254 lines
8.6 KiB
R
254 lines
8.6 KiB
R
my_afor <- function ( infile_master
|
|
, infile_metadata
|
|
, outfile
|
|
, drug
|
|
, gene
|
|
, idcol = "id"
|
|
, dr_muts_col
|
|
, other_muts_col){
|
|
|
|
#===========================================
|
|
# 1: Read master/raw data stored in Data/
|
|
#===========================================
|
|
raw_data_all = read.csv(infile_master, stringsAsFactors = F)
|
|
|
|
cat("\nExtracting columns based on variables:\n"
|
|
, drug
|
|
, "\n"
|
|
, dr_muts_col
|
|
, "\n"
|
|
, other_muts_col
|
|
, "\n===============================================================")
|
|
|
|
raw_data = raw_data_all[,c(idcol
|
|
, drug
|
|
, dr_muts_col
|
|
, other_muts_col)]
|
|
#rm(raw_data_all)
|
|
#rm(indir, in_filename_master, infile_master)
|
|
|
|
#-------------------
|
|
# 1a: exclude na
|
|
#--------------------
|
|
raw_data = raw_data[!is.na(raw_data[[drug]]),]
|
|
|
|
total_samples = length(unique(raw_data[[idcol]]))
|
|
cat(paste0("\nTotal samples without NA in", " ", drug, " is:", total_samples))
|
|
|
|
# sanity check: should be true
|
|
cat("\nThis should be True:\n"
|
|
, is.numeric(total_samples))
|
|
|
|
#----------------------------------------
|
|
# 1b: combine the two mutation columns
|
|
#-----------------------------------------
|
|
all_muts_colname = paste0("all_mutations_", drug)
|
|
cat(paste("\nNew column added:", all_muts_colname))
|
|
raw_data[[all_muts_colname]] = paste(raw_data[[dr_muts_col]], raw_data[[other_muts_col]])
|
|
head(raw_data[[all_muts_colname]])
|
|
|
|
#----------------------------------------------
|
|
# 1c: create yet another column that contains
|
|
# all the mutations but in lower case
|
|
#-----------------------------------------------
|
|
head(raw_data[[all_muts_colname]])
|
|
raw_data$all_muts_gene = tolower(raw_data[[all_muts_colname]])
|
|
head(raw_data$all_muts_gene)
|
|
|
|
# sanity checks
|
|
gene_match = paste0(gene,"_p.")
|
|
cat(paste0("\nconverting gene match: ", gene_match, " ", "to lowercase\n"))
|
|
gene_match = tolower(gene_match)
|
|
|
|
table(grepl(gene_match,raw_data$all_muts_gene))
|
|
|
|
# sanity check: should be TRUE
|
|
if(sum(table(grepl(gene_match, raw_data$all_muts_gene))) == total_samples){
|
|
cat("\nPASS: Total no. of samples match\n")
|
|
} else{
|
|
cat("\nFAIL: No. of samples mismatch\n")
|
|
exit()
|
|
}
|
|
|
|
#====================================================
|
|
# 2: Read valid snps for which OR
|
|
# can be calculated
|
|
#=====================================================
|
|
cat(paste0("\nReading gene associated metadata:", infile_metadata))
|
|
|
|
gene_metadata = read.csv(infile_metadata
|
|
, stringsAsFactors = F
|
|
, header = T)
|
|
|
|
cat(paste("\nDim of gene_metadata:\n"
|
|
, dim(gene_metadata)))
|
|
|
|
# count na in drug column
|
|
tot_drug_na = sum(is.na(gene_metadata[[drug]]))
|
|
expected_rows = nrow(gene_metadata) - tot_drug_na
|
|
|
|
# drop na from the drug column
|
|
gene_snps_or = gene_metadata[!is.na(gene_metadata[[drug]]),]
|
|
|
|
# sanity check
|
|
if(nrow(gene_snps_or) == expected_rows){
|
|
cat("\nPASS: no. of rows match with expected_rows")
|
|
} else{
|
|
cat("\nFAIL: nrows mismatch.")
|
|
exit()
|
|
}
|
|
|
|
# extract unique snps to iterate over for AF and OR calcs
|
|
gene_snps_unique = unique(gene_snps_or$mutation)
|
|
|
|
cat(paste0("\nTotal no. of distinct comp snps to perform OR calcs: ", length(gene_snps_unique)))
|
|
|
|
#==================================================
|
|
# OR calcs using the following 4
|
|
#1) logistic
|
|
#2) custom chisq.test
|
|
#3) fisher
|
|
#4) chisq.test
|
|
|
|
# adjusted logistic (NO good)
|
|
# kinship (separate script)
|
|
#=================================================
|
|
#snps <- gene_snps_unique [1:5] # small test
|
|
snps <- gene_snps_unique
|
|
cat(paste0("\nRunning calculations for:"
|
|
, length(snps), " nssnps\n"
|
|
, "\ngene: ", gene
|
|
, "\ndrug: ", drug ))
|
|
|
|
# DV: <drug> 0 or 1
|
|
dst = raw_data[[drug]]
|
|
|
|
# initialise an empty df
|
|
ors_df = data.frame()
|
|
x = sapply(snps,function(m){
|
|
|
|
mut = grepl(m,raw_data$all_muts_gene)
|
|
mut = as.numeric(mut)
|
|
cat(paste0("\nRunning mutation:", m, "\n"))
|
|
|
|
model<-glm(dst ~ mut, family = binomial)
|
|
|
|
#-------------------
|
|
# allele frequency
|
|
#-------------------
|
|
afs = mean(mut)
|
|
|
|
#-------------------
|
|
# logistic model
|
|
#-------------------
|
|
beta_logistic = summary(model)$coefficients[2,1]
|
|
|
|
or_logistic = exp(summary(model)$coefficients[2,1])
|
|
#print(paste0("logistic OR:", or_logistic))
|
|
|
|
pval_logistic = summary(model)$coefficients[2,4]
|
|
#print(paste0("logistic pval:", pval_logistic))
|
|
|
|
se_logistic = summary(model)$coefficients[2,2]
|
|
#print(paste0("logistic SE:", se_logistic))
|
|
|
|
zval_logistic = summary(model)$coefficients[2,3]
|
|
#print(paste0("logistic zval:", zval_logistic))
|
|
|
|
ci_mod = exp(confint(model))[2,]
|
|
#print(paste0("logistic CI:", ci_mod))
|
|
|
|
ci_lower_logistic = ci_mod[["2.5 %"]]
|
|
ci_upper_logistic = ci_mod[["97.5 %"]]
|
|
|
|
#-------------------
|
|
# custom_chisq and fisher: OR p-value and CI
|
|
#-------------------
|
|
or_mychisq = mychisq_or(dst, mut)
|
|
#print(paste0("mychisq OR:", or_mychisq))
|
|
|
|
odds_fisher = fisher.test(table(dst, mut))$estimate
|
|
or_fisher = odds_fisher[[1]]
|
|
|
|
pval_fisher = fisher.test(table(dst, mut))$p.value
|
|
|
|
ci_lower_fisher = fisher.test(table(dst, mut))$conf.int[1]
|
|
ci_upper_fisher = fisher.test(table(dst, mut))$conf.int[2]
|
|
|
|
#-------------------
|
|
# chi sq estimates
|
|
#-------------------
|
|
estimate_chisq = chisq.test(table(dst, mut))$statistic; estimate_chisq
|
|
est_chisq = estimate_chisq[[1]]; print(est_chisq)
|
|
|
|
pval_chisq = chisq.test(table(dst, mut))$p.value
|
|
|
|
# build a row to append to df
|
|
row = data.frame(mutation = m
|
|
, af = afs
|
|
, beta_logistic = beta_logistic
|
|
, or_logistic = or_logistic
|
|
, pval_logistic = pval_logistic
|
|
, se_logistic = se_logistic
|
|
, zval_logistic = zval_logistic
|
|
, ci_low_logistic = ci_lower_logistic
|
|
, ci_hi_logistic = ci_upper_logistic
|
|
, or_mychisq = or_mychisq
|
|
, or_fisher = or_fisher
|
|
, pval_fisher = pval_fisher
|
|
, ci_low_fisher= ci_lower_fisher
|
|
, ci_hi_fisher = ci_upper_fisher
|
|
, est_chisq = est_chisq
|
|
, pval_chisq = pval_chisq
|
|
)
|
|
|
|
ors_df <<- rbind(ors_df, row)
|
|
})
|
|
|
|
#==============================================
|
|
# Writing file with calculated ORs and AFs
|
|
#==============================================
|
|
cat(paste0("\nwriting output file: "
|
|
, "\nFile: ", outfile))
|
|
|
|
write.csv(ors_df, outfile
|
|
, row.names = F)
|
|
|
|
cat(paste0("\nFinished writing:"
|
|
, outfile
|
|
, "\nNo. of rows: ", nrow(ors_df)
|
|
, "\nNo. of cols: ", ncol(ors_df)))
|
|
cat("\n======sneak peek into a few muts with prominent or and p-vals=======\n")
|
|
|
|
cat(paste0("======================================="
|
|
, "\nmutation with highest logistic OR:"
|
|
, "\n=======================================\n"))
|
|
print(ors_df[which(ors_df$or_logistic == max(ors_df$or_logistic)),])
|
|
|
|
cat(paste0("======================================="
|
|
, "\nmutation with highest mychisq OR:"
|
|
, "\n=======================================\n"))
|
|
|
|
print(ors_df[which(ors_df$or_mychisq == max(ors_df$or_mychisq)),])
|
|
|
|
# gives too many due to Inf
|
|
#cat(paste0("======================================="
|
|
#, "\nmutation with highest fisher OR:"
|
|
#, "\n=======================================\n"))
|
|
#print(ors_df[which(ors_df$or_fisher == max(ors_df$or_fisher)),])
|
|
|
|
cat(paste0("======================================="
|
|
, "\nmutation with lowest logistic pval:"
|
|
, "\n=======================================\n"))
|
|
print(ors_df[which(ors_df$pval_logistic == min(ors_df$pval_logistic)),])
|
|
|
|
cat(paste0("======================================="
|
|
, "\nmutation with lowest fisher pval:"
|
|
, "\n=======================================\n"))
|
|
print(ors_df[which(ors_df$pval_fisher == min(ors_df$pval_fisher)),])
|
|
|
|
################################################################
|
|
cat("\nEnd of script: calculated AF, OR, pvalues and saved file\n")
|
|
#################################################################
|
|
|
|
}
|