added commonly used mutation format for missense muts in the gene_specific nssnp_info file
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4 changed files with 194 additions and 568 deletions
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#########################################################
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# TASK: To compare OR from master snps
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# chisq, fisher test and logistic and adjusted logistic
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#########################################################
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getwd()
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setwd('~/git/LSHTM_analysis/scripts')
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getwd()
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#install.packages("logistf")
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library(logistf)
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#########################################################
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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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# input and output dirs
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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(datadir, '/', drug, '/', 'output')
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#===========
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# input and output files
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#===========
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in_filename = 'original_tanushree_data_v2.csv'
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#in_filename = 'mtb_gwas_v3.csv'
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infile = paste0(datadir, '/', in_filename)
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cat(paste0('Reading infile1: raw snps', ' ', infile) )
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# infile2: _gene associated meta snps file to extract valid snps and add calcs to.
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# This is outfile3 from snps_extraction.py
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in_filename_metadata = paste0(tolower(gene), '_metadata.csv')
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infile_metadata = paste0(outdir, '/', in_filename_metadata)
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cat(paste0('Reading infile2: gene associated metadata:', infile_metadata))
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#===========
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# output
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#===========
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out_filename = paste0(tolower(gene),'_', 'af_or.csv')
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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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# 1: Read master/raw snps stored in snps/
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#####################################################
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#===============
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# Step 1: read raw snps (all remove entries with NA in pza column)
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#===============
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raw_data_all = read.csv(infile, stringsAsFactors = F)
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# building cols to extract
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dr_muts_col = paste0('dr_mutations_', drug)
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other_muts_col = paste0('other_mutations_', drug)
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cat('Extracting columns based on variables:\n'
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, drug
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, '\n'
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, dr_muts_col
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, '\n'
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, other_muts_col
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, '\n===============================================================')
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raw_data = raw_data_all[,c("id"
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, drug
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, dr_muts_col
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, other_muts_col)]
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rm(raw_data_all)
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rm(indir, in_filename, infile)
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#===========
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# 1a: exclude na
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#===========
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raw_data = raw_data[!is.na(raw_data[[drug]]),]
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total_samples = length(unique(raw_data$id))
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cat(paste0('Total samples without NA in', ' ', drug, 'is:', total_samples))
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# sanity check: should be true
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is.numeric(total_samples)
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#===========
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# 1b: combine the two mutation columns
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#===========
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#raw_data$all_mutations_pyrazinamide = paste(raw_data$dr_mutations_pyrazinamide, raw_data$other_mutations_pyrazinamide)
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all_muts_colname = paste0('all_mutations_', drug)
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raw_data[[all_muts_colname]] = paste(raw_data[[dr_muts_col]], raw_data[[other_muts_col]])
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head(raw_data[[all_muts_colname]])
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#===========
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# 1c: create yet another column that contains all the mutations but in lower case
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#===========
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head(raw_data[[all_muts_colname]])
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raw_data$all_muts_gene = tolower(raw_data[[all_muts_colname]])
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head(raw_data$all_muts_gene)
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# sanity checks
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#table(grepl("gene_p",raw_data$all_muts_gene))
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cat(paste0('converting gene match:', gene_match, ' ', 'to lowercase'))
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gene_match = tolower(gene_match)
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table(grepl(gene_match,raw_data$all_muts_gene))
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# sanity check
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if(sum(table(grepl(gene_match, raw_data$all_muts_gene))) == total_samples){
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cat('PASS: Total no. of samples match')
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} else{
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cat('FAIL: No. of samples mismatch')
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}
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#########################################################
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# 2: Read valid snps for which OR
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# can be calculated
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#########################################################
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cat(paste0('Reading metadata infile:', infile_metadata))
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gene_metadata = read.csv(infile_metadata
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#, file.choose()
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, stringsAsFactors = F
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, header = T)
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# clear variables
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rm(in_filename_metadata, infile_metadata)
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# count na in pyrazinamide column
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tot_pza_na = sum(is.na(gene_metadata$pyrazinamide))
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expected_rows = nrow(gene_metadata) - tot_pza_na
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# drop na from the pyrazinamide colum
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gene_snps_or = gene_metadata[!is.na(gene_metadata[[drug]]),]
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# sanity check
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if(nrow(gene_snps_or) == expected_rows){
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cat('PASS: no. of rows match with expected_rows')
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} else{
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cat('FAIL: nrows mismatch.')
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}
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# extract unique snps to iterate over for AF and OR calcs
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gene_snps_unique = unique(gene_snps_or$mutation)
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cat(paste0('Total no. of distinct comp snps to perform OR calcs: ', length(gene_snps_unique)))
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#=====================================
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#OR calcs using the following 4
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#1) chisq.test
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#2) fisher
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#3) modified chisq.test
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#4) logistic
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#5) adjusted logistic?
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#======================================
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#########################
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# custom chisq function:
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# To calculate OR
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#########################
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#i = "pnca_p.trp68gly"
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#mut = grepl(i,raw_data$all_muts_gene)
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#dst = raw_data[[drug]]
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#x = as.numeric(mut)
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#y = dst
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mychisq_or = function(x,y){
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tab = as.matrix(table(x,y))
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a = tab[2,2]
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if (a==0){ a<-0.5}
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b = tab[2,1]
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if (b==0){ b<-0.5}
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c = tab[1,2]
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if (c==0){ c<-0.5}
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d = tab[1,1]
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if (d==0){ d<-0.5}
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(a/b)/(c/d)
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}
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#======================================
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#==============
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# TEST WITH ONE
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#==============
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i = "pnca_p.trp68gly"
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i = "pnca_p.gln10pro"
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i = "pnca_p.leu159arg"
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# IV
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table(grepl(i,raw_data$all_muts_gene))
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mut = grepl(i,raw_data$all_muts_gene)
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# DV
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#dst = raw_data$pyrazinamide
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dst = raw_data[[drug]] # or raw_data[,drug]
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# 2X2 table
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table(mut, dst)
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# CV
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#c = raw_data$id[mut]
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c = raw_data$id[grepl(i,raw_data$all_muts_gene)]
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#sid = grepl(raw_data$id[mut], raw_data$id) # warning
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#argument 'pattern' has length > 1 and only the first element will be used
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#grepl(raw_data$id=="ERR2512440", raw_data$id)
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sid = grepl(paste(c,collapse="|"), raw_data$id)
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table(sid)
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# 3X2 table
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table(mut, dst, sid)
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#===================================================
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# compare ORs from different calcs
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#1) chisq
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chisq.test(table(mut,dst))
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chisq_estimate = chisq.test(table(mut,dst))$statistic
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est_chisq = chisq_estimate[[1]]; print(paste0('chi sq estimate:', est_chisq))# numeric part only
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pval_chisq = chisq.test(table(mut,dst))$p.value; print(paste0('pvalue:', pval_chisq))
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#2) fisher
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fisher.test(table(mut, dst))
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fisher.test(table(mut, dst))$p.value
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est_fisher = fisher.test(table(mut, dst))$estimate
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or_fisher = est_fisher[[1]]; print(paste0('OR fisher:', or_fisher))# numeric part only
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pval_fisher = fisher.test(table(mut, dst))$p.value; print(paste0('pval fisher:', pval_fisher))
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#3) custom chisq
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or_mychisq = mychisq_or(mut,dst)
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#4) logistic
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summary(model<-glm(dst ~ mut, family = binomial))
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or_logistic = exp(summary(model)$coefficients[2,1]); print(paste0('OR logistic:', or_logistic))
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pval_logistic = summary(model)$coefficients[2,4]; print(paste0('pval logistic:', pval_logistic))
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# extract SE of the logistic model for a given snp
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logistic_se = summary(model)$coefficients[2,2]; print(paste0('SE:', logistic_se))
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# extract Z of the logistic model for a given snp
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logistic_zval = summary(model)$coefficients[2,3]; print(paste0('Z-value:', logistic_zval))
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# extract confint interval of snp (2 steps, since the output is a named number)
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ci_mod = exp(confint(model))[2,]; print(paste0('CI:', ci_mod))
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#logistic_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
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logistic_ci_lower = ci_mod[["2.5 %"]]; print(paste0('CI_lower:', logistic_ci_lower))
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logistic_ci_upper = ci_mod[["97.5 %"]]; print(paste0('CI_upper:', logistic_ci_upper))
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# adjusted logistic or: doesn't seem to make a difference
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summary(model2<-glm(dst ~ mut + sid, family = binomial))
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or_logistic2 = exp(summary(model2)$coefficients[2,1]); print(paste0('Adjusted OR logistic:', or_logistic2))
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pval_logistic2 = summary(model2)$coefficients[2,4]; print(paste0('Adjusted pval logistic:',pval_logistic2))
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# print all output
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writeLines(c(paste0("mutation:", i)
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, paste0("=========================")
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, paste0("OR logistic:", or_logistic,"--->", "pval logistic:", pval_logistic )
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, paste0("OR adjusted logistic:", or_logistic2,"--->", "pval adjusted logistic:", pval_logistic2)
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, paste0("OR fisher:", or_fisher, "--->","pval fisher:", pval_fisher )
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, paste0("OR custom chisq:", or_mychisq, "--->","pval fisher:", pval_fisher )
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, paste0("Chisq estimate:", est_chisq, "--->","pval chisq:", pval_chisq)))
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#%%========================================================
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# looping with sapply: a subset of mutations
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#%%========================================================
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snps = c("pnca_p.trp68gly", "pnca_p.leu4ser", "pnca_p.leu159arg","pnca_p.his57arg" )
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#snps = snps_test[1:2]
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snps
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# custom chisq
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ors = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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mychisq_or(mut,dst)
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})
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head(ors)
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# pvalue fisher, to be used with custom chisq
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pvals = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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fisher.test(mut,dst)$p.value
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})
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head(pvals)
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# allele frequency
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afs = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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mean(mut)
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})
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head(afs)
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# logistic reg parameters: individual sapply
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#--------------
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## logistci or
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#--------------
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ors_logistic = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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model<-glm(dst ~ mut, family = binomial)
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or_logistic = exp(summary(model)$coefficients[2,1])
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})
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ors_logistic
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head(ors_logistic); head(names(ors_logistic))
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#-------------------
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## logistic p-value
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#--------------
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pvals_logistic = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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model<-glm(dst ~ mut , family = binomial)
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pval_logistic = summary(model)$coefficients[2,4]
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})
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head(pvals_logistic); head(names(pvals_logistic))
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#--------------
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## logistic se
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#--------------
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se_logistic = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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model<-glm(dst ~ mut , family = binomial)
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logistic_se = summary(model)$coefficients[2,2]
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})
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head(se_logistic); head(names(se_logistic))
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#--------------
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## logistic z-value
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#--------------
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zval_logistic = sapply(gene_snps_unique,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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model<-glm(dst ~ mut , family = binomial)
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logistic_zval = summary(model)$coefficients[2,3]
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})
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head(zval_logistic); head(names(zval_logistic))
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#--------------
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## logistic ci - lower bound
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#--------------
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ci_lb_logistic = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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model<-glm(dst ~ mut , family = binomial)
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ci_mod = exp(confint(model))[2,]
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logistic_ci_lower = ci_mod[["2.5 %"]]
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})
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head(ci_lb_logistic); head(names(ci_lb_logistic))
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#--------------
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## logistic ci - upper bound
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#--------------
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ci_ub_logistic = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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model<-glm(dst ~ mut , family = binomial)
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ci_mod = exp(confint(model))[2,]
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logistic_ci_upper = ci_mod[["97.5 %"]]
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})
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head(ci_ub_logistic); head(names(ci_ub_logistic))
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#--------------
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# adjusted logistic with sample id: Doesn't seem to make a difference
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#--------------
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logistic_ors2 = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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c = raw_data$id[mut]
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sid = grepl(paste(c,collapse="|"), raw_data$id)
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model2<-glm(dst ~ mut + sid, family = binomial)
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or_logistic2 = exp(summary(model2)$coefficients[2,1])
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#pval_logistic2 = summary(model2)$coefficients[2,4]
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})
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head(logistic_ors)
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#=!=!=!=!=!=!=!=!=!=!=!=!=!=!
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# COMMENT: individual sapply seem wasteful
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#=!=!=!=!=!=!=!=!=!=!=!=!=!=!
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#%%========================================================
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# sapply with multiple values being returned as df
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#Link: https://gist.github.com/primaryobjects/33adabc337edd67b4a8d
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#%%========================================================
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snps = c("pnca_p.trp68gly", "pnca_p.leu4ser", "pnca_p.leu159arg","pnca_p.his57arg" )
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#snps = snps_test[1:4]
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snps
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# yayy works!
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# DV: pyrazinamide 0 or 1
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dst = raw_data[[drug]]
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# initialise an empty df
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or_df = data.frame()
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x = sapply(snps,function(m){
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#df = data.frame()
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mut = grepl(m,raw_data$all_muts_gene)
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model<-glm(dst ~ mut, family = binomial)
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# allele frequency
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afs = mean(mut)
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# logistic model
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beta_logistic = summary(model)$coefficients[2,1]
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or_logistic = exp(summary(model)$coefficients[2,1])
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print(paste0('logistic OR:', or_logistic))
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pval_logistic = summary(model)$coefficients[2,4]
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print(paste0('logistic pval:', pval_logistic))
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se_logistic = summary(model)$coefficients[2,2]
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zval_logistic = summary(model)$coefficients[2,3]
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ci_mod = exp(confint(model))[2,]
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ci_lower_logistic = ci_mod[["2.5 %"]]
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ci_upper_logistic = ci_mod[["97.5 %"]]
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# custom_chisq and fisher: OR p-value and CI
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or_mychisq = mychisq_or(dst, mut)
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or_fisher = fisher.test(dst, mut)$estimate
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or_fisher = or_fisher[[1]]
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pval_fisher = fisher.test(dst, mut)$p.value
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ci_lower_fisher = fisher.test(dst, mut)$conf.int[1]
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ci_upper_fisher = fisher.test(dst, mut)$conf.int[2]
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# chi sq estimates
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estimate_chisq = chisq.test(dst, mut)$statistic; estimate_chisq
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est_chisq = estimate_chisq[[1]]; print(est_chisq)
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pval_chisq = chisq.test(dst, mut)$p.value
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# build a row to append to df
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row = data.frame(mutation = m
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, af = afs
|
||||
, beta_logistic = beta_logistic
|
||||
, or_logistic = or_logistic
|
||||
, pval_logistic = pval_logistic
|
||||
, se_logistic = se_logistic
|
||||
, zval_logistic = zval_logistic
|
||||
, ci_low_logistic = ci_lower_logistic
|
||||
, ci_hi_logistic = ci_upper_logistic
|
||||
, or_mychisq = or_mychisq
|
||||
, or_fisher = or_fisher
|
||||
, pval_fisher = pval_fisher
|
||||
, ci_low_fisher= ci_lower_fisher
|
||||
, ci_hi_fisher = ci_upper_fisher
|
||||
, est_chisq = est_chisq
|
||||
, pval_chisq = pval_chisq
|
||||
)
|
||||
#print(row)
|
||||
|
||||
or_df <<- rbind(or_df, row)
|
||||
|
||||
})
|
||||
|
||||
write.csv(or_df, 'test_ors.csv')
|
||||
|
||||
#=================================
|
||||
# testing logistic or with maxit, etc.
|
||||
|
||||
print(paste0('subset to iterate over;', snps))
|
||||
|
||||
# start loop
|
||||
perfectSeparation <- function(w) {
|
||||
if(grepl("fitted probabilities numerically 0 or 1 occurred",
|
||||
as.character(w))) {ww <<- ww+1}
|
||||
}
|
||||
|
||||
for (i in snps){
|
||||
|
||||
print(i)
|
||||
|
||||
# IV
|
||||
#mut<-as.numeric(grepl(i,raw_data$all_muts_gene))
|
||||
mut = grepl(i,raw_data$all_muts_gene)
|
||||
table(mut)
|
||||
|
||||
# DV
|
||||
#dst<-as.numeric(raw_data[[drug]])
|
||||
dst = raw_data[[drug]]
|
||||
|
||||
# table
|
||||
print(table(dst, mut))
|
||||
|
||||
#=====================
|
||||
# logistic regression, glm.control(maxit = n)
|
||||
#https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression
|
||||
#=====================
|
||||
#n = 1
|
||||
summary(model<-glm(dst ~ mut
|
||||
, family = binomial
|
||||
#, control = glm.control(maxit = 1)
|
||||
#, options(warn = 1)
|
||||
))
|
||||
#, warning = perfectSeparation))
|
||||
or_logistic = exp(summary(model)$coefficients[2,1]); print(or_logistic)
|
||||
pval_logistic_maxit = summary(model)$coefficients[2,4]; print(pval_logistic_maxit)
|
||||
logistic_se = summary(model)$coefficients[2,2]
|
||||
logistic_zval = summary(model)$coefficients[2,3]
|
||||
ci_mod = exp(confint(model))[2,]
|
||||
logistic_ci_lower = ci_mod[["2.5 %"]]
|
||||
logistic_ci_upper = ci_mod[["97.5 %"]]
|
||||
|
||||
#=====================
|
||||
# fishers test
|
||||
#=====================
|
||||
#attributes(fisher.test(table(dst, mut)))
|
||||
or_fisher = fisher.test(table(dst, mut))$estimate
|
||||
or_fisher = or_fisher[[1]]; or_fisher
|
||||
|
||||
pval_fisher = fisher.test(table(dst, mut))$p.value ; pval_fisher
|
||||
|
||||
#=====================
|
||||
# custom chi square
|
||||
#=====================
|
||||
or_mychisq = mychisq_or(mut,dst)
|
||||
|
||||
#=====================
|
||||
# chi square
|
||||
#=====================
|
||||
#chisq.test(y = dst, x = mut)
|
||||
#attributes(chisq.test(table(dst, mut)))
|
||||
est_chisq = chisq.test(table(dst, mut))$statistic
|
||||
est_chisq = est_chisq[[1]]; est_chisq
|
||||
|
||||
pval_chisq = chisq.test(table(dst, mut))$p.value; pval_chisq
|
||||
|
||||
# all output
|
||||
writeLines(c(paste0("mutation:", i)
|
||||
, paste0("=========================")
|
||||
, paste0("OR logistic:", or_logistic,"--->", "pval logistic:", pval_logistic )
|
||||
, paste0("OR fisher:", or_fisher, "--->","pval fisher:", pval_fisher )
|
||||
, paste0("OR custom chisq:", or_mychisq, "--->","pval fisher:", pval_fisher )
|
||||
, paste0("Chisq estimate:", est_chisq, "--->","pval chisq:", pval_chisq)))
|
||||
}
|
||||
#=====================
|
||||
# fishers test
|
||||
#=====================
|
||||
#attributes(fisher.test(table(dst, mut)))
|
||||
or_fisher = fisher.test(table(dst, mut))$estimate
|
||||
or_fisher = or_fisher[[1]]; or_fisher
|
||||
|
||||
pval_fisher = fisher.test(table(dst, mut))$p.value ; pval_fisher
|
||||
|
||||
|
||||
# https://stats.stackexchange.com/questions/259635/what-is-the-difference-using-a-fishers-exact-test-vs-a-logistic-regression-for
|
||||
#mymethod = "minlike" # default
|
||||
mymethod = "central"
|
||||
#mymethod = "blaker"
|
||||
|
||||
exact2x2(table(dst, mut),tsmethod=mymethod)
|
||||
|
||||
mcnemar.exact(x,y=NULL, conf.level=.95)
|
||||
|
||||
#=====================================================================
|
||||
152
scripts/nssnp_info_format.py
Executable file
152
scripts/nssnp_info_format.py
Executable file
|
|
@ -0,0 +1,152 @@
|
|||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
Created on Wed Jun 10 11:13:49 2020
|
||||
|
||||
@author: tanu
|
||||
"""
|
||||
#==============================================================================
|
||||
# TASK
|
||||
# To format snp_fino.txt file that has already been processed in bash
|
||||
# to include mcsm style muts and gwas style muts. The idea is that the info file
|
||||
# will contain all possible mutation format style to make it easy to populate
|
||||
# and link other files with this sort of meta data. For example: or file
|
||||
#=======================================================================
|
||||
|
||||
# FIXME : add bash info here as well
|
||||
|
||||
#%% useful links
|
||||
#https://chrisalbon.com/python/data_wrangling/pandas_join_merge_dataframe/
|
||||
#https://kanoki.org/2019/11/12/how-to-use-regex-in-pandas/
|
||||
#https://stackoverflow.com/questions/40348541/pandas-diff-with-string
|
||||
#=======================================================================
|
||||
#%% specify dirs
|
||||
import os, sys
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import re
|
||||
import argparse
|
||||
|
||||
homedir = os.path.expanduser('~')
|
||||
os.chdir(homedir + '/git/LSHTM_analysis/scripts')
|
||||
|
||||
#from reference_dict import my_aa_dict
|
||||
#from reference_dict import low_3letter_dict # equivalent of my_aa_dict
|
||||
from reference_dict import oneletter_aa_dict
|
||||
#=======================================================================
|
||||
#%% command line args
|
||||
arg_parser = argparse.ArgumentParser()
|
||||
|
||||
arg_parser.add_argument('-d', '--drug', help = 'drug name', default = 'pyrazinamide')
|
||||
arg_parser.add_argument('-g', '--gene', help = 'gene name (case sensitive)', default = 'pncA') # case sensitive
|
||||
|
||||
args = arg_parser.parse_args()
|
||||
#=======================================================================
|
||||
#%% variables
|
||||
#gene = 'pncA'
|
||||
#drug = 'pyrazinamide'
|
||||
#gene_match = gene +'_p.'
|
||||
|
||||
# cmd variables
|
||||
gene = args.gene
|
||||
drug = args.drug
|
||||
gene_match = gene +'_p.'
|
||||
|
||||
#=======================================================================
|
||||
#%% input and output dirs and files
|
||||
#=======
|
||||
# data dir
|
||||
#=======
|
||||
datadir = homedir + '/' + 'git/Data'
|
||||
indir = datadir + '/' + drug + '/input'
|
||||
outdir = datadir + '/' + drug + '/output'
|
||||
|
||||
#=======
|
||||
# input
|
||||
#=======
|
||||
|
||||
gene_info_filename = 'ns'+ gene.lower()+ '_snp_info1.txt'
|
||||
gene_info = indir + '/' + gene_info_filename
|
||||
print('gene info file: ', gene_info
|
||||
, '\n============================================================')
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
gene_snp_info_filename = 'ns' + gene.lower() + '_snp_info.csv' # other one is called AFandOR
|
||||
outfile_snp_info = indir + '/' + gene_snp_info_filename
|
||||
print('Output file: ', outfile_snp_info
|
||||
, '\n============================================================')
|
||||
|
||||
#%% read files: preformatted using bash
|
||||
info_df2 = pd.read_csv(gene_info, sep = '\t', header = 0) #303, 10
|
||||
|
||||
#%% Split into three cols with 1-letter aa_code & combine to get mutationinformation column
|
||||
# check mutation format in exisiting df
|
||||
info_df2.head()
|
||||
info_df2['mut_info'].head()
|
||||
|
||||
print('Creating column: mutationinformation')
|
||||
info_df2_ncols = len(info_df2.columns)
|
||||
|
||||
info_df2['wild_type'] = info_df2['mut_info'].str.extract('(\w{1})>')
|
||||
info_df2['position'] = info_df2['mut_info'].str.extract('(\d+)')
|
||||
info_df2['mutant_type'] = info_df2['mut_info'].str.extract('>\d+(\w{1})')
|
||||
|
||||
info_df2['mutationinformation'] = info_df2['wild_type'] + info_df2['position'] + info_df2['mutant_type']
|
||||
|
||||
# sanity check
|
||||
ncols_add = 4 # Beware hardcoded
|
||||
if len(info_df2.columns) == info_df2_ncols + ncols_add:
|
||||
print('PASS: Succesfully extracted and added mutationinformation (mcsm style) as below\n'
|
||||
, info_df2['mutationinformation'].head()
|
||||
, '\n=====================================================================')
|
||||
else:
|
||||
print('FAIL: No. of cols mismatch'
|
||||
,'\noriginal length:', info_df2_ncols
|
||||
, '\nExpected no. of cols:', info_df2_ncols + ncols_add
|
||||
, '\nGot no. of cols:', len(info_df2.columns))
|
||||
sys.exit()
|
||||
|
||||
# update ncols
|
||||
info_df2_ncols = len(info_df2.columns)
|
||||
|
||||
#%% Creating column 'mutation' which as mutation of the format;
|
||||
# <gene_match>.lower()<abc>1<cde>: pnca_p.trp68gly
|
||||
# match the 'one_letter_code' value to get the dict key (three-letter code)
|
||||
|
||||
print('Creating column: mutation')
|
||||
|
||||
# dict to use: oneletter_aa_dict
|
||||
lookup_dict = dict()
|
||||
for k1, v1 in oneletter_aa_dict.items():
|
||||
lookup_dict[k1] = v1['three_letter_code_lower']
|
||||
for k2, v2 in lookup_dict.items():
|
||||
info_df2['wt_3let'] = info_df2['wild_type'].squeeze().map(lookup_dict)
|
||||
info_df2['mt_3let'] = info_df2['mutant_type'].squeeze().map(lookup_dict)
|
||||
|
||||
# create column mutation
|
||||
info_df2['mutation'] = info_df2['wt_3let'] + info_df2['position'] + info_df2['mt_3let']
|
||||
|
||||
# add prefix: gene_match to each value in column
|
||||
info_df2['mutation'] = gene_match.lower() + info_df2['mutation'].astype(str)
|
||||
|
||||
# sanity check
|
||||
ncols_add = 3 # Beware hardcoded
|
||||
if len(info_df2.columns) == info_df2_ncols + ncols_add:
|
||||
print('PASS: Succesfully created column mutation as below\n'
|
||||
, info_df2['mutation'].head()
|
||||
, '\n=====================================================================')
|
||||
else:
|
||||
print('FAIL: No. of cols mismatch\noriginal length:', info_df2_ncols
|
||||
, '\nExpected no. of cols:', info_df2_ncols + ncols_add
|
||||
, '\nGot no. of cols:', len(info_df2.columns))
|
||||
sys.exit()
|
||||
|
||||
#%% write file
|
||||
print('\n====================================================================='
|
||||
, '\nWriting output file:\n', outfile_snp_info
|
||||
, '\nNo.of rows:', len(info_df2)
|
||||
, '\nNo. of cols:', len(info_df2.columns))
|
||||
info_df2.to_csv(outfile_snp_info, index = False)
|
||||
|
||||
|
|
@ -221,9 +221,9 @@ else:
|
|||
print('FAIL: Second cross check unsuccessfull. Debug please!')
|
||||
sys.exit()
|
||||
|
||||
#%% extract mut info into three cols
|
||||
orig_len = len(dfm2_mis.columns)
|
||||
|
||||
#%% extract mut info into three cols
|
||||
dfm2_mis['wild_type'] = dfm2_mis['mut_info'].str.extract('(\w{1})>')
|
||||
dfm2_mis['position'] = dfm2_mis['mut_info'].str.extract('(\d+)')
|
||||
dfm2_mis['mutant_type'] = dfm2_mis['mut_info'].str.extract('>\d+(\w{1})')
|
||||
|
|
|
|||
|
|
@ -57,12 +57,17 @@ print('Input filename:', in_filename
|
|||
#%% end of variable assignment for input and output files
|
||||
#=======================================================================
|
||||
#%% Read input file
|
||||
my_aa = pd.read_csv(infile) #20, 6
|
||||
aa_table = pd.read_csv(infile) #20, 6
|
||||
|
||||
#------------------------
|
||||
#1) 3-letter (lower) code as key
|
||||
#-------------------------
|
||||
# assign the one_letter code as the row names so that it is easier to create
|
||||
# a dict of dicts using index
|
||||
#my_aa = pd.read_csv('aa_codes.csv', index_col = 0) #20, 6 #a way to it since it is the first column
|
||||
my_aa = my_aa.set_index('three_letter_code_lower') #20, 5
|
||||
my_aa = aa_table.set_index('three_letter_code_lower') #20, 5
|
||||
my_aa.columns
|
||||
my_aa.index
|
||||
|
||||
#==================
|
||||
# convert file
|
||||
|
|
@ -75,6 +80,40 @@ my_aa = my_aa.set_index('three_letter_code_lower') #20, 5
|
|||
my_aa_dict = my_aa.to_dict('index') #20, with 5 subkeys
|
||||
print('Printing my_aa_dict:', my_aa_dict.keys())
|
||||
|
||||
#FIXME : use the below in all code
|
||||
low_3letter_dict = my_aa.to_dict('index') #20, with 5 subkeys
|
||||
print('Printing lower-case 3 letter aa dict:',low_3letter_dict.keys())
|
||||
|
||||
#------------------------
|
||||
#2) 1-letter code as key
|
||||
#-------------------------
|
||||
aa_1let = aa_table.set_index('one_letter_code') #20, 5
|
||||
aa_1let.columns
|
||||
aa_1let.index
|
||||
|
||||
oneletter_aa_dict = aa_1let.to_dict('index') #20, with 5 subkeys
|
||||
print('Printing one letter aa dict:', oneletter_aa_dict.keys())
|
||||
|
||||
#------------------------
|
||||
#3) amino acid name as key
|
||||
#-------------------------
|
||||
aa_name = aa_table.set_index('amino_acid_name') #20, 5
|
||||
aa_name.columns
|
||||
aa_name.index
|
||||
|
||||
aa_name_dict = aa_name.to_dict('index') #20, with 5 subkeys
|
||||
print('Printing amino acid names aa dict:', aa_name_dict.keys())
|
||||
|
||||
#------------------------
|
||||
#3) 3 letter uppercase as key
|
||||
#-------------------------
|
||||
aa_up3let = aa_table.set_index('three_letter_code_upper') #20, 5
|
||||
aa_up3let.columns
|
||||
aa_up3let.index
|
||||
|
||||
up_3letter_aa_dict = aa_up3let.to_dict('index') #20, with 5 subkeys
|
||||
print('Printing upper case 3 letter aa dict:', up_3letter_aa_dict.keys())
|
||||
|
||||
#================================================
|
||||
# dict of aa with their corresponding properties
|
||||
# This is defined twice
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue