7c55b713
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98cd8a74e8
3 changed files with 191 additions and 119 deletions
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@ -48,6 +48,8 @@ cat("Variables imported:"
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nAngstrom symbol:", angstroms_symbol
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, "\nNo. of duplicated muts:", dup_muts_nu
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, "\ndr_muts_col:", dr_muts_col
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, "\nother_muts_col:", other_muts_col
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, "\ndrtype_col:", resistance_col)
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@ -30,13 +30,15 @@ source("combining_dfs_plotting.R")
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# 9) my_df_u
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# 10) my_df_u_lig
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cat(paste0("Directories imported:"
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cat("Directories imported:"
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, "\n===================="
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir
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, "\nplotdir:", plotdir))
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, "\nplotdir:", plotdir)
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cat(paste0("Variables imported:"
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cat("Variables imported:"
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, "\n====================="
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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@ -44,7 +46,11 @@ cat(paste0("Variables imported:"
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, "\nNo. of duplicated muts:", dup_muts_nu
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, "\nNA count for ORs:", na_count
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, "\nNA count in df2:", na_count_df2
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, "\nNA count in df3:", na_count_df3))
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, "\nNA count in df3:", na_count_df3
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, "\ndr_muts_col:", dr_muts_col
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, "\nother_muts_col:", other_muts_col
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, "\ndrtype_col:", resistance_col)
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#===========
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# input
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@ -67,7 +73,6 @@ plot_basic_bp_lineage = paste0(plotdir,"/", basic_bp_lineage)
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#================
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# REASSIGNMENT as necessary
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my_df = merged_df2
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#my_df = merged_df2_comp
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# clear excess variable
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rm(merged_df2_comp, merged_df3, merged_df3_comp)
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@ -3,36 +3,55 @@ getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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getwd()
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########################################################################
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# Installing and loading required packages #
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########################################################################
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#########################################################
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# TASK: Lineage dist plots: ggridges
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source("../Header_TT.R")
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#source("../barplot_colour_function.R")
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#require(data.table)
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# Output: 2 SVGs for PS stability
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########################################################################
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# Read file: call script for combining df for PS #
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########################################################################
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# 1) all muts
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# 2) dr_muts
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source("combining_two_df.R")
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##########################################################
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# Installing and loading required packages
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##########################################################
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#---------------------- PAY ATTENTION
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# the above changes the working dir
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#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
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#---------------------- PAY ATTENTION
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source("combining_dfs_plotting.R")
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# PS combined:
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# 1) merged_df2
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# 2) merged_df2_comp
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# 3) merged_df3
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# 4) merged_df3_comp
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#==========================
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# This will return:
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# LIG combined:
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# 5) merged_df2_lig
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# 6) merged_df2_comp_lig
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# 7) merged_df3_lig
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# 8) merged_df3_comp_lig
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# df with NA for <drug>
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# merged_df2
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# merged_df3
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# 9) my_df_u
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# 10) my_df_u_lig
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cat("Directories imported:"
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, "\n===================="
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, "\ndatadir:", datadir
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, "\nindir:", indir
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, "\noutdir:", outdir
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, "\nplotdir:", plotdir)
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cat("Variables imported:"
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, "\n====================="
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, "\ndrug:", drug
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, "\ngene:", gene
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, "\ngene_match:", gene_match
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, "\nAngstrom symbol:", angstroms_symbol
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, "\nNo. of duplicated muts:", dup_muts_nu
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, "\nNA count for ORs:", na_count
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, "\nNA count in df2:", na_count_df2
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, "\nNA count in df3:", na_count_df3
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, "\ndr_muts_col:", dr_muts_col
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, "\nother_muts_col:", other_muts_col
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, "\ndrtype_col:", resistance_col)
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# df without NA for <drug>
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# merged_df2_comp
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# merged_df3_comp
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#===========================
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###########################
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# Data for plots
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@ -43,13 +62,8 @@ source("combining_two_df.R")
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# we lose some muts and at this level, we should use
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# as much info as available, hence use df with NA
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###########################
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# uncomment as necessary
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#%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT
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my_df = merged_df2
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#my_df = merged_df2_comp
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#%%%%%%%%%%%%%%%%%%%%%%%%
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# delete variables not required
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rm(my_df_u, merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
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@ -63,7 +77,7 @@ is.factor(my_df$lineage)
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my_df$lineage = as.factor(my_df$lineage)
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is.factor(my_df$lineage)
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table(my_df$mutation_info); str(my_df$mutation_info)
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table(my_df$mutation_info)
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# subset df with dr muts only
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my_df_dr = subset(my_df, mutation_info == "dr_mutations_pyrazinamide")
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@ -74,59 +88,32 @@ table(my_df_dr$mutation_info)
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########################################################################
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#==========================
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# Run two times:
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# uncomment as necessary
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# 1) for all muts
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# 2) for dr_muts
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#===========================
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#%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT
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#================
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# for ALL muts
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#================
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plot_df = my_df
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my_plot_name = 'lineage_dist_PS.svg'
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plot_lineage_duet = paste0(plotdir,"/", my_plot_name)
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#my_plot_name = 'lineage_dist_PS_comp.svg'
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#================
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# for dr muts ONLY
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#================
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plot_df = my_df_dr
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#my_plot_name = 'lineage_dist_dr_PS.svg'
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#my_plot_name = 'lineage_dist_dr_PS_comp.svg'
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my_plot_name = 'lineage_dist_drug_muts_PS.svg'
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plot_lineage_duet = paste0(plotdir,"/", my_plot_name)
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#%%%%%%%%%%%%%%%%%%%%%%%%
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#==========================
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# Plot: Lineage Distribution
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# Plot 1: ALL Muts
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# x = mcsm_values, y = dist
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# fill = stability
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#============================
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my_plot_name = 'lineage_dist_PS.svg'
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plot_lineage_duet = paste0(plotdir,"/", my_plot_name)
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#===================
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# Data for plots
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#===================
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table(plot_df$lineage); str(plot_df$lineage)
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table(my_df$lineage); str(my_df$lineage)
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# subset only lineages1-4
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sel_lineages = c("lineage1"
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, "lineage2"
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, "lineage3"
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, "lineage4")
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, "lineage4"
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#, "lineage5"
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#, "lineage6"
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#, "lineage7")
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#, "lineage7"
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)
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# uncomment as necessary
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df_lin = subset(plot_df, subset = lineage %in% sel_lineages )
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df_lin = subset(my_df, subset = lineage %in% sel_lineages )
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table(df_lin$lineage)
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# refactor
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@ -139,16 +126,8 @@ table(df_lin$lineage)#{RESULT: No of samples within lineage}
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length(unique(df_lin$mutationinformation))#{Result: No. of unique mutations the 4 lineages contribute to}
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length(df_lin$mutationinformation)
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# sanity checks
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# FIXME
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r1 = 2:7 # when merged_df2 used: because there is missing lineages
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if(sum(table(plot_df$lineage)[r1]) == nrow(df_lin)) {
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print ("sanity check passed: numbers match")
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} else{
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print("Error!: check your numbers")
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}
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u2 = unique(plot_df$mutationinformation)
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u2 = unique(my_df$mutationinformation)
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u = unique(df_lin$mutationinformation)
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check = u2[!u2%in%u]; print(check) #{Muts not present within selected lineages}
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@ -173,11 +152,101 @@ names(my_labels) = c('lineage1', 'lineage2', 'lineage3', 'lineage4'
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# , 'lineage5', 'lineage6', 'lineage7'
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)
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# check plot name
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my_plot_name
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plot_lineage_duet
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# output svg
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svg(plot_lineage_duet)
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printFile = ggplot(df, aes(x = duet_scaled
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#svg(plot_lineage_duet)
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p1 = ggplot(df, aes(x = duet_scaled
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, y = duet_outcome))+
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#printFile=geom_density_ridges_gradient(
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geom_density_ridges_gradient(aes(fill = ..x..)
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, scale = 3
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, size = 0.3 ) +
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facet_wrap( ~lineage
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, scales = "free"
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#, switch = 'x'
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, labeller = labeller(lineage = my_labels) ) +
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coord_cartesian( xlim = c(-1, 1)) +
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scale_fill_gradientn(colours = c("#f8766d", "white", "#00bfc4")
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, name = "DUET" ) +
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theme(axis.text.x = element_text(size = my_ats
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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, axis.text.y = element_blank()
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, axis.title.x = element_blank()
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, axis.title.y = element_blank()
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, axis.ticks.y = element_blank()
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, plot.title = element_blank()
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, strip.text = element_text(size = my_als)
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, legend.text = element_text(size = my_als-5)
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, legend.title = element_text(size = my_als)
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)
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print(p1)
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#dev.off()
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#######################################################################
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# lineage distribution plot for dr_muts
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#######################################################################
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#==========================
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# Plot 2: dr muts ONLY
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# x = mcsm_values, y = dist
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# fill = stability
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#============================
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my_plot_name_dr = 'lineage_dist_dr_muts_PS.svg'
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plot_lineage_dr_duet = paste0(plotdir,"/", my_plot_name_dr)
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#===================
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# Data for plots
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#===================
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table(my_df_dr$lineage); str(my_df_dr$lineage)
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# uncomment as necessary
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df_lin_dr = subset(my_df_dr, subset = lineage %in% sel_lineages)
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table(df_lin_dr$lineage)
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# refactor
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df_lin_dr$lineage = factor(df_lin_dr$lineage)
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sum(table(df_lin_dr$lineage)) #{RESULT: Total number of samples for lineage}
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table(df_lin_dr$lineage)#{RESULT: No of samples within lineage}
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length(unique(df_lin_dr$mutationinformation))#{Result: No. of unique mutations the 4 lineages contribute to}
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length(df_lin_dr$mutationinformation)
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u2 = unique(my_df_dr$mutationinformation)
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u = unique(df_lin_dr$mutationinformation)
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check = u2[!u2%in%u]; print(check) #{Muts not present within selected lineages}
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT
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df_dr <- df_lin_dr
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#%%%%%%%%%%%%%%%%%%%%%%%%%
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rm(df_lin_dr)
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#******************
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# generate distribution plot of lineages
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#******************
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# 2 : ggridges (good!)
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my_ats = 15 # axis text size
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my_als = 20 # axis label size
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# check plot name
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plot_lineage_dr_duet
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# output svg
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#svg(plot_lineage_dr_duet)
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p2 = ggplot(df_dr, aes(x = duet_scaled
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, y = duet_outcome))+
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#printFile=geom_density_ridges_gradient(
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@ -198,35 +267,31 @@ printFile = ggplot(df, aes(x = duet_scaled
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, angle = 90
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, hjust = 1
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, vjust = 0.4)
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# , axis.text.y = element_text(size = my_ats
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# , angle = 0
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# , hjust = 1
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# , vjust = 0)
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, axis.text.y = element_blank()
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, axis.title.x = element_blank()
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, axis.title.y = element_blank()
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, axis.ticks.y = element_blank()
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, plot.title = element_blank()
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, strip.text = element_text(size = my_als)
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, legend.text = element_text(size = 10)
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, legend.title = element_text(size = my_als)
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# , legend.position = c(0.3, 0.8)
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# , legend.key.height = unit(1, 'mm')
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#, legend.text = element_text(size = 10)
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#, legend.title = element_text(size = my_als)
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, legend.position = "none"
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)
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print(p2)
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#dev.off()
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#====================
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#combine
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#=======
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# output
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#=======
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lineage_dist_combined = "lineage_dist_combined_PS.svg"
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plot_lineage_dist_combined = paste0(plotdir,"/", lineage_dist_combined)
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svg(plot_lineage_dist_combined, width = 16, height = 12)
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printFile = cowplot::plot_grid(p1, p2
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, label_size = my_als+10)
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print(printFile)
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dev.off()
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#=!=!=!=!=!=!=!
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# COMMENT: Not much differences in the distributions
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# when using merged_df2 or merged_df2_comp.
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# Also, the lineage differences disappear when looking at all muts
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# The pattern we are interested in is possibly only for dr_mutations
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#=!=!=!=!=!=!=!
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#===================================================
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# COMPARING DISTRIBUTIONS: KS test
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# run: "../KS_test_PS.R"
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Reference in a new issue