435 lines
No EOL
14 KiB
R
435 lines
No EOL
14 KiB
R
#!/usr/bin/env Rscript
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#########################################################
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# TASK: To combine struct params and meta data for plotting
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# Input csv files:
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# 1) <gene>_all_params.csv
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# 2) <gene>_meta_data.csv
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# Output:
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# 1) muts with opposite effects on stability
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# 2) large combined df including NAs for AF, OR,etc
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# Dim: same no. of rows as gene associated meta_data_with_AFandOR
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# 3) small combined df including NAs for AF, OR, etc.
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# Dim: same as mcsm data
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# 4) large combined df excluding NAs
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# Dim: dim(#1) - na_count_df2
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# 5) small combined df excluding NAs
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# Dim: dim(#2) - na_count_df3
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# This script is sourced from other .R scripts for plotting
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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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require("getopt", quietly = TRUE) # cmd parse arguments
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# load functions
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source("~/git/LSHTM_analysis/scripts/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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#############################################################
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# command line args
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#********************
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# !!!FUTURE TODO!!!
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# Can pass additional params of output/plot dir by user.
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# Not strictly required for my workflow since it is optimised
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# to have a streamlined input/output flow without filename worries.
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#********************
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spec = matrix(c(
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"drug" ,"d", 1, "character",
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"gene" ,"g", 1, "character",
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"data" ,"f", 2, "character"
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), byrow = TRUE, ncol = 4)
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opt = getopt(spec)
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#FIXME: detect if script running from cmd, then set these
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drug = opt$drug
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gene = opt$gene
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infile = opt$data
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# hardcoding when not using cmd
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#drug = "streptomycin"
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#gene = "gid"
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if(is.null(drug)|is.null(gene)) {
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stop("Missing arguments: --drug and --gene must both be specified (case-sensitive)")
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}
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#########################################################
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# call functions with relevant args
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#***********************************
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# import_dirs(): returns
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# datadir
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# indir
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# outdir
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# plotdir
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# dr_muts_col
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# other_muts_col
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# resistance_col
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#***********************************
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import_dirs(drug, gene)
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#***********************************
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# plotting_data(): returns
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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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#infile = "/home/tanu/git/Data/streptomycin/output/gid_comb_stab_struc_params.csv"
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if (!exists("infile") && exists("gene")){
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#if (!is.character(infile) && 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_stab_struc_params.csv") # part combined for gid
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in_filename_params = paste0(tolower(gene), "_comb_afor.csv") # part combined for gid
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infile = paste0(outdir, "/", in_filename_params)
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cat("\nInput file not specified, assuming filename: ", infile, "\n")
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}
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# Get the DFs out of plotting_data()
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pd_df = plotting_data(infile)
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my_df = pd_df[[1]]
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my_df_u = pd_df[[2]]
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my_df_u_lig = pd_df[[3]]
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dup_muts = pd_df[[4]]
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cat(paste0("Directories imported:"
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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(paste0("\nVariables 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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, "\n"))
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#========================================================
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#===========
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# input
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#===========
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#in_file1: output of plotting_data.R
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# my_df_u
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# infile 2: gene associated meta data
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#in_filename_gene_metadata = paste0(tolower(gene), "_meta_data_with_AFandOR.csv")
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in_filename_gene_metadata = paste0(tolower(gene), "_metadata.csv")
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infile_gene_metadata = paste0(outdir, "/", in_filename_gene_metadata)
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cat(paste0("Input infile 2:", infile_gene_metadata))
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#===========
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# output
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#===========
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# other variables that you can write
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# primarily called by other scripts for plotting
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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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# 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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#%%===============================================================
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###########################
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# 2: Read file: <gene>_meta data.csv
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###########################
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cat("Reading meta data file:", infile_gene_metadata)
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gene_metadata <- read.csv(infile_gene_metadata
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, stringsAsFactors = F
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, header = T)
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cat("Dim:", dim(gene_metadata))
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table(gene_metadata$mutation_info)
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# counting NAs in AF, OR cols
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# or_mychisq
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if (identical(sum(is.na(my_df_u$or_mychisq))
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, sum(is.na(my_df_u$pval_fisher))
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, sum(is.na(my_df_u$af)))){
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cat("\nPASS: NA count match for OR, pvalue and AF\n")
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na_count = sum(is.na(my_df_u$af))
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cat("\nNo. of NAs: ", sum(is.na(my_df_u$or_mychisq)))
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} else{
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cat("\nFAIL: NA count mismatch"
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, "\nNA in OR: ", sum(is.na(my_df_u$or_mychisq))
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, "\nNA in pvalue: ", sum(is.na(my_df_u$pval_fisher))
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, "\nNA in AF:", sum(is.na(my_df_u$af)))
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}
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# or kin
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if (identical(sum(is.na(my_df_u$or_kin))
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, sum(is.na(my_df_u$pwald_kin))
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, sum(is.na(my_df_u$af_kin)))){
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cat("\nPASS: NA count match for OR, pvalue and AF\n from Kinship matrix calculations")
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na_count = sum(is.na(my_df_u$af_kin))
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cat("\nNo. of NAs: ", sum(is.na(my_df_u$or_kin)))
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} else{
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cat("\nFAIL: NA count mismatch"
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, "\nNA in OR: ", sum(is.na(my_df_u$or_kin))
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, "\nNA in pvalue: ", sum(is.na(my_df_u$pwald_kin))
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, "\nNA in AF:", sum(is.na(my_df_u$af_kin)))
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}
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str(gene_metadata)
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###################################################################
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# combining: PS
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###################################################################
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# sort by position (same as my_df)
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head(gene_metadata$position)
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gene_metadata = gene_metadata[order(gene_metadata$position),]
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head(gene_metadata$position)
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#=========================
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# Merge 1: merged_df2
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# dfs with NAs in ORs
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#=========================
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head(my_df_u$mutationinformation)
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head(gene_metadata$mutationinformation)
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# Find common columns b/w two df
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merging_cols = intersect(colnames(my_df_u), colnames(gene_metadata))
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cat(paste0("Merging dfs with NAs: big df (1-many relationship b/w id & mut)"
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, "\nNo. of merging cols:", length(merging_cols)
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, "\nMerging columns identified:"))
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print(merging_cols)
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# using all common cols create confusion, so pick one!
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# merging_cols = merging_cols[[1]]
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merging_cols = 'mutationinformation'
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cat("\nLinking column being used: mutationinformation")
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# important checks!
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table(nchar(my_df_u$mutationinformation))
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table(nchar(my_df_u$wild_type))
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table(nchar(my_df_u$mutant_type))
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table(nchar(my_df_u$position))
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# all.y because x might contain non-structural positions!
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merged_df2 = merge(x = gene_metadata
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, y = my_df_u
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, by = merging_cols
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, all.y = T)
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cat("Dim of merged_df2: ", dim(merged_df2))
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# Remove duplicate columns
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dup_cols = names(merged_df2)[grepl("\\.x$|\\.y$", names(merged_df2))]
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cat("\nNo. of duplicate cols:", length(dup_cols))
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check_df_cols = merged_df2[dup_cols]
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identical(check_df_cols$wild_type.x, check_df_cols$wild_type.y)
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identical(check_df_cols$position.x, check_df_cols$position.y)
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identical(check_df_cols$mutant_type.x, check_df_cols$mutant_type.y)
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# False: because some of the ones with OR don't have mutation
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identical(check_df_cols$mutation.x, check_df_cols$mutation.y)
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cols_to_drop = names(merged_df2)[grepl("\\.y",names(merged_df2))]
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cat("\nNo. of cols to drop:", length(cols_to_drop))
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# Drop duplicate columns
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merged_df2 = merged_df2[,!(names(merged_df2)%in%cols_to_drop)]
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# Drop the '.x' suffix in the colnames
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names(merged_df2)[grepl("\\.x$|\\.y$", names(merged_df2))]
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colnames(merged_df2) <- gsub("\\.x$", "", colnames(merged_df2))
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names(merged_df2)[grepl("\\.x$|\\.y$", names(merged_df2))]
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head(merged_df2$position)
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# sanity check
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cat("Checking nrows in merged_df2")
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if(nrow(gene_metadata) == nrow(merged_df2)){
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cat("PASS: nrow(merged_df2) = nrow (gene associated gene_metadata)"
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,"\nExpected no. of rows: ",nrow(gene_metadata)
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,"\nGot no. of rows: ", nrow(merged_df2))
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} else{
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cat("FAIL: nrow(merged_df2)!= nrow(gene associated gene_metadata)"
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, "\nExpected no. of rows after merge: ", nrow(gene_metadata)
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, "\nGot no. of rows: ", nrow(merged_df2)
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, "\nFinding discrepancy")
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merged_muts_u = unique(merged_df2$mutationinformation)
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meta_muts_u = unique(gene_metadata$mutationinformation)
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# find the index where it differs
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unique(meta_muts_u[! meta_muts_u %in% merged_muts_u])
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quit()
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}
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#=========================
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# Merge 2: merged_df3
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# dfs with NAs in ORs
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#
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# Cannot trust lineage, country from this df as the same mutation
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# can have many different lineages
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# but this should be good for the numerical corr plots
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#=========================
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# remove duplicated mutations
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cat("Merging dfs without NAs: small df (removing muts with no AF|OR associated)"
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,"\nCannot trust lineage info from this"
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,"\nlinking col: mutationinforamtion"
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,"\nfilename: merged_df3")
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merged_df3 = merged_df2[!duplicated(merged_df2$mutationinformation),]
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head(merged_df3$position); tail(merged_df3$position) # should be sorted
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# sanity check
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cat("Checking nrows in merged_df3")
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if(nrow(my_df_u) == nrow(merged_df3)){
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cat("PASS: No. of rows match with my_df"
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,"\nExpected no. of rows: ", nrow(my_df_u)
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,"\nGot no. of rows: ", nrow(merged_df3))
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} else {
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cat("FAIL: No. of rows mismatch"
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, "\nNo. of rows my_df: ", nrow(my_df_u)
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, "\nNo. of rows merged_df3: ", nrow(merged_df3))
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quit()
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}
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# counting NAs in AF, OR cols in merged_df3
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# this is because mcsm has no AF, OR cols,
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# so you cannot count NAs
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if (identical(sum(is.na(merged_df3$or_kin))
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, sum(is.na(merged_df3$pwald_kin))
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, sum(is.na(merged_df3$af_kin)))){
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cat("PASS: NA count match for OR, pvalue and AF\n")
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na_count_df3 = sum(is.na(merged_df3$af_kin))
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cat("No. of NAs: ", sum(is.na(merged_df3$or_kin)))
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} else{
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cat("FAIL: NA count mismatch"
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, "\nNA in OR: ", sum(is.na(merged_df3$or_kin))
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, "\nNA in pvalue: ", sum(is.na(merged_df3$pwald_kin))
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, "\nNA in AF:", sum(is.na(merged_df3$af_kin)))
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}
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#=========================
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# Merge3: merged_df2_comp
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# same as merge 1 but excluding NAs from ORs, etc.
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#=========================
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cat("Merging dfs without any NAs: big df (1-many relationship b/w id & mut)"
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,"\nfilename: merged_df2_comp")
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na_count_df2 = sum(is.na(merged_df2$af))
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merged_df2_comp = merged_df2[!is.na(merged_df2$af),]
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# sanity check: no +-1 gymnastics
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cat("Checking nrows in merged_df2_comp")
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if(nrow(merged_df2_comp) == (nrow(merged_df2) - na_count_df2)){
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cat("\nPASS: No. of rows match"
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,"\nDim of merged_df2_comp: "
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,"\nExpected no. of rows: ", nrow(merged_df2) - na_count_df2
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, "\nNo. of rows: ", nrow(merged_df2_comp)
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, "\nNo. of cols: ", ncol(merged_df2_comp))
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}else{
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cat("FAIL: No. of rows mismatch"
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,"\nExpected no. of rows: ", nrow(merged_df2) - na_count_df2
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,"\nGot no. of rows: ", nrow(merged_df2_comp))
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}
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#=========================
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# Merge4: merged_df3_comp
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# same as merge 2 but excluding NAs from ORs, etc or
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# remove duplicate mutation information
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#=========================
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na_count_df3 = sum(is.na(merged_df3$af))
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#merged_df3_comp = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),] # a way
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merged_df3_comp = merged_df3[!is.na(merged_df3$af),] # another way
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cat("Checking nrows in merged_df3_comp")
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if(nrow(merged_df3_comp) == (nrow(merged_df3) - na_count_df3)){
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cat("\nPASS: No. of rows match"
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,"\nDim of merged_df3_comp: "
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,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
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, "\nNo. of rows: ", nrow(merged_df3_comp)
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, "\nNo. of cols: ", ncol(merged_df3_comp))
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}else{
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cat("FAIL: No. of rows mismatch"
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,"\nExpected no. of rows: ", nrow(merged_df3) - na_count_df3
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,"\nGot no. of rows: ", nrow(merged_df3_comp))
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}
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# alternate way of deriving merged_df3_comp
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foo = merged_df3[!is.na(merged_df3$af),]
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bar = merged_df3_comp[!duplicated(merged_df3_comp$mutationinformation),]
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# compare dfs: foo and merged_df3_com
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all.equal(foo, bar)
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#summary(comparedf(foo, bar))
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#==============================================================
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#####################################################################
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# Combining: LIG
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#####################################################################
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#=========================
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# Merges 5-8
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#=========================
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merged_df2_lig = merged_df2[merged_df2$ligand_distance<10,]
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merged_df2_comp_lig = merged_df2_comp[merged_df2_comp$ligand_distance<10,]
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merged_df3_lig = merged_df3[merged_df3$ligand_distance<10,]
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merged_df3_comp_lig = merged_df3_comp[merged_df3_comp$ligand_distance<10,]
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# sanity check
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if (nrow(merged_df3_lig) == nrow(my_df_u_lig)){
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print("PASS: verified merged_df3_lig")
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}else{
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cat(paste0("FAIL: nrow mismatch for merged_df3_lig"
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, "\nExpected:", nrow(my_df_u_lig)
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, "\nGot:", nrow(merged_df3_lig)))
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}
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#==============================================================
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#################
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# OPTIONAL: write output files in one go
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#################
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#outvars = c(#"merged_df2",
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#"merged_df2_comp",
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#"merged_df2_lig",
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#"merged_df2_comp_lig",
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#"meregd_df3_comp"
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#"merged_df3_comp_lig",
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#"merged_df3",
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#"merged_df3_lig")
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#cat("Writing output files: "
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#, "\nPath:", outdir)
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#for (i in outvars){
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#out_filename = paste0(i, ".csv")
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#outfile = paste0(outdir, "/", out_filename)
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#cat("Writing output file:"
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# ,"\nFilename: ", out_filename,"\n")
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#write.csv(get(i), outfile, row.names = FALSE)
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#cat("Finished writing: ", outfile
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# , "\nNo. of rows: ", nrow(get(i))
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# , "\nNo. of cols: ", ncol(get(i)), "\n")
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#}
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# clear variables
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rm(foo, bar, gene_metadata
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, in_filename_params, infile_params, merging_cols
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, in_filename_gene_metadata, infile_gene_metadata)
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#==========================================================================
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# end of script
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##========================================================================= |