512 lines
19 KiB
Text
512 lines
19 KiB
Text
, stringsAsFactors = F)
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x = as.numeric(grepl(i,raw_data$all_muts_pza))
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# DV: pyrazinamide 0 or 1
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y = as.numeric(raw_data$pyrazinamide)
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table(y,x)
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# run glm model
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model = glm(y ~ x, family = binomial)
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#model = glm(y ~ x, family = binomial(link = "logit"))
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summary(model)
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#**********
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# extract relevant model output
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#**********
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# extract log OR i.e the Beta estimate of the logistic model for a given snp
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my_logor = summary(model)$coefficients[2,1]
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print(paste0('Beta:', my_logor))
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# extract SE of the logistic model for a given snp
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my_se = summary(model)$coefficients[2,2]
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print(paste0('SE:', my_se))
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# extract Z of the logistic model for a given snp
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my_zval = summary(model)$coefficients[2,3]
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print(paste0('Z-value:', my_zval))
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# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
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#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
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my_or = exp(summary(model)$coefficients[2,1])
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print(paste0('OR:', my_or))
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# sanity check : should be True
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log(my_or) == my_logor
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# extract P-value of the logistic model for a given snp
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my_pval = summary(model)$coefficients[2,4]
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print(paste0('P-value:', my_pval))
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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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my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
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print(paste0('CI:', my_ci))
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#*************
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# Assign the regression output in the original df
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# you can use ('=' or '<-/->')
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#*************
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#pnca_snps_or$logistic_logOR[pnca_snps_or$Mutationinformation == i] = my_logor
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my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$Mutationinformation == i]
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my_logor
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pnca_snps_or$Mutationinformation == i
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View(pnca_snps_or)
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#===============
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# Step 4: Calculate for one snp
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# using i, when you run the loop, it is easy
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#===============
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i = "pnca_p.trp68gly"
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pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
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, stringsAsFactors = F
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, header = T) #2133
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# uncomment as necessary
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pnca_snps_or = pnca_snps_or[1:5,]
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pnca_snps_or = pnca_snps_or[c(1:5),]
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#===============
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pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
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, stringsAsFactors = F
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, header = T) #2133
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pnca_snps_or = pnca_snps_or[1:5,]
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pnca_snps_or = pnca_snps_or[c(1:5),]
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pnca_snps_or = pnca_snps_or[1:5]
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pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
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, stringsAsFactors = F
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, header = T) #2133
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pnca_snps_or = pnca_snps_or[1:5]
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pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
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, stringsAsFactors = F
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, header = T) #2133
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foo = pnca_snps_or[c(1:5,)]
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foo = pnca_snps_or[c(1:5),]
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foo = as.data.frame(pnca_snps_or[c(1:5),])
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View(foo)
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# create an empty dataframe
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pnca_snps_or = as.data.frame(pnca_snps_or[c(1:5),])
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# IV: corresponds to each unique snp (extracted using grep)
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x = as.numeric(grepl(i,raw_data$all_muts_pza))
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# DV: pyrazinamide 0 or 1
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y = as.numeric(raw_data$pyrazinamide)
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table(y,x)
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# run glm model
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model = glm(y ~ x, family = binomial)
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#model = glm(y ~ x, family = binomial(link = "logit"))
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summary(model)
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my_logor = summary(model)$coefficients[2,1]
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print(paste0('Beta:', my_logor))
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# extract SE of the logistic model for a given snp
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my_se = summary(model)$coefficients[2,2]
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print(paste0('SE:', my_se))
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# extract Z of the logistic model for a given snp
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my_zval = summary(model)$coefficients[2,3]
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print(paste0('Z-value:', my_zval))
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# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
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#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
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my_or = exp(summary(model)$coefficients[2,1])
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print(paste0('OR:', my_or))
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# sanity check : should be True
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log(my_or) == my_logor
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# extract P-value of the logistic model for a given snp
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my_pval = summary(model)$coefficients[2,4]
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print(paste0('P-value:', my_pval))
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# extract confint interval of snp (2 steps, since the output is a named number)
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ci_mod = exp(confint(model))[2,]
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my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
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print(paste0('CI:', my_ci))
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#*************
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# Assign the regression output in the original df
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# you can use ('=' or '<-/->')
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#*************
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#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
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my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
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my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
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my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
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my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
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my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
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my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
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#===============
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# Step 4: Iterate through this unique list
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# and calculate OR, but only for one snp
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# this is test before you apply it all others
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#===============
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pnca_snps_or$mutation == i
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View(pnca_snps_or)
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# create an empty dataframe
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pnca_snps_or = data.frame(mutation = i)
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my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
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my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
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my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
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my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
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my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
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my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
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View(pnca_snps_or_copy)
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#===============
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# Step 4: Iterate through this unique list
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# and calculate OR, but only for one snp
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# this is test before you apply it all others
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#===============
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#reset original df so you don't make a mistake
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pnca_snps_or = pnca_snps_or_copy
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for (i in pnca_snps_unique){
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print(i)
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}
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pnca_snps_or = pnca_snps_or_copy #2133, 1
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#........................................
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# create an empty dataframe : uncomment as necessary
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pnca_snps_or = data.frame(mutation = c(i, "blank_mut")
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#........................................
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# create an empty dataframe : uncomment as necessary
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pnca_snps_or = data.frame(mutation = c(i, "blank_mut"))
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#........................................
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# create an empty dataframe : uncomment as necessary
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pnca_snps_or = data.frame(mutation = c(i, "blank_mut"))
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View(pnca_snps_or)
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# IV: corresponds to each unique snp (extracted using grep)
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x = as.numeric(grepl(i,raw_data$all_muts_pza))
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# DV: pyrazinamide 0 or 1
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y = as.numeric(raw_data$pyrazinamide)
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table(y,x)
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# run glm model
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model = glm(y ~ x, family = binomial)
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#model = glm(y ~ x, family = binomial(link = "logit"))
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summary(model)
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#**********
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# extract relevant model output
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#**********
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# extract log OR i.e the Beta estimate of the logistic model for a given snp
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my_logor = summary(model)$coefficients[2,1]
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print(paste0('Beta:', my_logor))
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# extract SE of the logistic model for a given snp
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my_se = summary(model)$coefficients[2,2]
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print(paste0('SE:', my_se))
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# extract Z of the logistic model for a given snp
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my_zval = summary(model)$coefficients[2,3]
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print(paste0('Z-value:', my_zval))
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# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
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#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
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my_or = exp(summary(model)$coefficients[2,1])
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print(paste0('OR:', my_or))
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# sanity check : should be True
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log(my_or) == my_logor
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# extract P-value of the logistic model for a given snp
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my_pval = summary(model)$coefficients[2,4]
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print(paste0('P-value:', my_pval))
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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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my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
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print(paste0('CI:', my_ci))
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#*************
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# Assign the regression output in the original df
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# you can use ('=' or '<-/->')
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#*************
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#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
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my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
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my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
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my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
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my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
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my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
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my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
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View(pnca_snps_or)
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pnca_snps_or = pnca_snps_or_copy #2133, 1
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for (i in pnca_snps_unique){
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print(i)
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#*************
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# start logistic regression model building
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#*************
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# set the IV and DV for the logistic regression model
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# IV: corresponds to each unique snp (extracted using grep)
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x = as.numeric(grepl(i,raw_data$all_muts_pza))
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# DV: pyrazinamide 0 or 1
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y = as.numeric(raw_data$pyrazinamide)
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table(y,x)
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# run glm model
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model = glm(y ~ x, family = binomial)
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#model = glm(y ~ x, family = binomial(link = "logit"))
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summary(model)
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#**********
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# extract relevant model output
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#**********
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# extract log OR i.e the Beta estimate of the logistic model for a given snp
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my_logor = summary(model)$coefficients[2,1]
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print(paste0('Beta:', my_logor))
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# extract SE of the logistic model for a given snp
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my_se = summary(model)$coefficients[2,2]
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print(paste0('SE:', my_se))
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# extract Z of the logistic model for a given snp
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my_zval = summary(model)$coefficients[2,3]
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print(paste0('Z-value:', my_zval))
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# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
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#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
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my_or = exp(summary(model)$coefficients[2,1])
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print(paste0('OR:', my_or))
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# sanity check : should be True
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log(my_or) == my_logor
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# extract P-value of the logistic model for a given snp
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my_pval = summary(model)$coefficients[2,4]
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print(paste0('P-value:', my_pval))
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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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my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
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print(paste0('CI:', my_ci))
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#*************
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# Assign the regression output in the original df
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# you can use ('=' or '<-/->')
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#*************
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#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
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my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
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my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
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my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
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my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
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my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
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my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
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}
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warnings()
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View(pnca_snps_or)
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View(pnca_snps_or_copy)
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#sanity check
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pnca_snps_or$mutation == i1
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#sanity check
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pnca_snps_or[pnca_snps_or$mutation == i1]
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pnca_snps_or[pnca_snps_or$mutation == i2]
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pnca_snps_or[pnca_snps_or$mutation == i2,]
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pnca_snps_or1 = unique(pnca_snps_or)
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write.csv(pnca_snps_or1, "../Data_original/valid_pnca_snps_with_OR.csv")
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# you only need it for the unique mutations
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pnca_snps_or = unique(pnca_snps_or) #2133, 1
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for (i in pnca_snps_unique){
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print(i)
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#*************
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# start logistic regression model building
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#*************
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# set the IV and DV for the logistic regression model
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# IV: corresponds to each unique snp (extracted using grep)
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x = as.numeric(grepl(i,raw_data$all_muts_pza))
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# DV: pyrazinamide 0 or 1
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y = as.numeric(raw_data$pyrazinamide)
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table(y,x)
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# run glm model
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model = glm(y ~ x, family = binomial)
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#model = glm(y ~ x, family = binomial(link = "logit"))
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summary(model)
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#**********
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# extract relevant model output
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#**********
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# extract log OR i.e the Beta estimate of the logistic model for a given snp
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my_logor = summary(model)$coefficients[2,1]
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print(paste0('Beta:', my_logor))
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# extract SE of the logistic model for a given snp
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my_se = summary(model)$coefficients[2,2]
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print(paste0('SE:', my_se))
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# extract Z of the logistic model for a given snp
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my_zval = summary(model)$coefficients[2,3]
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print(paste0('Z-value:', my_zval))
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# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
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#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
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my_or = exp(summary(model)$coefficients[2,1])
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print(paste0('OR:', my_or))
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# sanity check : should be True
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log(my_or) == my_logor
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# extract P-value of the logistic model for a given snp
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my_pval = summary(model)$coefficients[2,4]
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print(paste0('P-value:', my_pval))
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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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my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
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print(paste0('CI:', my_ci))
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#*************
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# Assign the regression output in the original df
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# you can use ('=' or '<-/->')
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#*************
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#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
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my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
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my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
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my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
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my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
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my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
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my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
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}
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View(pnca_snps_or)
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2.290256e+01
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1.561132e+06
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3.242285e-04
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#sanity check
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pnca_snps_or[pnca_snps_or$mutation == i1]
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pnca_snps_or[pnca_snps_or$mutation == i2,]
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write.csv(pnca_snps_or1, "../Data_original/valid_pnca_snps_with_OR.csv")
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my_data = read.csv("../Data_original/meta_pza_with_AF.csv"
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, stringsAsFactors = FALSE) #11374, 19
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View(my_data)
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# remove the first column
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my_data = my_data[-1] #11374, 18
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# check if first col is 'id': should be TRUE
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colnames(my_data)[1] == 'id'
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# sanity check
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snps_all = unique(my_data$mutation)# 337
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pnca_snps_or = snps_all
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pnca_snps_or = as.data.frame(snps_all)
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View(pnca_snps_or)
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snps_all[-"true_wt"]
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pnca_snps_or = as.data.frame(snps_all[-c(1,1)])
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View(pnca_snps_or)
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snps_all = as.data.frame(snps_all)
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View(snps_all)
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#remove true_wt entry
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w1 = which(rownames(snps_all) == "true_wt")
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View(snps_all)
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#remove true_wt entry
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w1 = which(snps_all$snps_all == "true_wt")
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rm(pnca_snps_or)
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pnca_snps_or = snps_all[-w1]
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pnca_snps_or = snps_all[,-w1]
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pnca_snps_or = as.data.frame(snps_all[-c(1,1)])
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#remove true_wt entry
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w1 = which(snps_all) == "true_wt"
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pnca_snps_or = as.data.frame(snps_all[-c(1,1)])
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my_data = read.csv("../Data_original/meta_pza_with_AF.csv"
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, stringsAsFactors = FALSE) #11374, 19
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# remove the first column
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my_data = my_data[-1] #11374, 18
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# check if first col is 'id': should be TRUE
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colnames(my_data)[1] == 'id'
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# sanity check
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snps_all = unique(my_data$mutation)# 337
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snps_all = as.data.frame(snps_all)
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snps_all[-c(1,1)]
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pnca_snps_or = as.data.frame(snps_all[-c(1,1)])
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pnca_snps_or = as.data.frame(snps_all[, -c(1,1)])
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#remove true_wt entry
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#w1 = which(snps_all) == "true_wt"
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pnca_snps_or = snps_all
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pnca_snps_or = pnca_snps_or_copy
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#remove true_wt entry
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#w1 = which(snps_all) == "true_wt"
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pnca_snps_or = snps_all
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pnca_snps_or -> pnca_snps_or_copy
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#===============
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# Step 4: Iterate through this unique list
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# and calculate OR for each snp
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# and assign to the pnca_snps_or df that has
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# each row as a unique snp
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#===============
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# reset original df so you don't make a mistake: IMPORTANT
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pnca_snps_or = pnca_snps_or_copy #2133, 1
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# you only need it for the unique mutations
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pnca_snps_or = unique(pnca_snps_or) #337, 1
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for (i in pnca_snps_unique){
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print(i)
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#*************
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# start logistic regression model building
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#*************
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# set the IV and DV for the logistic regression model
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# IV: corresponds to each unique snp (extracted using grep)
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x = as.numeric(grepl(i,raw_data$all_muts_pza))
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# DV: pyrazinamide 0 or 1
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y = as.numeric(raw_data$pyrazinamide)
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table(y,x)
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# run glm model
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model = glm(y ~ x, family = binomial)
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#model = glm(y ~ x, family = binomial(link = "logit"))
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summary(model)
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#**********
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|
# extract relevant model output
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#**********
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# extract log OR i.e the Beta estimate of the logistic model for a given snp
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my_logor = summary(model)$coefficients[2,1]
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print(paste0('Beta:', my_logor))
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# extract SE of the logistic model for a given snp
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my_se = summary(model)$coefficients[2,2]
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print(paste0('SE:', my_se))
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# extract Z of the logistic model for a given snp
|
|
my_zval = summary(model)$coefficients[2,3]
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print(paste0('Z-value:', my_zval))
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# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
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#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
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my_or = exp(summary(model)$coefficients[2,1])
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print(paste0('OR:', my_or))
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|
# sanity check : should be True
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|
log(my_or) == my_logor
|
|
# extract P-value of the logistic model for a given snp
|
|
my_pval = summary(model)$coefficients[2,4]
|
|
print(paste0('P-value:', my_pval))
|
|
# extract confint interval of snp (2 steps, since the output is a named number)
|
|
ci_mod = exp(confint(model))[2,]
|
|
my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
|
|
print(paste0('CI:', my_ci))
|
|
#*************
|
|
# Assign the regression output in the original df
|
|
# you can use ('=' or '<-/->')
|
|
#*************
|
|
#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
|
|
my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
|
|
my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
|
|
my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
|
|
my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
|
|
my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
|
|
my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
|
|
}
|
|
getwd()
|
|
#setwd("~/Documents/git/LSHTM_Y1_PNCA/meta_data_analysis") # work
|
|
setwd("~/git/LSHTM_Y1_PNCA/meta_data_analysis") # thinkpad
|
|
#setwd("/Users/tanu/git/LSHTM_Y1_PNCA/meta_data_analysis") # mac
|
|
getwd()
|
|
#===============
|
|
# Step 1: read raw data
|
|
#===============
|
|
raw_data<-read.csv("../Data_original/original_tanushree_data_v2.csv"
|
|
,stringsAsFactors = F)[,c("id","pyrazinamide","dr_mutations_pyrazinamide","other_mutations_pyrazinamide")]#19265, 4
|
|
raw_data<-raw_data[!is.na(raw_data$pyrazinamide),]#12511, 4
|
|
# combine the two mutation columns
|
|
raw_data$all_mutations_pyrazinamide<-paste(raw_data$dr_mutations_pyrazinamide, raw_data$other_mutations_pyrazinamide)#12511, 5
|
|
head(raw_data$all_mutations_pyrazinamide)
|
|
# create yet another column that contains all the mutations but in lower case
|
|
raw_data$all_muts_pza = tolower(raw_data$all_mutations_pyrazinamide) #12511, 6
|
|
table(grepl("pnca_p",raw_data$all_muts_pza))
|
|
#FALSE TRUE
|
|
#10603 1908
|
|
pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
|
|
, stringsAsFactors = F
|
|
, header = T) #2133
|
|
# subset a snall section to test
|
|
#pnca_snps_or_copy = pnca_snps_or
|
|
#pnca_snps_or = pnca_snps_or_copy
|
|
pnca_snps_unique = unique(pnca_snps_or$mutation) #293
|
|
i2 = "pnca_p.trp68gly" # Should exist
|
|
grep(i2, pnca_snps_unique)
|
|
my_data = read.csv("../Data_original/meta_pza_with_AF.csv"
|
|
, stringsAsFactors = FALSE) #11374, 19
|
|
# remove the first column
|
|
my_data = my_data[-1] #11374, 18
|
|
# check if first col is 'id': should be TRUE
|
|
colnames(my_data)[1] == 'id'
|
|
# sanity check
|
|
head(my_data$mutation)
|
|
my_data = unique(my_data$mutation)
|
|
my_data[!duplicated(my_data$mutation)]
|
|
my_data_unique = my_data[!duplicated(my_data$mutation),]
|
|
my_data[!duplicated('mutation'),]
|
|
my_data_unique = my_data[!duplicated(my_data[,'mutation']),]
|
|
my_data_unique = my_data[!duplicated(my_data['mutation']),]
|
|
getwd()
|
|
setwd("/git/LSHTM_analysis/meta_data_analysis")
|
|
getwd()
|
|
getwd()
|
|
setwd("/git/github/LSHTM_analysis/meta_data_analysis")
|
|
getwd()
|
|
#===============
|
|
# Step 1: read GWAS raw data stored in Data_original/
|
|
#===============
|
|
infile = read.csv("../Data_original", file.choose(), stringsAsFactors = F))
|
|
c = file.choose()
|
|
c = file.choose(../Data_original)
|
|
c = read.csv(file.choose(), stringsAsFactors = F)
|
|
#===============
|
|
# Step 1: read GWAS raw data stored in Data_original/
|
|
#===============
|
|
infile = read.csv(file.choose(), stringsAsFactors = F))
|
|
c = read.csv(file.choose(), stringsAsFactors = F)
|
|
#===============
|
|
# Step 1: read GWAS raw data stored in Data_original/
|
|
#===============
|
|
infile = read.csv(file.choose(), stringsAsFactors = F)
|
|
#===============
|
|
# Step 1: read GWAS raw data stored in Data_original/
|
|
#===============
|
|
infile = read.csv(file.choose(), stringsAsFactors = F)
|
|
raw_data = infile[,c("id","pyrazinamide","dr_mutations_pyrazinamide","other_mutations_pyrazinamide")]
|
|
outdir = paste0("../mcsm_analysis",drug,"/Data/")
|
|
# define output variables
|
|
drug = 'pyrazinamide'
|
|
outdir = paste0("../mcsm_analysis",drug,"/Data/")
|
|
outdir = paste0("../mcsm_analysis/",drug,"/Data/")
|
|
outFile = "meta_data_with_AFandOR.csv"
|
|
output_filename = paste0(outdir, outFile)
|
|
output_filename
|