154 lines
5.6 KiB
R
Executable file
154 lines
5.6 KiB
R
Executable file
#!/usr/bin/env Rscript
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#########################################################
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# TASK: formatting data that will be used for various plots
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#########################################################
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# load libraries and functions
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library(data.table)
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library(dplyr)
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# ADDED: New
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# geneL_normal = c("pnca")
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# geneL_na = c("gid", "rpob")
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# geneL_ppi2 = c("alr", "embb", "katg", "rpob")
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#========================================================
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# plotting_data(): formatting data for plots
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# input args:
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## input csv file
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## lig cut off dist, default = 10 Ang
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# output: list of 4 dfs, that need to be decompressed
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## my_df
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## my_df_u
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## my_df_u_lig
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## dup_muts
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#========================================================
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#lig_dist_colname = 'ligand_distance' or global var LigDist_colname
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#lig_dist_cutoff = 10 or global var LigDist_cutoff
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plotting_data <- function(df
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, gene # ADDED
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, lig_dist_colname = 'ligand_distance'
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, lig_dist_cutoff = 10
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) {
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my_df = data.frame()
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my_df_u = data.frame()
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my_df_u_lig = data.frame()
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dup_muts = data.frame()
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#===========================
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# Read file: struct params
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#===========================
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#df = read.csv(infile_params, header = T)
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cat("\nInput dimensions:", dim(df))
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#==================================
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# extract unique mutation entries
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#==================================
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# check for duplicate mutations
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if ( length(unique(df$mutationinformation)) != length(df$mutationinformation)){
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cat(paste0("\nCAUTION:", " Duplicate mutations identified"
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, "\nExtracting these...\n"))
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#cat(my_df[duplicated(my_df$mutationinformation),])
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dup_muts = df[duplicated(df$mutationinformation),]
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dup_muts_nu = length(unique(dup_muts$mutationinformation))
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cat(paste0("\nDim of duplicate mutation df:", nrow(dup_muts)
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, "\nNo. of unique duplicate mutations:", dup_muts_nu
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, "\n\nExtracting df with unique mutations only\n"))
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my_df_u = df[!duplicated(df$mutationinformation),]
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} else {
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cat(paste0("\nNo duplicate mutations detected\n"))
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my_df_u = df
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}
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upos = unique(my_df_u$position)
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cat("\nDim of clean df:"); cat(dim(my_df_u), "\n")
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cat("\nNo. of unique mutational positions:"); cat(length(upos), "\n")
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#===============================================
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# ADD : na distance column for genes with nucleic acid affinity
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#===============================================
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# if (tolower(gene)%in%geneL_na){
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#
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# distcol_nca_name = read.csv(infilename_nca, header = F)
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# head(distcol_nca_name)
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# colnames(distcol_nca_name) <- c("mutationinformation", "nca_distance")
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# head(distcol_nca_name)
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# class(distcol_nca_name)
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#
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# mcol = colnames(distcol_nca_name)[colnames(distcol_nca_name)%in%colnames(my_df_u)]
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# mcol
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# head(my_df_u$mutationinformation)
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# head(distcol_nca_name$mutationinformation)
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#
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# my_df_u = merge(my_df_u, distcol_nca_name,
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# by = "mutationinformation",
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# all = T)
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#
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# }
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geneL_na=c("gid","rpob")
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if (tolower(gene)%in%geneL_na){
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infilename_nca = paste0("/home/tanu/git/Misc/mcsm_na_dist/"
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, tolower(gene), "_nca_distances.csv")
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distcol_nca_name = read.csv(infilename_nca, header = F)
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if (tolower(gene)=='rpob'){
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print('WARNING: running special-case handler for rpoB')
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# create 5uhc equivalent column for mutationinformation
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my_df_u$X5uhc_mutationinformation = paste0(my_df_u$wild_type,
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my_df_u$X5uhc_position,
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my_df_u$mutant_type)
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colnames(distcol_nca_name) <- c("X5uhc_mutationinformation", "nca_distance")
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# do stuff here
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mcol = colnames(distcol_nca_name)[colnames(distcol_nca_name)%in%colnames(my_df_u)]
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cat(paste0("\nMerging for gene: ", tolower(gene), "\non column: ", mcol))
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head(my_df_u$mutationinformation)
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head(distcol_nca_name$X5uhc_mutationinformation)
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my_df_u = merge(my_df_u, distcol_nca_name,
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by = "X5uhc_mutationinformation",
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all = T)
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} else {
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head(distcol_nca_name)
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colnames(distcol_nca_name) <- c("mutationinformation", "nca_distance")
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head(distcol_nca_name)
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class(distcol_nca_name)
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mcol = colnames(distcol_nca_name)[colnames(distcol_nca_name)%in%colnames(my_df_u)]
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cat(paste0("\nMerging for gene: ", tolower(gene), "\non column: ", mcol))
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head(my_df_u$mutationinformation)
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head(distcol_nca_name$mutationinformation)
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my_df_u = merge(my_df_u, distcol_nca_name,
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by = "mutationinformation",
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all = T)
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}
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}
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#===============================================
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# extract mutations <10 Angstroms and symbol
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#===============================================
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table(my_df_u[[lig_dist_colname]] < lig_dist_cutoff)
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my_df_u_lig = my_df_u[my_df_u[[lig_dist_colname]] < lig_dist_cutoff,]
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cat(paste0("There are ", nrow(my_df_u_lig), " sites lying within 10\u212b of the ligand\n"))
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# return list of DFs
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my_df = df
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#df_names = c("my_df", "my_df_u", "my_df_u_lig", "dup_muts")
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all_df = list(my_df, my_df_u, my_df_u_lig, dup_muts)
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#all_df = Map(setNames, all_df, df_names)
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return(all_df)
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}
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########################################################################
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# end of data extraction and cleaning for plots #
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########################################################################
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