all OR calcs using sapply and output as df
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1 changed files with 260 additions and 79 deletions
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@ -1,5 +1,5 @@
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#########################################################
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# TASK: To compare OR from master data
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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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@ -28,10 +28,10 @@ outdir = paste0(datadir, '/', drug, '/', 'output')
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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 data', ' ', infile) )
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cat(paste0('Reading infile1: raw snps', ' ', infile) )
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# infile2: _gene associated meta data file to extract valid snps and add calcs to.
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# This is outfile3 from data_extraction.py
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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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@ -39,17 +39,17 @@ 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),'_', 'meta_data_with_AF_OR.csv')
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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 data stored in Data/
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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 data (all remove entries with NA in pza column)
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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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@ -147,7 +147,6 @@ if(nrow(gene_snps_or) == expected_rows){
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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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@ -163,7 +162,7 @@ cat(paste0('Total no. of distinct comp snps to perform OR calcs: ', length(gene_
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# Define OR function
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#x = as.numeric(mut)
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#y = dst
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logistic_chisq_or = function(x,y){
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custom_chisq_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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@ -179,7 +178,7 @@ logistic_chisq_or = function(x,y){
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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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@ -211,6 +210,11 @@ table(mut, dst, sid)
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#============================
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# compare OR
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chisq.test(table(mut,dst))
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chisq.test(table(mut,dst)) $ statistic
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f = chisq.test(table(mut,dst)) $ statistic
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chisq.test(dst, mut) $ statistic
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fisher.test(table(mut, dst))
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fisher.test(table(mut, dst))$p.value
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fisher.test(table(mut, dst))$estimate
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@ -219,53 +223,79 @@ logistic_chisq_or(mut,dst)
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# logistic or
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summary(model<-glm(dst ~ mut, family = binomial))
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or_logistic = exp(summary(model)$coefficients[2,1]); print(or_logistic)
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pval_logistic = summary(model)$coefficients[2,4]; print(pval_logistic)
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pval_logistic_maxit = summary(model)$coefficients[2,4]; print(pval_logistic_maxit)
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# adjusted logistic or
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# extract SE of the logistic model for a given snp
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logistic_se = summary(model)$coefficients[2,2]
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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)$coefficients[2,3]
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print(paste0('Z-value:', logistic_zval))
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# extract confint interval of snp (2 steps, since the output is a named number)
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ci_mod = exp(confint(model))[2,]
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print(paste0('CI:', ci_mod))
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#logistic_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
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logistic_ci_lower = ci_mod[["2.5 %"]]
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logistic_ci_upper = ci_mod[["97.5 %"]]
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print(paste0('CI_lower:', logistic_ci_lower))
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print(paste0('CI_upper:', logistic_ci_upper))
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# adjusted logistic or: doesn't seem to make a difference
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summary(model2<-glm(dst ~ mut + sid, family = binomial))
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or_logistic2 = exp(summary(model2)$coefficients[2,1]); print(or_logistic2)
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pval_logistic2 = summary(model2)$coefficients[2,4]; print(pval_logistic2)
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#=========================
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#============ looping with sapply
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#####################
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# iterate: subset
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#####################
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ors = sapply(gene_snps_unique,function(m){
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snps_test = c("pnca_p.trp68gly", "pnca_p.leu4ser", "pnca_p.leu159arg","pnca_p.his57arg" )
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snps = snps_test[1:4]
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snps
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ors = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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logistic_chisq_or(mut,dst)
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})
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ors
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pvals = sapply(gene_snps_unique,function(m){
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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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pvals
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afs = sapply(gene_snps_unique,function(m){
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afs = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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mean(mut)
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})
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afs
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# logistic or
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ors_logistic = sapply(gene_snps_unique,function(m){
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## logistic or
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ors_logistic = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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model<-glm(dst ~ mut, family = binomial)
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or_logistic = exp(summary(model)$coefficients[2,1])
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#pval_logistic = summary(model)$coefficients[2,4]
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#logistic_se = summary(model)$coefficients[2,2]
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#logistic_zval = summary(model)$coefficients[2,3]
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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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#logistic_ci_upper = ci_mod[["97.5 %"]]
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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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## logistic p-value
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pvals_logistic = sapply(gene_snps_unique,function(m){
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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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@ -274,7 +304,7 @@ pvals_logistic = sapply(gene_snps_unique,function(m){
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head(pvals_logistic); head(names(pvals_logistic))
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## logistic se
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se_logistic = sapply(gene_snps_unique,function(m){
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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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@ -293,7 +323,7 @@ zval_logistic = sapply(gene_snps_unique,function(m){
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head(zval_logistic); head(names(zval_logistic))
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## logistic ci - lower bound
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ci_lb_logistic = sapply(gene_snps_unique,function(m){
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ci_lb_logistic = sapply(snps,function(m){
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mut = grepl(m,raw_data$all_muts_gene)
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model<-glm(dst ~ mut , family = binomial)
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ci_mod = exp(confint(model))[2,]
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@ -303,7 +333,7 @@ ci_lb_logistic = sapply(gene_snps_unique,function(m){
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head(ci_lb_logistic); head(names(ci_lb_logistic))
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## logistic ci - upper bound
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ci_ub_logistic = sapply(gene_snps_unique,function(m){
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ci_ub_logistic = sapply(snps,function(m){
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mut = grepl(m,raw_data$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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@ -314,7 +344,7 @@ ci_ub_logistic = sapply(gene_snps_unique,function(m){
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head(ci_ub_logistic); head(names(ci_ub_logistic))
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# logistic adj # Doesn't seem to make a difference
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logistic_ors2 = sapply(gene_snps_unique,function(m){
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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$id)
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@ -327,50 +357,106 @@ logistic_ors2
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or_logistic2; pval_logistic2
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head(logistic_ors)
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#====================================
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# logistic
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summary(model<-glm(dst ~ mut
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, family = binomial
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#, control = glm.control(maxit = 1)
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#, options(warn = 1)
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))
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or_logistic = exp(summary(model)$coefficients[2,1]); print(or_logistic)
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pval_logistic_maxit = summary(model)$coefficients[2,4]; print(pval_logistic_maxit)
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#===========================================================
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#%%
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# sapply with multiple values
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# extract SE of the logistic model for a given snp
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logistic_se = summary(model)$coefficients[2,2]
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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]
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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,]
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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 %"]]
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logistic_ci_upper = ci_mod[["97.5 %"]]
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print(paste0('CI_lower:', logistic_ci_lower))
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print(paste0('CI_upper:', logistic_ci_upper))
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#####################
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# iterate: subset
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#####################
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#https://gist.github.com/primaryobjects/33adabc337edd67b4a8d
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snps_test = c("pnca_p.trp68gly", "pnca_p.leu4ser", "pnca_p.leu159arg","pnca_p.his57arg" )
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data = snps_test[1:2]
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snps = snps_test[1:4]
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data
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################# start loop
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for (i in data){
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snps
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# DV: pyrazinamide 0 or 1
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dst = raw_data[[drug]]
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# yayy works!
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testdf = 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)$coefficients[2,3]
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ci_mod = exp(confint(model))[2,]
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ci_lower_logistic = ci_mod[["2.5 %"]]
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ci_upper_logistic = ci_mod[["97.5 %"]]
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# custom_chisq and fisher: OR p-value and CI
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or_mychisq = custom_chisq_or(dst, mut)
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or_fisher = fisher.test(dst, mut)$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
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, beta_logistic = beta_logistic
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, or_logistic = or_logistic
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, pval_logistic = pval_logistic
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, se_logistic = se_logistic
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, zval_logistic = zval_logistic
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, ci_low_logistic = ci_lower_logistic
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, ci_hi_logistic = ci_upper_logistic
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, or_mychisq = or_mychisq
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, or_fisher = or_fisher
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, pval_fisher = pval_fisher
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, ci_low_fisher= ci_lower_fisher
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, ci_hi_fisher = ci_upper_fisher
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, est_chisq = est_chisq
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, pval_chisq = pval_chisq
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)
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#print(row)
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testdf <<- rbind(testdf, row)
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})
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write.csv(testdf, 'test_ors.csv')
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#=================================
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####################
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# iterate: subset
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#####################
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print(paste0('subset to iterate over;', snps))
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# start loop
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perfectSeparation <- function(w) {
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if(grepl("fitted probabilities numerically 0 or 1 occurred",
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as.character(w))) {ww <<- ww+1}
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}
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for (i in snps){
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print(i)
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@ -386,7 +472,6 @@ for (i in data){
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# table
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print(table(dst, mut))
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#=====================
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# logistic regression, glm.control(maxit = n)
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#https://stats.stackexchange.com/questions/11109/how-to-deal-with-perfect-separation-in-logistic-regression
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@ -405,6 +490,7 @@ for (i in data){
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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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logistic_ci_upper = ci_mod[["97.5 %"]]
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#=====================
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# fishers test
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#=====================
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@ -431,17 +517,7 @@ for (i in data){
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, paste0("OR_fisher:", or_fisher, "--->","P-val_fisher:", pval_fisher )
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, paste0("Chi_sq_estimate:", est_chisq, "--->","P-val_chisq:", pval_chisq)))
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}
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i = "gene_p.leu159arg"
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mut<-as.numeric(grepl(i,raw_data$all_muts_pza))
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# DV
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dst<-as.numeric(raw_data$pyrazinamide)
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# tablehttps://mail.google.com/mail/?tab=rm&ogbl
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table(dst, mut)
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#=====================
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#=====================
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# fishers test
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#=====================
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#attributes(fisher.test(table(dst, mut)))
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@ -455,3 +531,108 @@ table(dst, mut)
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exact2x2(table(dst, mut),tsmethod="central")
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#=====================================================================
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# iterate over a df and then add these values
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#
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my_data = as.data.frame(gene_snps_unique)
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colnames(my_data) = "mutation"
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print(colnames(my_data))
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perfectSeparation <- function(w) {
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if(grepl("fitted probabilities numerically 0 or 1 occurred",
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as.character(w))) {ww <<- ww+1}
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}
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for(i in my_data$mutation) {
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print(paste0('snp to iterate over:', i))
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}
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for(i in my_data$mutation) {
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print(paste0('snp to iterate over:', i))
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#####
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# Run logistic regression
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#####
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#*************
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# start logistic regression model building
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# set the IV and DV for the logistic regression model and model
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#*************
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# IV: corresponds to each unique snp (extracted using grep)
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mut = as.numeric(grepl(i,raw_data$dr_muts_pza))
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# DV: pyrazinamide 0 or 1
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dst = as.numeric(raw_data$pyrazinamide)
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tab = table(mut, dst)
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print(tab)
|
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|
||||
# glm model: with and without maxit
|
||||
model = tryCatch( glm(dst ~ mut
|
||||
, family = binomial
|
||||
#, control = glm.control(maxit = 1) # only used when required for one step estimator
|
||||
), warning = perfectSeparation)
|
||||
|
||||
model = glm(dst ~ mut, family = binomial)
|
||||
|
||||
print(summary(model))
|
||||
|
||||
#**********
|
||||
# extract relevant model output
|
||||
#**********
|
||||
# extract log OR i.e the Beta estimate of the logistic model for a given snp
|
||||
my_logor = summary(model)$coefficients[2,1]
|
||||
print(paste0('Beta:', my_logor))
|
||||
|
||||
# Dervive OR i.e exp(my_or) from the logistic model for a given snp
|
||||
#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
|
||||
my_or = exp(summary(model)$coefficients[2,1])
|
||||
print(paste0('OR:', my_or))
|
||||
|
||||
# extract SE of the logistic model for a given snp
|
||||
my_se = summary(model)$coefficients[2,2]
|
||||
print(paste0('SE:', my_se))
|
||||
|
||||
# extract Z of the logistic model for a given snp
|
||||
my_zval = summary(model)$coefficients[2,3]
|
||||
print(paste0('Z-value:', my_zval))
|
||||
|
||||
# extract P-value of the logistic model for a given snp
|
||||
my_pval = summary(model)$coefficients[2,4]
|
||||
print(paste0('P-value:', my_pval))
|
||||
|
||||
# extract confint interval of snp (2 steps, since the output is a named number)
|
||||
ci_mod = exp(confint(model))[2,]
|
||||
#my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
|
||||
|
||||
my_ci_lower = ci_mod[["2.5 %"]]
|
||||
my_ci_upper = ci_mod[["97.5 %"]]
|
||||
|
||||
print(paste0('CI_lower:', my_ci_lower))
|
||||
print(paste0('CI_upper:', my_ci_upper))
|
||||
|
||||
#*************
|
||||
# Assign the regression output in the to df (meta_pza_pnca_snps_only)
|
||||
# you can use ('=' or '<-/->')
|
||||
#*************
|
||||
#my_data$logistic_logOR[my_data$mutation == i] = my_logor
|
||||
|
||||
my_or -> my_data$OR[my_data$mutation == i]
|
||||
|
||||
my_pval -> my_data$pvalue[my_data$mutation == i]
|
||||
|
||||
my_zval -> my_data$zvalue[my_data$mutation == i]
|
||||
|
||||
my_se -> my_data$logistic_se[my_data$mutation == i]
|
||||
|
||||
my_ci_lower -> my_data$ci_lower[my_data$mutation == i]
|
||||
|
||||
my_ci_upper -> my_data$ci_upper[my_data$mutation == i]
|
||||
|
||||
#=#=#=#=#=#=#=#
|
||||
# COMMENT: This assigns the relevant extracted output
|
||||
# to the df and fills NA where the mutation (row) doesn't exist
|
||||
# in my mutation list I am iterating over
|
||||
#=#=#=#=#=#=#=#
|
||||
|
||||
}
|
||||
Loading…
Add table
Add a link
Reference in a new issue