239 lines
7.1 KiB
R
239 lines
7.1 KiB
R
#!/usr/bin/env Rscript
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#########################################################
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# TASK: producing logo-type plot showing
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# multiple muts per position coloured by aa property
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#########################################################
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#=======================================================================
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# working dir and loading libraries
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getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting")
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getwd()
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source("Header_TT.R")
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source("../functions/plotting_globals.R")
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source("../functions/plotting_data.R")
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source("../functions/combining_dfs_plotting.R")
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###########################################################
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# command line args
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#********************
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drug = 'streptomycin'
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gene = 'gid'
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#===========
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# input
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#===========
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#---------------------
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# call: import_dirs()
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#---------------------
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import_dirs(drug, gene)
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#---------------------------
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# call: plotting_data()
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#---------------------------
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if (!exists("infile_params") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){
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#in_filename_params = paste0(tolower(gene), "_all_params.csv")
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in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
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infile_params = paste0(outdir, "/", in_filename_params)
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cat("\nInput file for mcsm comb data not specified, assuming filename: ", infile_params, "\n")
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}
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# Input 1: read <gene>_comb_afor.csv
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pd_df = plotting_data(infile_params)
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my_df_u = pd_df[[1]] # this forms one of the input for combining_dfs_plotting()
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#--------------------------------
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# call: combining_dfs_plotting()
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#--------------------------------
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if (!exists("infile_metadata") && exists("gene")){
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#if (!is.character(infile_params) && exists("gene")){{
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in_filename_metadata = paste0(tolower(gene), "_metadata.csv") # part combined for gid
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infile_metadata = paste0(outdir, "/", in_filename_metadata)
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cat("\nInput file for gene metadata not specified, assuming filename: ", infile_metadata, "\n")
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}
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# Input 2: read <gene>_meta data.csv
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cat("\nReading meta data file:", infile_metadata)
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gene_metadata <- read.csv(infile_metadata
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, stringsAsFactors = F
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, header = T)
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all_plot_dfs = combining_dfs_plotting(my_df_u
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, gene_metadata
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, lig_dist_colname = 'ligand_distance'
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, lig_dist_cutoff = 10)
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#merged_df2 = all_plot_dfs[[1]]
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merged_df3 = all_plot_dfs[[2]]
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#merged_df2_comp = all_plot_dfs[[3]]
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#merged_df3_comp = all_plot_dfs[[4]]
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#merged_df2_lig = all_plot_dfs[[5]]
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#merged_df3_lig = all_plot_dfs[[6]]
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#===========
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# output
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#===========
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logo_multiple_muts = "logo_multiple_muts.svg"
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plot_logo_multiple_muts = paste0(plotdir,"/", logo_multiple_muts)
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##########################################################################
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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# REASSIGNMENT
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my_df = merged_df3
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#%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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colnames(my_df)
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str(my_df)
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#rownames(my_df) = my_df$mutation
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c1 = unique(my_df$position)
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nrow(my_df)
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# get freq count of positions so you can subset freq<1
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#require(data.table)
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setDT(my_df)[, mut_pos_occurrence := .N, by = .(position)] #189, 36
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table(my_df$position)
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table(my_df$mut_pos_occurrence)
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max_mut = max(table(my_df$position))
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# extract freq_pos>1
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my_data_snp = my_df[my_df$mut_pos_occurrence!=1,]
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u = unique(my_data_snp$position)
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max_mult_mut = max(table(my_data_snp$position))
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if (nrow(my_data_snp) == nrow(my_df) - table(my_df$mut_pos_occurrence)[[1]] ){
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cat("PASS: positions with multiple muts extracted"
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, "\nNo. of mutations:", nrow(my_data_snp)
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, "\nNo. of positions:", length(u)
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, "\nMax no. of muts at any position", max_mult_mut)
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}else{
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cat("FAIL: positions with multiple muts could NOT be extracted"
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, "\nExpected:",nrow(my_df) - table(my_df$mut_pos_occurrence)[[1]]
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, "\nGot:", nrow(my_data_snp) )
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}
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cat("\nNo. of sites with only 1 mutations:", table(my_df$mut_pos_occurrence)[[1]])
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########################################################################
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# end of data extraction and cleaning for_mychisq plots #
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########################################################################
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#==============
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# matrix for_mychisq mutant type
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# frequency of mutant type by position
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#==============
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table(my_data_snp$mutant_type, my_data_snp$position)
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tab_mt = table(my_data_snp$mutant_type, my_data_snp$position)
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class(tab_mt)
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# unclass to convert to matrix
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tab_mt = unclass(tab_mt)
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tab_mt = as.matrix(tab_mt, rownames = T)
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#should be TRUE
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is.matrix(tab_mt)
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rownames(tab_mt) #aa
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colnames(tab_mt) #pos
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#**************
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# Plot 1: mutant logo
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#**************
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p0 = ggseqlogo(tab_mt
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, method = 'custom'
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, seq_type = 'aa') +
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#ylab('my custom height') +
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theme(axis.text.x = element_blank()) +
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theme_logo()+
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scale_x_continuous(breaks = 1:ncol(tab_mt)
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, labels = colnames(tab_mt))+
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scale_y_continuous( breaks = 1:max_mult_mut
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, limits = c(0, max_mult_mut))
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p0
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# further customisation
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p1 = p0 + theme(legend.position = "none"
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, legend.title = element_blank()
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, legend.text = element_text(size = 20)
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, axis.text.x = element_text(size = 17, angle = 90)
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, axis.text.y = element_blank())
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p1
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#==============
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# matrix for wild type
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# frequency of wild type by position
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#==============
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tab_wt = table(my_data_snp$wild_type, my_data_snp$position); tab_wt
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tab_wt = unclass(tab_wt)
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#remove wt duplicates
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wt = my_data_snp[, c("position", "wild_type")]
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wt = wt[!duplicated(wt),]
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tab_wt = table(wt$wild_type, wt$position); tab_wt # should all be 1
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rownames(tab_wt)
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rownames(tab_wt)
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#**************
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# Plot 2: wild_type logo
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#**************
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# sanity check: MUST BE TRUE
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identical(colnames(tab_mt), colnames(tab_wt))
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identical(ncol(tab_mt), ncol(tab_wt))
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p2 = ggseqlogo(tab_wt
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, method = 'custom'
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, seq_type = 'aa'
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#, col_scheme = "taylor"
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#, col_scheme = chemistry2
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) +
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#ylab('my custom height') +
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theme(axis.text.x = element_blank()
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, axis.text.y = element_blank()) +
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theme_logo() +
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scale_x_continuous(breaks = 1:ncol(tab_wt)
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, labels = colnames(tab_wt))
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p2
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# further customise
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p3 = p2 +
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theme(legend.position = "bottom"
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#, legend.title = element_blank()
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, legend.title = element_text("Amino acid properties", size = 20)
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, legend.text = element_text(size = 20)
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, axis.text.x = element_text(size = 17, angle = 90)
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, axis.text.y = element_blank()
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, axis.title.x = element_text(size = 22))+
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labs(x= "Position")
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p3
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# Now combine using cowplot, which ensures the plots are aligned
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suppressMessages( require(cowplot) )
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plot_grid(p1, p3, ncol = 1, align = 'v') #+
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#colour scheme
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#https://rdrr.io/cran/ggseqlogo/src/R/col_schemes.r
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cat("Output plot:", plot_logo_multiple_muts)
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svg(plot_logo_multiple_muts, width = 32, height = 10)
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OutPlot1 = cowplot::plot_grid(p1, p3
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, nrow = 2
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, align = "v"
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, rel_heights = c(3/4, 1/4))
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print(OutPlot1)
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dev.off()
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