442 lines
15 KiB
R
442 lines
15 KiB
R
getwd()
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setwd("~/git/LSHTM_analysis/scripts/plotting/")
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getwd()
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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) - no. of NAs(AF|OR) + 1
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# 5) small combined df excluding NAs
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# Dim: dim(#2) - no. of unique NAs - 1
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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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# Installing and loading required packages
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##########################################################
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source("~/git/LSHTM_analysis/scripts/Header_TT.R")
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#require(data.table)
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#require(arsenal)
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#require(compare)
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#library(tidyverse)
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#%% variable assignment: input and output paths & filenames
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drug = "pyrazinamide"
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gene = "pncA"
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gene_match = paste0(gene,"_p.")
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cat(gene_match)
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#=============
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# directories
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#=============
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datadir = paste0("~/git/Data")
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indir = paste0(datadir, "/", drug, "/input")
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outdir = paste0("~/git/Data", "/", drug, "/output")
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#===========
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# input
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#===========
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#in_filename = "mcsm_complex1_normalised.csv"
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in_filename_params = paste0(tolower(gene), "_all_params.csv")
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infile_params = paste0(outdir, "/", in_filename_params)
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cat(paste0("Input file 1:", infile_params) )
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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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# mutations with opposite effects
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out_filename_opp_muts = paste0(tolower(gene), "_muts_opp_effects.csv")
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outfile_opp_muts = paste0(outdir, "/", out_filename_opp_muts)
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#%%===============================================================
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###########################
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# Read file: struct params
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###########################
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cat("Reading struct params including mcsm:"
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, in_filename_params)
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mcsm_data = read.csv(infile_params
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#, row.names = 1
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, stringsAsFactors = F
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, header = T)
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cat("Input dimensions:", dim(mcsm_data)) #416, 86
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# clear variables
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rm(in_filename_params, infile_params)
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str(mcsm_data)
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table(mcsm_data$duet_outcome); sum(table(mcsm_data$duet_outcome) )
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# spelling Correction 1: DUET incase American spelling needed!
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#mcsm_data$duet_outcome[mcsm_data$duet_outcome=="Stabilising"] <- "Stabilizing"
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#mcsm_data$duet_outcome[mcsm_data$duet_outcome=="Destabilising"] <- "Destabilizing"
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# checks: should be the same as above
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table(mcsm_data$duet_outcome); sum(table(mcsm_data$duet_outcome) )
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head(mcsm_data$duet_outcome); tail(mcsm_data$duet_outcome)
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# spelling Correction 2: Ligand incase American spelling needed!
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table(mcsm_data$ligand_outcome); sum(table(mcsm_data$ligand_outcome) )
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#mcsm_data$ligand_outcome[mcsm_data$ligand_outcome=="Stabilising"] <- "Stabilizing"
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#mcsm_data$ligand_outcome[mcsm_data$ligand_outcome=="Destabilising"] <- "Destabilizing"
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# checks: should be the same as above
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table(mcsm_data$ligand_outcome); sum(table(mcsm_data$ligand_outcome) )
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head(mcsm_data$ligand_outcome); tail(mcsm_data$ligand_outcome)
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# muts with opposing effects on protomer and ligand stability
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table(mcsm_data$duet_outcome != mcsm_data$ligand_outcome)
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changes = mcsm_data[which(mcsm_data$duet_outcome != mcsm_data$ligand_outcome),]
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# sanity check: redundant, but uber cautious!
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dl_i = which(mcsm_data$duet_outcome != mcsm_data$ligand_outcome)
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ld_i = which(mcsm_data$ligand_outcome != mcsm_data$duet_outcome)
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cat("Identifying muts with opposite stability effects")
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if(nrow(changes) == (table(mcsm_data$duet_outcome != mcsm_data$ligand_outcome)[[2]]) & identical(dl_i,ld_i)) {
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cat("PASS: muts with opposite effects on stability and affinity correctly identified"
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, "\nNo. of such muts: ", nrow(changes))
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}else {
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cat("FAIL: unsuccessful in extracting muts with changed stability effects")
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}
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#***************************
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# write file: changed muts
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write.csv(changes, outfile_opp_muts)
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cat("Finished writing file for muts with opp effects:"
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, "\nFilename: ", outfile_opp_muts
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, "\nDim:", dim(changes))
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# clear variables
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rm(out_filename_opp_muts, outfile_opp_muts)
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rm(changes, dl_i, ld_i)
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#***************************
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# count na in each column
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na_count = sapply(mcsm_data, function(y) sum(length(which(is.na(y))))); na_count
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# sort by mutationinformation
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##mcsm_data = mcsm_data[order(mcsm_data$mutationinformation),]
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##head(mcsm_data$mutationinformation)
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df_ncols = ncol(mcsm_data)
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# REMOVE as this is dangerous due to dup muts
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# get freq count of positions and add to the df
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#setDT(mcsm_data)[, occurrence := .N, by = .(position)]
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#cat("Added 1 col: position frequency to see which posn has how many muts"
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# , "\nNo. of cols now", ncol(mcsm_data)
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# , "\nNo. of cols before: ", df_ncols)
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#pos_count_check = data.frame(mcsm_data$position, mcsm_data$occurrence)
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# check duplicate muts
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if (length(unique(mcsm_data$mutationinformation)) == length(mcsm_data$mutationinformation)){
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cat("No duplicate mutations in mcsm data")
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}else{
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dup_muts = mcsm_data[duplicated(mcsm_data$mutationinformation),]
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dup_muts_nu = length(unique(dup_muts$mutationinformation))
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cat(paste0("CAUTION:", nrow(dup_muts), " Duplicate mutations identified"
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, "\nOf these, no. of unique mutations are:", dup_muts_nu
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, "\nExtracting df with unique mutations only"))
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mcsm_data_u = mcsm_data[!duplicated(mcsm_data$mutationinformation),]
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}
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if (nrow(mcsm_data_u) == length(unique(mcsm_data$mutationinformation))){
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cat("Df without duplicate mutations successfully extracted")
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} else{
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cat("FAIL: could not extract clean df!")
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quit()
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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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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# FIXME: remove
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# counting NAs in AF, OR cols:
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if (identical(sum(is.na(gene_metadata$OR))
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, sum(is.na(gene_metadata$pvalue))
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, sum(is.na(gene_metadata$AF)))){
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cat("PASS: NA count match for OR, pvalue and AF\n")
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na_count = sum(is.na(gene_metadata$AF))
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cat("No. of NAs: ", sum(is.na(gene_metadata$OR)))
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} else{
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cat("FAIL: NA count mismatch"
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, "\nNA in OR: ", sum(is.na(gene_metadata$OR))
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, "\nNA in pvalue: ", sum(is.na(gene_metadata$pvalue))
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, "\nNA in AF:", sum(is.na(gene_metadata$AF)))
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}
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# clear variables
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rm(in_filename_gene_metadata, infile_gene_metadata)
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str(gene_metadata)
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# sort by position (same as mcsm_data)
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# earlier it was mutationinformation
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#head(gene_metadata$mutationinformation)
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#gene_metadata = gene_metadata[order(gene_metadata$mutationinformation),]
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##head(gene_metadata$mutationinformation)
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head(gene_metadata$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: two dfs with NA
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# merged_df2
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###########################
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head(mcsm_data$mutationinformation)
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head(gene_metadata$mutationinformation)
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# Find common columns b/w two df
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merging_cols = intersect(colnames(mcsm_data), 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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#=============
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# merged_df2): gene_metadata + mcsm_data
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#==============
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merged_df2 = merge(x = gene_metadata
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, y = mcsm_data
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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) #4520, 11
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)
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head(merged_df2$position)
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# FIXME: count how many unique muts have entries in meta data
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# sanity check
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cat("Checking nrows in merged_df2")
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if(nrow(gene_metadata) == nrow(merged_df2)){
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cat("nrow(merged_df2) = nrow (gene associated gene_metadata)"
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,"\nExpected no. of rows: ",nrow(gene_metadata)
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,"\nGot no. of rows: ", nrow(merged_df2))
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} else{
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cat("nrow(merged_df2)!= nrow(gene associated gene_metadata)"
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, "\nExpected no. of rows after merge: ", nrow(gene_metadata)
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, "\nGot no. of rows: ", nrow(merged_df2)
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, "\nFinding discrepancy")
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merged_muts_u = unique(merged_df2$mutationinformation)
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meta_muts_u = unique(gene_metadata$mutationinformation)
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# find the index where it differs
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unique(meta_muts_u[! meta_muts_u %in% merged_muts_u])
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}
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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# sort by position
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head(merged_df2$position)
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merged_df2 = merged_df2[order(merged_df2$position),]
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head(merged_df2$position)
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merged_df2v3 = merge(x = gene_metadata
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, y = mcsm_data
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, by = merging_cols
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, all = T)
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merged_df2v2 = merge(x = gene_metadata
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, y = mcsm_data
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, by = merging_cols
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, all.x = T)
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#!=!=!=!=!=!=!=!
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# COMMENT: used all.y since position 186 is not part of the struc,
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# hence doesn"t have a mcsm value
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# but 186 is associated with mutation
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#!=!=!=!=!=!=!=!
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# should be False
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identical(merged_df2, merged_df2v2)
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table(merged_df2$position%in%merged_df2v2$position)
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rm(merged_df2v2)
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#!!!!!!!!!!! check why these differ
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#########
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# merge 3b (merged_df3):remove duplicate mutation information
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#########
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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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#==#=#=#=#=#=#
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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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merged_df3 = merged_df2[!duplicated(merged_df2$mutationinformation),]
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head(merged_df3$position); tail(merged_df3$position) # should be sorted
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# sanity check
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cat("Checking nrows in merged_df3")
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if(nrow(mcsm_data) == nrow(merged_df3)){
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cat("PASS: No. of rows match with mcsm_data"
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,"\nExpected no. of rows: ", nrow(mcsm_data)
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,"\nGot no. of rows: ", nrow(merged_df3))
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} else {
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cat("FAIL: No. of rows mismatch"
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, "\nNo. of rows mcsm_data: ", nrow(mcsm_data)
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, "\nNo. of rows merged_df3: ", nrow(merged_df3))
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}
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# counting NAs in AF, OR cols in merged_df3
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# this is becuase mcsm has no AF, OR cols,
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# so you cannot count NAs
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if (identical(sum(is.na(merged_df3$OR))
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, sum(is.na(merged_df3$pvalue))
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, sum(is.na(merged_df3$AF)))){
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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$AF))
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cat("No. of NAs: ", sum(is.na(merged_df3$OR)))
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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$OR))
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, "\nNA in pvalue: ", sum(is.na(merged_df3$pvalue))
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, "\nNA in AF:", sum(is.na(merged_df3$AF)))
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}
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###########################
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# 4: merging two dfs: without NA
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###########################
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#########
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# merge 4a (merged_df2_comp): same as merge 1 but excluding NA
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#########
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cat("Merging dfs without any NAs: big df (1-many relationship b/w id & mut)"
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,"\nlinking col: Mutationinforamtion"
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,"\nfilename: merged_df2_comp")
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merged_df2_comp = merged_df2[!is.na(merged_df2$AF),]
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#merged_df2_comp = merged_df2[!duplicated(merged_df2$mutationinformation),]
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# sanity check
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cat("Checking nrows in merged_df2_comp")
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if(nrow(merged_df2_comp) == (nrow(merged_df2) - na_count + 1)){
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cat("PASS: No. of rows match"
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,"\nDim of merged_df2_comp: "
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,"\nExpected no. of rows: ", nrow(merged_df2) - na_count + 1
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, "\nNo. of rows: ", nrow(merged_df2_comp)
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, "\nNo. of cols: ", ncol(merged_df2_comp))
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}else{
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cat("FAIL: No. of rows mismatch"
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,"\nExpected no. of rows: ", nrow(merged_df2) - na_count + 1
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,"\nGot no. of rows: ", nrow(merged_df2_comp))
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}
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#########
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# merge 4b (merged_df3_comp): remove duplicate mutation information
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#########
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merged_df3_comp = merged_df2_comp[!duplicated(merged_df2_comp$mutationinformation),]
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cat("Dim of merged_df3_comp: "
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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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# alternate way of deriving merged_df3_comp
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foo = merged_df3[!is.na(merged_df3$AF),]
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# compare dfs: foo and merged_df3_com
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all.equal(foo, merged_df3)
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summary(comparedf(foo, merged_df3))
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# sanity check
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cat("Checking nrows in merged_df3_comp")
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if(nrow(merged_df3_comp) == nrow(merged_df3)){
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cat("NO NAs detected in merged_df3 in AF|OR cols"
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,"\nNo. of rows are identical: ", nrow(merged_df3))
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} else{
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if(nrow(merged_df3_comp) == nrow(merged_df3) - na_count_df3) {
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cat("PASS: NAs detected in merged_df3 in AF|OR cols"
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, "\nNo. of NAs: ", na_count_df3
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, "\nExpected no. of rows in merged_df3_comp: ", 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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}
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#=============== end of combining df
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#*********************
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# writing 1 file in the style of a loop: merged_df3
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# print(output dir)
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#i = "merged_df3"
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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
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# ,"\nPath: ", outdir)
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#template: write.csv(merged_df3, "merged_df3.csv")
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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)))
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#%% write_output files; all 4 files:
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outvars = c("merged_df2"
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, "merged_df3"
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, "merged_df2_comp"
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, "merged_df3_comp")
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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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# cat(i, "\n")
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out_filename = paste0(i, ".csv")
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# cat(out_filename, "\n")
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# cat("getting value of variable: ", get(i))
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outfile = paste0(outdir, "/", out_filename)
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# cat("Full output path: ", outfile, "\n")
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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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# alternate way to replace with implicit loop
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# FIXME
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#sapply(outvars, function(x, y) write.csv(get(outvars), paste0(outdir, "/", outvars, ".csv")))
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#*************************
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# clear variables
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rm(mcsm_data, gene_metadata, foo, drug, gene, gene_match, indir, merged_muts_u, meta_muts_u, na_count, df_ncols, outdir)
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rm(pos_count_check)
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#============================= end of script
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