updated combining df scripts for duet & lig
This commit is contained in:
parent
c184841951
commit
96ebb85069
3 changed files with 458 additions and 342 deletions
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@ -1,17 +1,17 @@
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getwd()
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setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/")
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getwd()
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#########################################################
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# TASK: To combine mcsm and meta data with af and or
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# This script doesn't output anything, but can do if needed.
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# This script is sourced from other .R scripts for plotting
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#########################################################
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getwd()
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setwd('~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/')
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getwd()
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##########################################################
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# Installing and loading required packages
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##########################################################
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########################################################################
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# Installing and loading required packages #
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########################################################################
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#source("Header_TT.R")
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source('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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@ -22,14 +22,58 @@ getwd()
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# output of step 4 mcsm_pipeline
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#################################
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inDir = "~/git/Data/pyrazinamide/input/processed/"
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inFile = paste0(inDir, "mcsm_complex1_normalised.csv"); inFile
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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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mcsm_data = read.csv(inFile
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#===========
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# input
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#===========
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# infile1: mCSM data
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#indir = '~/git/Data/pyrazinamide/input/processed/'
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indir = paste0('~/git/Data', '/', drug, '/', 'output') # revised {TODO: change in mcsm pipeline}
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in_filename = 'mcsm_complex1_normalised.csv'
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infile = paste0(indir, '/', in_filename)
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cat(paste0('Reading infile1: mCSM output file', ' ', infile) )
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# infile2: gene associated meta data combined with AF and OR
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#indir: same as above
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in_filename_comb = paste0(tolower(gene), '_meta_data_with_AFandOR.csv')
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infile_comb = paste0(indir, '/', in_filename_comb)
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cat(paste0('Reading infile2: gene associated combined metadata:', infile_comb))
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#===========
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# output
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#===========
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# Uncomment if and when required to output
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outdir = paste0('~/git/Data', '/', drug, '/', 'output') #same as indir
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cat('Output dir: ', outdir)
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#out_filename = paste0(tolower(gene), 'XXX')
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#outfile = paste0(outdir, '/', out_filename)
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#cat(paste0('Output file with full path:', outfile))
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#%% end of variable assignment for input and output files
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#################################
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# Read file: normalised file
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# output of step 4 mcsm_pipeline
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#################################
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cat('Reading mcsm_data:'
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, '\nindir: ', indir
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, '\ninfile_comb: ', in_filename)
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mcsm_data = read.csv(infile
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, row.names = 1
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, stringsAsFactors = F
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, header = T)
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rm(inDir, inFile)
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cat('Read mcsm_data file:'
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, '\nNo.of rows: ', nrow(mcsm_data)
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, '\nNo. of cols:', ncol(mcsm_data))
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# clear variables
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rm(in_filename, infile)
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str(mcsm_data)
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@ -53,6 +97,35 @@ mcsm_data$Lig_outcome[mcsm_data$Lig_outcome=='Destabilizing'] <- 'Destabilising'
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table(mcsm_data$Lig_outcome); sum(table(mcsm_data$Lig_outcome) )
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head(mcsm_data$Lig_outcome); tail(mcsm_data$Lig_outcome)
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# muts with opposing effects on protomer and ligand stability
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table(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome)
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changes = mcsm_data[which(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome),]
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# sanity check: redundant, but uber cautious!
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dl_i = which(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome)
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ld_i = which(mcsm_data$Lig_outcome != mcsm_data$DUET_outcome)
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if(nrow(changes) == (table(mcsm_data$DUET_outcome != mcsm_data$Lig_outcome)[[2]]) & identical(dl_i,ld_i)) {
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cat('PASS: muts with opposite effects on stability and affinity identified correctly'
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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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out_filename = 'muts_opp_effects.csv'
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outfile = paste0(outdir, '/', out_filename)
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cat('Writing file for muts with opp effects:'
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, '\nFilename: ', outfile
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, '\nPath: ', outdir)
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write.csv(changes, outfile)
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#****************************
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# clear variables
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rm(out_filename, outfile)
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rm(changes, dl_i, ld_i)
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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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@ -60,23 +133,33 @@ na_count = sapply(mcsm_data, function(y) sum(length(which(is.na(y))))); na_count
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mcsm_data = mcsm_data[order(mcsm_data$Mutationinformation),]
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head(mcsm_data$Mutationinformation)
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orig_col = ncol(mcsm_data)
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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: ', orig_col)
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pos_count_check = data.frame(mcsm_data$Position, mcsm_data$occurrence)
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###########################
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# 2: Read file: meta data with AFandOR
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###########################
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cat('Reading combined meta data and AFandOR file:'
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, '\nindir: ', indir
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, '\ninfile_comb: ', in_filename_comb)
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inDir = "~/git/Data/pyrazinamide/input/processed/"
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inFile2 = paste0(inDir, "meta_data_with_AFandOR.csv"); inFile2
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meta_with_afor <- read.csv(inFile2
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meta_with_afor <- read.csv(infile_comb
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, stringsAsFactors = F
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, header = T)
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rm(inDir, inFile2)
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cat('Read mcsm_data file:'
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, '\nNo.of rows: ', nrow(meta_with_afor)
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, '\nNo. of cols:', ncol(meta_with_afor))
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# clear variables
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rm(in_filename_comb, infile_comb)
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str(meta_with_afor)
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@ -85,113 +168,45 @@ head(meta_with_afor$Mutationinformation)
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meta_with_afor = meta_with_afor[order(meta_with_afor$Mutationinformation),]
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head(meta_with_afor$Mutationinformation)
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# sanity check: should be True for all the mentioned columns
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#is.numeric(meta_with_afor$OR)
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na_var = c("AF", "OR", "pvalue", "logor", "neglog10pvalue")
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c1 = NULL
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for (i in na_var){
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print(i)
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c0 = is.numeric(meta_with_afor[,i])
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c1 = c(c0, c1)
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if ( all(c1) ){
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print("Sanity check passed: These are all numeric cols")
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} else{
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print("Error: Please check your respective data types")
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}
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}
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# If OR, and P value are not numeric, then convert to numeric and then count
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# else they will say 0
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na_count = sapply(meta_with_afor, function(y) sum(length(which(is.na(y))))); na_count
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str(na_count)
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# compare if the No of "NA" are the same for all these cols
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na_len = NULL
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for (i in na_var){
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temp = na_count[[i]]
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na_len = c(na_len, temp)
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}
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# extract how many NAs:
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# should be all TRUE
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# should be a single number since
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# all the cols should have "equal" and "same" no. of NAs
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my_nrows = NULL
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for ( i in 1: (length(na_len)-1) ){
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#print(compare(na_len[i]), na_len[i+1])
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c = compare(na_len[i], na_len[i+1])
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if ( c$result ) {
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my_nrows = na_len[i] }
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else {
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print("Error: Please check your numbers")
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}
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}
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my_nrows
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#=#=#=#=#=#=#=#=#
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# COMMENT: AF, OR, pvalue, logor and neglog10pvalue
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# these are the same 7 ones
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#=#=#=#=#=#=#=#=#
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# sanity check
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#which(is.na(meta_with_afor$OR))
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# initialise an empty df with nrows as extracted above
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na_count_df = data.frame(matrix(vector(mode = 'numeric'
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# , length = length(na_var)
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)
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, nrow = my_nrows
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# , ncol = length(na_var)
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))
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# populate the df with the indices of the cols that are NA
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for (i in na_var){
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print(i)
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na_i = which(is.na(meta_with_afor[i]))
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na_count_df = cbind(na_count_df, na_i)
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colnames(na_count_df)[which(na_var == i)] <- i
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}
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# Now compare these indices to ensure these are the same
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c2 = NULL
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for ( i in 1: ( length(na_count_df)-1 ) ) {
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# print(na_count_df[i] == na_count_df[i+1])
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c1 = identical(na_count_df[[i]], na_count_df[[i+1]])
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c2 = c(c1, c2)
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if ( all(c2) ) {
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print("Sanity check passed: The indices for AF, OR, etc are all the same")
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} else {
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print ("Error: Please check indices which are NA")
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}
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}
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rm( c, c0, c1, c2, i, my_nrows
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, na_count, na_i, na_len
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, na_var, temp
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, na_count_df
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, pos_count_check )
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###########################
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# 3:merging two dfs: with NA
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# 3: merging two dfs: with NA
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###########################
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# link col name = 'Mutationinforamtion'
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cat('Merging dfs with NAs: big df (1-many relationship b/w id & mut)'
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,'\nlinking col: Mutationinforamtion'
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,'\nfilename: merged_df2')
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# link col name = Mutationinforamtion
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head(mcsm_data$Mutationinformation)
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head(meta_with_afor$Mutationinformation)
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#########
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# merge 1a: meta data with mcsm
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# merge 3a: meta data with mcsm
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#########
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merged_df2 = merge(x = meta_with_afor
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,y = mcsm_data
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, by = "Mutationinformation"
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, by = 'Mutationinformation'
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, all.y = T)
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cat('Dim of merged_df2: '
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, '\nNo. of rows: ', nrow(merged_df2)
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, '\nNo. of cols: ', ncol(merged_df2))
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head(merged_df2$Position)
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if(nrow(meta_with_afor) == nrow(merged_df2)){
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cat('nrow(merged_df2) = nrow (gene associated metadata)'
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,'\nExpected no. of rows: ',nrow(meta_with_afor)
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,'\nGot no. of rows: ', nrow(merged_df2))
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} else{
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cat('nrow(merged_df2)!= nrow(gene associated metadata)'
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, '\nExpected no. of rows after merge: ', nrow(meta_with_afor)
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, '\nGot no. of rows: ', nrow(merged_df2)
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, '\nFinding discrepancy')
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merged_muts_u = unique(merged_df2$Mutationinformation)
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meta_muts_u = unique(meta_with_afor$Mutationinformation)
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# find the index where it differs
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unique(meta_muts_u[! meta_muts_u %in% merged_muts_u])
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}
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# sort by Position
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head(merged_df2$Position)
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merged_df2 = merged_df2[order(merged_df2$Position),]
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@ -199,12 +214,12 @@ head(merged_df2$Position)
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merged_df2v2 = merge(x = meta_with_afor
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,y = mcsm_data
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, by = "Mutationinformation"
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, by = 'Mutationinformation'
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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 with mutation
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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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@ -214,8 +229,12 @@ table(merged_df2$Position%in%merged_df2v2$Position)
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rm(merged_df2v2)
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#########
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# merge 1b:remove duplicate mutation information
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# merge 3b:remove duplicate mutation information
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#########
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cat('Merging dfs with NAs: small df (removing duplicate muts)'
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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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@ -228,9 +247,13 @@ head(merged_df3$Position); tail(merged_df3$Position) # should be sorted
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# sanity checks
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# nrows of merged_df3 should be the same as the nrows of mcsm_data
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if(nrow(mcsm_data) == nrow(merged_df3)){
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print("sanity check: Passed")
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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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print("Error!: check data, nrows is not as expected")
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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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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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@ -250,19 +273,31 @@ if(nrow(mcsm_data) == nrow(merged_df3)){
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#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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###########################
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# 3b :merging two dfs: without NA
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# 4: merging two dfs: without 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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#########
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# merge 2a:same as merge 1 but excluding NA
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# merge 4a: same as merge 1 but excluding NA
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#########
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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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cat('Dim of merged_df2_comp: '
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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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#########
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# merge 2b: remove duplicate mutation information
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# merge 4b: 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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@ -271,29 +306,40 @@ all.equal(foo, merged_df3)
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summary(comparedf(foo, merged_df3))
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#=============== end of combining df
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#clear variables
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rm(mcsm_data
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, meta_with_afor
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, foo)
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#rm(diff_n, my_merged, mcsm)
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#=====================
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#*********************
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# write_output files
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#=====================
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# output dir
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outDir = "~/git/Data/pyrazinamide/output/"
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getwd()
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#outdir = '~/git/Data/pyrazinamide/output/'
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#uncomment as necessary
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#FIXME
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#out_filenames = c('merged_df2'
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# , 'merged_df3'
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# , 'meregd_df2_comp'
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# , 'merged_df3_comp'
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#)
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outFile1 = paste0(outDir, "merged_df3.csv"); outFile1
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#write.csv(merged_df3, outFile1)
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#cat('Writing output files: '
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# , '\nPath:', outdir)
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#outFile2 = paste0(outDir, "merged_df3_comp.csv"); outFile2
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#write.csv(merged_df3_comp, outFile2)
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#for (i in out_filenames){
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# print(i)
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# print(get(i))
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# outvar = get(i)
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# print(outvar)
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# outfile = paste0(outdir, '/', outvar, '.csv')
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# cat('Writing output file:'
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# ,'\nFilename: ', outfile
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# ,'\n')
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# write.csv(outvar, outfile)
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# cat('Finished writing file:'
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# ,'\nNo. of rows:', nrow(outvar)
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# , '\nNo. of cols:', ncol(outvar))
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#}
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rm(outDir
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||||
, outFile1
|
||||
# , outFile2
|
||||
)
|
||||
#sapply(out_filenames, function(x) write.csv(x, 'x.csv'))
|
||||
#*************************
|
||||
# clear variables
|
||||
rm(mcsm_data, meta_with_afor, foo, drug, gene, gene_match, indir, merged_muts_u, meta_muts_u, na_count, orig_col, outdir)
|
||||
rm(pos_count_check)
|
||||
#============================= end of script
|
||||
|
||||
|
|
|
|||
|
|
@ -1,17 +1,18 @@
|
|||
getwd()
|
||||
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/")
|
||||
getwd()
|
||||
|
||||
#########################################################
|
||||
# TASK: To combine mcsm and meta data with af and or
|
||||
# by filtering for distance to ligand (<10Ang)
|
||||
# by filtering for distance to ligand (<10Ang).
|
||||
# This script doesn't output anything, but can do if needed.
|
||||
# This script is sourced from other .R scripts for plotting
|
||||
#########################################################
|
||||
getwd()
|
||||
setwd('~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/')
|
||||
getwd()
|
||||
|
||||
#########################################################
|
||||
##########################################################
|
||||
# Installing and loading required packages
|
||||
#########################################################
|
||||
##########################################################
|
||||
|
||||
#source("Header_TT.R")
|
||||
source('Header_TT.R')
|
||||
#require(data.table)
|
||||
#require(arsenal)
|
||||
#require(compare)
|
||||
|
|
@ -22,14 +23,58 @@ getwd()
|
|||
# output of step 4 mcsm_pipeline
|
||||
#################################
|
||||
|
||||
inDir = "~/git/Data/pyrazinamide/input/processed/"
|
||||
inFile = paste0(inDir, "mcsm_complex1_normalised.csv"); inFile
|
||||
#%% variable assignment: input and output paths & filenames
|
||||
drug = 'pyrazinamide'
|
||||
gene = 'pncA'
|
||||
gene_match = paste0(gene,'_p.')
|
||||
cat(gene_match)
|
||||
|
||||
mcsm_data = read.csv(inFile
|
||||
#===========
|
||||
# input
|
||||
#===========
|
||||
# infile1: mCSM data
|
||||
#indir = '~/git/Data/pyrazinamide/input/processed/'
|
||||
indir = paste0('~/git/Data', '/', drug, '/', 'output') # revised {TODO: change in mcsm pipeline}
|
||||
in_filename = 'mcsm_complex1_normalised.csv'
|
||||
infile = paste0(indir, '/', in_filename)
|
||||
cat(paste0('Reading infile1: mCSM output file', ' ', infile) )
|
||||
|
||||
# infile2: gene associated meta data combined with AF and OR
|
||||
#indir: same as above
|
||||
in_filename_comb = paste0(tolower(gene), '_meta_data_with_AFandOR.csv')
|
||||
infile_comb = paste0(indir, '/', in_filename_comb)
|
||||
cat(paste0('Reading infile2: gene associated combined metadata:', infile_comb))
|
||||
|
||||
#===========
|
||||
# output
|
||||
#===========
|
||||
# Uncomment if and when required to output
|
||||
outdir = paste0('~/git/Data', '/', drug, '/', 'output') #same as indir
|
||||
cat('Output dir: ', outdir)
|
||||
#out_filename = paste0(tolower(gene), 'XXX')
|
||||
#outfile = paste0(outdir, '/', out_filename)
|
||||
#cat(paste0('Output file with full path:', outfile))
|
||||
#%% end of variable assignment for input and output files
|
||||
|
||||
#################################
|
||||
# Read file: normalised file
|
||||
# output of step 4 mcsm_pipeline
|
||||
#################################
|
||||
cat('Reading mcsm_data:'
|
||||
, '\nindir: ', indir
|
||||
, '\ninfile_comb: ', in_filename)
|
||||
|
||||
mcsm_data = read.csv(infile
|
||||
, row.names = 1
|
||||
, stringsAsFactors = F
|
||||
, header = T)
|
||||
rm(inDir, inFile)
|
||||
|
||||
cat('Read mcsm_data file:'
|
||||
, '\nNo.of rows: ', nrow(mcsm_data)
|
||||
, '\nNo. of cols:', ncol(mcsm_data))
|
||||
|
||||
# clear variables
|
||||
rm(in_filename, infile)
|
||||
|
||||
str(mcsm_data)
|
||||
|
||||
|
|
@ -39,7 +84,7 @@ table(mcsm_data$DUET_outcome); sum(table(mcsm_data$DUET_outcome) )
|
|||
mcsm_data$DUET_outcome[mcsm_data$DUET_outcome=='Stabilizing'] <- 'Stabilising'
|
||||
mcsm_data$DUET_outcome[mcsm_data$DUET_outcome=='Destabilizing'] <- 'Destabilising'
|
||||
|
||||
# checks
|
||||
# checks: should be the same as above
|
||||
table(mcsm_data$DUET_outcome); sum(table(mcsm_data$DUET_outcome) )
|
||||
head(mcsm_data$DUET_outcome); tail(mcsm_data$DUET_outcome)
|
||||
|
||||
|
|
@ -53,6 +98,10 @@ mcsm_data$Lig_outcome[mcsm_data$Lig_outcome=='Destabilizing'] <- 'Destabilising'
|
|||
table(mcsm_data$Lig_outcome); sum(table(mcsm_data$Lig_outcome) )
|
||||
head(mcsm_data$Lig_outcome); tail(mcsm_data$Lig_outcome)
|
||||
|
||||
# muts with opposing effects on protomer and ligand stability
|
||||
# excluded from here as it is redundant.
|
||||
# check 'combining_two_df.R' to refer if required.
|
||||
|
||||
########################### !!! only for mcsm_lig
|
||||
# 4: Filter/subset data
|
||||
# Lig plots < 10Ang
|
||||
|
|
@ -82,57 +131,60 @@ length(unique(mcsm_data2$Mutationinformation))
|
|||
# count Destabilisinga and stabilising
|
||||
table(mcsm_data2$Lig_outcome) #{RESULT: no of mutations within 10Ang}
|
||||
|
||||
#<<<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||
# REASSIGNMENT: so as not to alter the script
|
||||
mcsm_data = mcsm_data2
|
||||
#<<<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||
|
||||
#############################
|
||||
# Extra sanity check:
|
||||
# for mcsm_lig ONLY
|
||||
# Dis_lig_Ang should be <10
|
||||
#############################
|
||||
|
||||
if (max(mcsm_data$Dis_lig_Ang) < 10){
|
||||
if (max(mcsm_data2$Dis_lig_Ang) < 10){
|
||||
print ("Sanity check passed: lig data is <10Ang")
|
||||
}else{
|
||||
print ("Error: data should be filtered to be within 10Ang")
|
||||
}
|
||||
|
||||
#!!!!!!!!!!!!!!!!!!!!!
|
||||
# REASSIGNMENT: so as not to alter the script
|
||||
mcsm_data = mcsm_data2
|
||||
#!!!!!!!!!!!!!!!!!!!!!
|
||||
# clear variables
|
||||
rm(mcsm_data2)
|
||||
|
||||
# count na in each column
|
||||
na_count = sapply(mcsm_data, function(y) sum(length(which(is.na(y))))); na_count
|
||||
|
||||
head(mcsm_data$Mutationinformation)
|
||||
mcsm_data[mcsm_data$Mutationinformation=="Q10P",]
|
||||
mcsm_data[mcsm_data$Mutationinformation=="L4S",]
|
||||
|
||||
# sort by Mutationinformation
|
||||
mcsm_data = mcsm_data[order(mcsm_data$Mutationinformation),]
|
||||
head(mcsm_data$Mutationinformation)
|
||||
|
||||
# check
|
||||
mcsm_data[grep("Q10P", mcsm_data$Mutationinformation),]
|
||||
mcsm_data[grep("A102T", mcsm_data$Mutationinformation),]
|
||||
|
||||
orig_col = ncol(mcsm_data)
|
||||
# get freq count of positions and add to the df
|
||||
setDT(mcsm_data)[, occurrence := .N, by = .(Position)]
|
||||
|
||||
cat('Added 1 col: position frequency to see which posn has how many muts'
|
||||
, '\nNo. of cols now', ncol(mcsm_data)
|
||||
, '\nNo. of cols before: ', orig_col)
|
||||
|
||||
pos_count_check = data.frame(mcsm_data$Position, mcsm_data$occurrence)
|
||||
|
||||
###########################
|
||||
# 2: Read file: meta data with AFandOR
|
||||
###########################
|
||||
cat('Reading combined meta data and AFandOR file:'
|
||||
, '\nindir: ', indir
|
||||
, '\ninfile_comb: ', in_filename_comb)
|
||||
|
||||
inDir = "~/git/Data/pyrazinamide/input/processed/"
|
||||
inFile2 = paste0(inDir, "meta_data_with_AFandOR.csv"); inFile2
|
||||
|
||||
meta_with_afor <- read.csv(inFile2
|
||||
meta_with_afor <- read.csv(infile_comb
|
||||
, stringsAsFactors = F
|
||||
, header = T)
|
||||
|
||||
cat('Read mcsm_data file:'
|
||||
, '\nNo.of rows: ', nrow(meta_with_afor)
|
||||
, '\nNo. of cols:', ncol(meta_with_afor))
|
||||
|
||||
# clear variables
|
||||
rm(in_filename_comb, infile_comb)
|
||||
|
||||
str(meta_with_afor)
|
||||
|
||||
# sort by Mutationinformation
|
||||
|
|
@ -140,114 +192,45 @@ head(meta_with_afor$Mutationinformation)
|
|||
meta_with_afor = meta_with_afor[order(meta_with_afor$Mutationinformation),]
|
||||
head(meta_with_afor$Mutationinformation)
|
||||
|
||||
# sanity check: should be True for all the mentioned columns
|
||||
#is.numeric(meta_with_afor$OR)
|
||||
na_var = c("AF", "OR", "pvalue", "logor", "neglog10pvalue")
|
||||
|
||||
c1 = NULL
|
||||
for (i in na_var){
|
||||
print(i)
|
||||
c0 = is.numeric(meta_with_afor[,i])
|
||||
c1 = c(c0, c1)
|
||||
if ( all(c1) ){
|
||||
print("Sanity check passed: These are all numeric cols")
|
||||
} else{
|
||||
print("Error: Please check your respective data types")
|
||||
}
|
||||
}
|
||||
|
||||
# If OR, and P value are not numeric, then convert to numeric and then count
|
||||
# else they will say 0
|
||||
|
||||
# NOW count na in each column: if you did it before, then
|
||||
# OR and Pvalue column would say 0 na since these were not numeric
|
||||
na_count = sapply(meta_with_afor, function(y) sum(length(which(is.na(y))))); na_count
|
||||
str(na_count)
|
||||
|
||||
# compare if the No of "NA" are the same for all these cols
|
||||
na_len = NULL
|
||||
na_var = c("AF", "OR", "pvalue", "logor", "neglog10pvalue")
|
||||
for (i in na_var){
|
||||
temp = na_count[[i]]
|
||||
na_len = c(na_len, temp)
|
||||
}
|
||||
|
||||
my_nrows = NULL
|
||||
|
||||
for ( i in 1: (length(na_len)-1) ){
|
||||
#print(compare(na_len[i]), na_len[i+1])
|
||||
c = compare(na_len[i], na_len[i+1])
|
||||
if ( c$result ) {
|
||||
my_nrows = na_len[i] }
|
||||
else {
|
||||
print("Error: Please check your numbers")
|
||||
}
|
||||
}
|
||||
|
||||
my_nrows
|
||||
|
||||
#=#=#=#=#=#=#=#=#
|
||||
# COMMENT: AF, OR, pvalue, logor and neglog10pvalue
|
||||
# all have 81 NA, with pyrazinamide with 960
|
||||
# and these are the same 7 ones
|
||||
#=#=#=#=#=#=#=#=#
|
||||
|
||||
# sanity check
|
||||
#which(is.na(meta_with_afor$OR))
|
||||
|
||||
# initialise an empty df with nrows as extracted above
|
||||
na_count_df = data.frame(matrix(vector(mode = 'numeric'
|
||||
# , length = length(na_var)
|
||||
)
|
||||
, nrow = my_nrows
|
||||
# , ncol = length(na_var)
|
||||
))
|
||||
|
||||
# populate the df with the indices of the cols that are NA
|
||||
for (i in na_var){
|
||||
print(i)
|
||||
na_i = which(is.na(meta_with_afor[i]))
|
||||
na_count_df = cbind(na_count_df, na_i)
|
||||
colnames(na_count_df)[which(na_var == i)] <- i
|
||||
}
|
||||
|
||||
# Now compare these indices to ensure these are the same
|
||||
c2 = NULL
|
||||
for ( i in 1: ( length(na_count_df)-1 ) ) {
|
||||
# print(na_count_df[i] == na_count_df[i+1])
|
||||
c1 = identical(na_count_df[[i]], na_count_df[[i+1]])
|
||||
c2 = c(c1, c2)
|
||||
if ( all(c2) ) {
|
||||
print("Sanity check passed: The indices for AF, OR, etc are all the same")
|
||||
} else {
|
||||
print ("Error: Please check indices which are NA")
|
||||
}
|
||||
}
|
||||
|
||||
rm( c, c1, c2, i, my_nrows
|
||||
, na_count, na_i, na_len
|
||||
, na_var, temp
|
||||
, na_count_df
|
||||
, pos_count_check )
|
||||
|
||||
###########################
|
||||
# 3:merging two dfs: with NA
|
||||
# 3: merging two dfs: with NA
|
||||
###########################
|
||||
# link col name = 'Mutationinforamtion'
|
||||
cat('Merging dfs with NAs: big df (1-many relationship b/w id & mut)'
|
||||
,'\nlinking col: Mutationinforamtion'
|
||||
,'\nfilename: merged_df2')
|
||||
|
||||
# link col name = Mutationinforamtion
|
||||
head(mcsm_data$Mutationinformation)
|
||||
head(meta_with_afor$Mutationinformation)
|
||||
|
||||
#########
|
||||
# merge 1a: meta data with mcsm
|
||||
# merge 3a: meta data with mcsm
|
||||
#########
|
||||
merged_df2 = merge(x = meta_with_afor
|
||||
, y = mcsm_data
|
||||
, by = "Mutationinformation"
|
||||
,y = mcsm_data
|
||||
, by = 'Mutationinformation'
|
||||
, all.y = T)
|
||||
|
||||
cat('Dim of merged_df2: '
|
||||
, '\nNo. of rows: ', nrow(merged_df2)
|
||||
, '\nNo. of cols: ', ncol(merged_df2))
|
||||
head(merged_df2$Position)
|
||||
|
||||
if(nrow(meta_with_afor) == nrow(merged_df2)){
|
||||
cat('nrow(merged_df2) = nrow (gene associated metadata)'
|
||||
,'\nExpected no. of rows: ',nrow(meta_with_afor)
|
||||
,'\nGot no. of rows: ', nrow(merged_df2))
|
||||
} else{
|
||||
cat('nrow(merged_df2)!= nrow(gene associated metadata)'
|
||||
, '\nExpected no. of rows after merge: ', nrow(meta_with_afor)
|
||||
, '\nGot no. of rows: ', nrow(merged_df2)
|
||||
, '\nFinding discrepancy')
|
||||
merged_muts_u = unique(merged_df2$Mutationinformation)
|
||||
meta_muts_u = unique(meta_with_afor$Mutationinformation)
|
||||
# find the index where it differs
|
||||
unique(meta_muts_u[! meta_muts_u %in% merged_muts_u])
|
||||
}
|
||||
|
||||
# sort by Position
|
||||
head(merged_df2$Position)
|
||||
merged_df2 = merged_df2[order(merged_df2$Position),]
|
||||
|
|
@ -255,13 +238,12 @@ head(merged_df2$Position)
|
|||
|
||||
merged_df2v2 = merge(x = meta_with_afor
|
||||
,y = mcsm_data
|
||||
, by = "Mutationinformation"
|
||||
, by = 'Mutationinformation'
|
||||
, all.x = T)
|
||||
|
||||
#!=!=!=!=!=!=!=!
|
||||
# COMMENT: used all.y since position 186 is not part of the struc,
|
||||
# hence doesn't have a mcsm value
|
||||
# but 186 is associated with with mutation
|
||||
# but 186 is associated with mutation
|
||||
#!=!=!=!=!=!=!=!
|
||||
|
||||
# should be False
|
||||
|
|
@ -271,8 +253,12 @@ table(merged_df2$Position%in%merged_df2v2$Position)
|
|||
rm(merged_df2v2)
|
||||
|
||||
#########
|
||||
# merge 1b:remove duplicate mutation information
|
||||
# merge 3b:remove duplicate mutation information
|
||||
#########
|
||||
cat('Merging dfs with NAs: small df (removing duplicate muts)'
|
||||
,'\nCannot trust lineage info from this'
|
||||
,'\nlinking col: Mutationinforamtion'
|
||||
,'\nfilename: merged_df3')
|
||||
|
||||
#==#=#=#=#=#=#
|
||||
# Cannot trust lineage, country from this df as the same mutation
|
||||
|
|
@ -280,69 +266,60 @@ rm(merged_df2v2)
|
|||
# but this should be good for the numerical corr plots
|
||||
#=#=#=#=#=#=#=
|
||||
merged_df3 = merged_df2[!duplicated(merged_df2$Mutationinformation),]
|
||||
head(merged_df3$Position) ; tail(merged_df3$Position) # should be sorted
|
||||
head(merged_df3$Position); tail(merged_df3$Position) # should be sorted
|
||||
|
||||
# sanity checks
|
||||
# nrows of merged_df3 should be the same as the nrows of mcsm_data
|
||||
if(nrow(mcsm_data) == nrow(merged_df3)){
|
||||
print("sanity check: Passed")
|
||||
cat('PASS: No. of rows match with mcsm_data'
|
||||
,'\nExpected no. of rows: ', nrow(mcsm_data)
|
||||
,'\nGot no. of rows: ', nrow(merged_df3))
|
||||
} else {
|
||||
print("Error!: check data, nrows is not as expected")
|
||||
cat('FAIL: No. of rows mismatch'
|
||||
, '\nNo. of rows mcsm_data: ', nrow(mcsm_data)
|
||||
, '\nNo. of rows merged_df3: ', nrow(merged_df3))
|
||||
}
|
||||
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
# uncomment as necessary
|
||||
# only need to run this if merged_df2v2 i.e non structural pos included
|
||||
#mcsm = mcsm_data$Mutationinformation
|
||||
#my_merged = merged_df3$Mutationinformation
|
||||
|
||||
# find the index where it differs
|
||||
#diff_n = which(!my_merged%in%mcsm)
|
||||
|
||||
#check if it is indeed pos 186
|
||||
#merged_df3[diff_n,]
|
||||
|
||||
# remove this entry
|
||||
#merged_df3 = merged_df3[-diff_n,]
|
||||
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||
|
||||
###########################
|
||||
# 3b :merging two dfs: without NA
|
||||
# 4: merging two dfs: without NA
|
||||
###########################
|
||||
cat('Merging dfs without any NAs: big df (1-many relationship b/w id & mut)'
|
||||
,'\nlinking col: Mutationinforamtion'
|
||||
,'\nfilename: merged_df2_comp')
|
||||
|
||||
#########
|
||||
# merge 2a:same as merge 1 but excluding NA
|
||||
# merge 4a: same as merge 1 but excluding NA
|
||||
#########
|
||||
merged_df2_comp = merged_df2[!is.na(merged_df2$AF),]
|
||||
#merged_df2_comp = merged_df2[!duplicated(merged_df2$Mutationinformation),]
|
||||
|
||||
cat('Dim of merged_df2_comp: '
|
||||
, '\nNo. of rows: ', nrow(merged_df2_comp)
|
||||
, '\nNo. of cols: ', ncol(merged_df2_comp))
|
||||
|
||||
#########
|
||||
# merge 2b: remove duplicate mutation information
|
||||
# merge 4b: remove duplicate mutation information
|
||||
#########
|
||||
merged_df3_comp = merged_df2_comp[!duplicated(merged_df2_comp$Mutationinformation),]
|
||||
|
||||
# FIXME: add this as a sanity check. I have manually checked!
|
||||
cat('Dim of merged_df3_comp: '
|
||||
, '\nNo. of rows: ', nrow(merged_df3_comp)
|
||||
, '\nNo. of cols: ', ncol(merged_df3_comp))
|
||||
|
||||
# alternate way of deriving merged_df3_comp
|
||||
foo = merged_df3[!is.na(merged_df3$AF),]
|
||||
|
||||
# compare dfs: foo and merged_df3_com
|
||||
all.equal(foo, merged_df3)
|
||||
|
||||
summary(comparedf(foo, merged_df3))
|
||||
|
||||
#=============== end of combining df
|
||||
#clear variables
|
||||
rm(mcsm_data
|
||||
, meta_with_afor
|
||||
, foo)
|
||||
|
||||
#rm(diff_n, my_merged, mcsm)
|
||||
|
||||
#===============end of script
|
||||
|
||||
#=====================
|
||||
#*********************
|
||||
# write_output files
|
||||
#=====================
|
||||
|
||||
# Not required as this is a subset of the "combining_two_df.R" script
|
||||
# Not required as this is a subset of the combining_two_df.R
|
||||
#*************************
|
||||
# clear variables
|
||||
rm(mcsm_data, meta_with_afor, foo, drug, gene, gene_match, indir, merged_muts_u, meta_muts_u, na_count, orig_col, outdir)
|
||||
rm(pos_count_check)
|
||||
#============================= end of script
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
#============================================
|
||||
#########################################################
|
||||
# TASK: To calculate Allele Frequency and
|
||||
# Odds Ratio from master data
|
||||
# and add the calculated params to meta_data extracted from
|
||||
# pnca_data_extraction.py
|
||||
#===========================================
|
||||
# data_extraction.py
|
||||
#########################################################
|
||||
getwd()
|
||||
setwd('~/git/LSHTM_analysis/meta_data_analysis')
|
||||
getwd()
|
||||
|
|
@ -14,9 +14,9 @@ gene = 'pncA'
|
|||
gene_match = paste0(gene,'_p.')
|
||||
cat(gene_match)
|
||||
|
||||
#=======
|
||||
# input dir
|
||||
#=======
|
||||
#===========
|
||||
# input
|
||||
#===========
|
||||
# infile1: Raw data
|
||||
#indir = 'git/Data/pyrazinamide/input/original'
|
||||
indir = paste0('~/git/Data')
|
||||
|
|
@ -24,27 +24,26 @@ in_filename = 'original_tanushree_data_v2.csv'
|
|||
infile = paste0(indir, '/', in_filename)
|
||||
cat(paste0('Reading infile1: raw data', ' ', infile) )
|
||||
|
||||
# infile2: gene associated meta data file to extract valid snps and add calcs to
|
||||
# filename: outfile3 from data_extraction.py
|
||||
# infile2: gene associated meta data file to extract valid snps and add calcs to.
|
||||
# This is outfile3 from data_extraction.py
|
||||
indir_metadata = paste0('~/git/Data', '/', drug, '/', 'output')
|
||||
in_filename_metadata = 'pnca_metadata.csv'
|
||||
infile_metadata = paste0(indir_metadata, '/', in_filename_metadata)
|
||||
cat(paste0('Reading infile2: gene associated metadata:', infile_metadata))
|
||||
|
||||
#=========
|
||||
# output dir
|
||||
#=========
|
||||
#===========
|
||||
# output
|
||||
#===========
|
||||
# outdir = 'git/Data/pyrazinamide/output'
|
||||
outdir = paste0('~/git/Data', '/', drug, '/', 'output')
|
||||
out_filename = paste0(tolower(gene),'_', 'meta_data_with_AFandOR.csv')
|
||||
outfile = paste0(outdir, '/', out_filename)
|
||||
cat(paste0('Output file with full path:', outfile))
|
||||
|
||||
#%% end of variable assignment for input and output files
|
||||
|
||||
#===============
|
||||
# Step 1: Read master/raw data stored in Data/
|
||||
#===============
|
||||
#########################################################
|
||||
# 1: Read master/raw data stored in Data/
|
||||
#########################################################
|
||||
raw_data_all = read.csv(infile, stringsAsFactors = F)
|
||||
|
||||
raw_data = raw_data_all[,c("id"
|
||||
|
|
@ -55,9 +54,9 @@ rm(raw_data_all)
|
|||
|
||||
rm(indir, in_filename, infile)
|
||||
|
||||
#####
|
||||
#===========
|
||||
# 1a: exclude na
|
||||
#####
|
||||
#===========
|
||||
raw_data = raw_data[!is.na(raw_data$pyrazinamide),]
|
||||
|
||||
total_samples = length(unique(raw_data$id))
|
||||
|
|
@ -66,16 +65,16 @@ cat(paste0('Total samples without NA in', ' ', drug, 'is:', total_samples))
|
|||
# sanity check: should be true
|
||||
is.numeric(total_samples)
|
||||
|
||||
#####
|
||||
#===========
|
||||
# 1b: combine the two mutation columns
|
||||
#####
|
||||
#===========
|
||||
raw_data$all_mutations_pyrazinamide = paste(raw_data$dr_mutations_pyrazinamide
|
||||
, raw_data$other_mutations_pyrazinamide)
|
||||
head(raw_data$all_mutations_pyrazinamide)
|
||||
|
||||
#####
|
||||
#===========
|
||||
# 1c: create yet another column that contains all the mutations but in lower case
|
||||
#####
|
||||
#===========
|
||||
raw_data$all_muts_pnca = tolower(raw_data$all_mutations_pyrazinamide)
|
||||
|
||||
# sanity checks
|
||||
|
|
@ -104,9 +103,10 @@ table(mut, dst)
|
|||
#fisher.test(table(mut, dst))
|
||||
#table(mut)
|
||||
|
||||
#===============
|
||||
# Step 2: Read valid snps for which OR can be calculated (infile_comp_snps.csv)
|
||||
#===============
|
||||
#########################################################
|
||||
# 2: Read valid snps for which OR
|
||||
# can be calculated (infile_comp_snps.csv)
|
||||
#########################################################
|
||||
cat(paste0('Reading metadata infile:', infile_metadata))
|
||||
|
||||
pnca_metadata = read.csv(infile_metadata
|
||||
|
|
@ -188,7 +188,7 @@ hist(log(ors)
|
|||
, breaks = 100
|
||||
)
|
||||
|
||||
# FIXME: could be good to add a sanity check
|
||||
# sanity check
|
||||
if (table(names(ors) == names(pvals)) & table(names(ors) == names(afs)) & table(names(pvals) == names(afs)) == length(pnca_snps_unique)){
|
||||
cat('PASS: names of ors, pvals and afs match: proceed with combining into a single df')
|
||||
} else{
|
||||
|
|
@ -214,9 +214,9 @@ if (table(rownames(comb_AF_and_OR) == comb_AF_and_OR$mutation)){
|
|||
cat('FAIL: rownames and mutation col values mismatch')
|
||||
}
|
||||
|
||||
############
|
||||
# Merge 1: combine meta data file with calculated num params
|
||||
###########
|
||||
#########################################################
|
||||
# 3: Merge meta data file + calculated num params
|
||||
#########################################################
|
||||
df1 = pnca_metadata
|
||||
df2 = comb_AF_and_OR
|
||||
|
||||
|
|
@ -254,7 +254,7 @@ if ( identical(na_count[[length(na_count)]], na_count[[length(na_count)-1]], na_
|
|||
cat('PASS: No. of NAs for OR, AF and Pvals are equal as expected',
|
||||
'\nNo. of NA: ', na_count[[length(na_count)]])
|
||||
} else {
|
||||
cat('FAIl: No. of NAs for OR, AF and Pvals mismatch')
|
||||
cat('FAIL: No. of NAs for OR, AF and Pvals mismatch')
|
||||
}
|
||||
|
||||
# reassign custom colnames
|
||||
|
|
@ -289,6 +289,7 @@ cat(paste0('Added', ' ', ncol_added, ' more cols to merged_df:'
|
|||
, '\nno. of cols in merged_df now: ', ncol(merged_df)))
|
||||
|
||||
#%% write file out: pnca_meta_data_with_AFandOR
|
||||
#*********************************************
|
||||
cat(paste0('writing output file: '
|
||||
, '\nFilename: ', out_filename
|
||||
, '\nPath:', outdir))
|
||||
|
|
@ -300,6 +301,98 @@ cat(paste0('Finished writing:'
|
|||
, out_filename
|
||||
, '\nNo. of rows: ', nrow(merged_df)
|
||||
, '\nNo. of cols: ', ncol(merged_df)))
|
||||
|
||||
#************************************************
|
||||
cat('======================================================================')
|
||||
rm(out_filename)
|
||||
cat('End of script: calculated AF, OR, pvalues and saved file')
|
||||
# End of script
|
||||
#%%
|
||||
# sanity check: Count NA in these four cols.
|
||||
# However these need to be numeric else these
|
||||
# will be misleading when counting NAs (i.e retrun 0)
|
||||
#is.numeric(meta_with_afor$OR)
|
||||
na_var = c('AF', 'OR', 'pvalue', 'logor', 'neglog10pvalue')
|
||||
|
||||
# loop through these vars and check if these are numeric.
|
||||
# if not, then convert to numeric
|
||||
check_all = NULL
|
||||
|
||||
for (i in na_var){
|
||||
# cat(i)
|
||||
check0 = is.numeric(meta_with_afor[,i])
|
||||
if (check0) {
|
||||
check_all = c(check0, check_all)
|
||||
cat('These are all numeric cols')
|
||||
} else{
|
||||
cat('First converting to numeric')
|
||||
check0 = as.numeric(meta_with_afor[,i])
|
||||
check_all = c(check0, check_all)
|
||||
}
|
||||
}
|
||||
|
||||
# count na now that the respective cols are numeric
|
||||
na_count = sapply(meta_with_afor, function(y) sum(length(which(is.na(y))))); na_count
|
||||
str(na_count)
|
||||
|
||||
# extract how many NAs:
|
||||
# should be all TRUE
|
||||
# should be a single number since
|
||||
# all the cols should have 'equal' and 'same' no. of NAs
|
||||
# compare if the No of 'NA' are the same for all these cols
|
||||
na_len = NULL
|
||||
for (i in na_var){
|
||||
temp = na_count[[i]]
|
||||
na_len = c(na_len, temp)
|
||||
}
|
||||
|
||||
cat('Checking how many NAs and if these are identical for the selected cols:')
|
||||
my_nrows = NULL
|
||||
for ( i in 1: (length(na_len)-1) ){
|
||||
# cat(compare(na_len[i]), na_len[i+1])
|
||||
c = compare(na_len[i], na_len[i+1])
|
||||
if ( c$result ) {
|
||||
cat('PASS: No. of NAa in selected cols are identical')
|
||||
my_nrows = na_len[i] }
|
||||
else {
|
||||
cat('FAIL: No. of NAa in selected cols mismatch')
|
||||
}
|
||||
}
|
||||
|
||||
cat('No. of NAs in each of the selected cols: ', my_nrows)
|
||||
|
||||
# yet more sanity checks:
|
||||
cat('Check whether the ', my_nrows, 'indices are indeed the same')
|
||||
|
||||
#which(is.na(meta_with_afor$OR))
|
||||
|
||||
# initialise an empty df with nrows as extracted above
|
||||
na_count_df = data.frame(matrix(vector(mode = 'numeric'
|
||||
# , length = length(na_var)
|
||||
)
|
||||
, nrow = my_nrows
|
||||
# , ncol = length(na_var)
|
||||
))
|
||||
|
||||
# populate the df with the indices of the cols that are NA
|
||||
for (i in na_var){
|
||||
cat(i)
|
||||
na_i = which(is.na(meta_with_afor[i]))
|
||||
na_count_df = cbind(na_count_df, na_i)
|
||||
colnames(na_count_df)[which(na_var == i)] <- i
|
||||
}
|
||||
|
||||
# Now compare these indices to ensure these are the same
|
||||
check2 = NULL
|
||||
for ( i in 1: ( length(na_count_df)-1 ) ) {
|
||||
# cat(na_count_df[i] == na_count_df[i+1])
|
||||
check_all = identical(na_count_df[[i]], na_count_df[[i+1]])
|
||||
check2 = c(check_all, check2)
|
||||
if ( all(check2) ) {
|
||||
cat('PASS: The indices for AF, OR, etc are all the same\n')
|
||||
} else {
|
||||
cat ('FAIL: Please check indices which are NA')
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue