#!/usr/bin/env Rscript                                                  
#require('compare')
require('getopt', quietly=TRUE) # We need to be able to parse arguments
#########################################################
# TASK: To calculate Allele Frequency and
# Odds Ratio from master data
# and add the calculated params to meta_data extracted from
# data_extraction.py
#########################################################
#getwd()
setwd('~/git/LSHTM_analysis/scripts')
cat(c(getwd(),'\n'))

# Command line args
spec = matrix(c(
  "drug"   , "d", 1, "character",
  "gene"   , "g", 1, "character"
), byrow = TRUE, ncol = 4)

opt = getopt(spec);

drug = opt$drug
gene = opt$gene

if(is.null(drug)|is.null(gene)) {
    stop('Missing arguments: --drug and --gene must both be specified (case-sensitive)')
    }

#options(scipen = 999) #disabling scientific notation in R.
#options(scipen = 4)  

#%% variable assignment: input and output paths & filenames
#drug = 'pyrazinamide'
#gene = 'pncA'
gene_match = paste0(gene,'_p.')
cat(gene_match)

#===========
# input
#===========
# infile1: Raw data
#indir = 'git/Data/pyrazinamide/input/original'
indir = paste0('~/git/Data')
in_filename  = 'original_tanushree_data_v2.csv'
#in_filename  = 'mtb_gwas_v3.csv'
infile = paste0(indir, '/', in_filename)
cat(paste0('Reading infile1: raw data', ' ', infile) )

# infile2: gene associated meta data file to extract valid snps and add calcs to.
# This is outfile3 from data_extraction.py
indir_metadata = paste0('~/git/Data', '/', drug, '/', 'output')
in_filename_metadata = 'pnca_metadata.csv'
infile_metadata = paste0(indir_metadata, '/', in_filename_metadata)
cat(paste0('Reading infile2: gene associated metadata:', infile_metadata))

#===========
# output
#===========
# outdir = 'git/Data/pyrazinamide/output'
outdir = paste0('~/git/Data', '/', drug, '/', 'output')
#out_filename = paste0(tolower(gene), '_meta_data_with_AF_OR.csv')
out_filename = paste0(tolower(gene), '_af_or.csv')
outfile = paste0(outdir, '/', out_filename)
cat(paste0('Output file with full path:', outfile))
#%% end of variable assignment for input and output files

#########################################################
# 1: Read master/raw data stored in Data/
#########################################################
raw_data_all = read.csv(infile, stringsAsFactors = F)

# building cols to extract
dr_muts_col = paste0('dr_mutations_', drug)
other_muts_col = paste0('other_mutations_', drug)

cat('Extracting columns based on variables:\n'
      , drug
      , '\n'
      , dr_muts_col
      , '\n'
      , other_muts_col
      , '\n===============================================================')

raw_data = raw_data_all[,c("id"
                     , drug
                     , dr_muts_col
                     , other_muts_col)]
rm(raw_data_all)

rm(indir, in_filename, infile)

#===========
# 1a: exclude na
#===========
raw_data = raw_data[!is.na(raw_data[[drug]]),]

total_samples = length(unique(raw_data$id))
cat(paste0('Total samples without NA in', ' ', drug, 'is:', total_samples))

# sanity check: should  be true
is.numeric(total_samples) 

#===========
# 1b: combine the two mutation columns
#===========
all_muts_colname = paste0('all_mutations_', drug)
#raw_data$all_mutations_pyrazinamide = paste(raw_data$dr_mutations_pyrazinamide, raw_data$other_mutations_pyrazinamide)
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
#table(grepl("gene_p",raw_data$all_muts_gene))
cat(paste0('converting gene match:', gene_match, ' ', 'to lowercase'))
gene_match = tolower(gene_match)

table(grepl(gene_match,raw_data$all_muts_gene))

# sanity check: should be TRUE
#sum(table(grepl("gene_p",raw_data$all_muts_gene))) == total_samples
# sanity check
if(sum(table(grepl(gene_match, raw_data$all_muts_gene))) == total_samples){
  cat('PASS: Total no. of samples match')
} else{
  cat('FAIL: No. of samples mismatch')
}

#########################################################
# 2: Read valid snps for which OR 
# can be calculated
#########################################################
cat(paste0('Reading metadata infile:', infile_metadata))

gene_metadata = read.csv(infile_metadata
                         #, file.choose()
                         , stringsAsFactors = F
                         , header = T)


# clear variables
rm(in_filename_metadata, infile_metadata)

# count na in pyrazinamide column
tot_pza_na = sum(is.na(gene_metadata$pyrazinamide))
expected_rows = nrow(gene_metadata) - tot_pza_na

# drop na from the pyrazinamide colum
gene_snps_or = gene_metadata[!is.na(gene_metadata[[drug]]),]

# sanity check
if(nrow(gene_snps_or) == expected_rows){
  cat('PASS: no. of rows match with expected_rows')
} else{
  cat('FAIL: nrows mismatch.')
}

# extract unique snps to iterate over for AF and OR calcs
gene_snps_unique = unique(gene_snps_or$mutation) 

cat(paste0('Total no. of distinct comp snps to perform OR calcs: ', length(gene_snps_unique)))

#=====================================
#OR calcs using the following 4
#1) chisq.test
#2) fisher
#3) modified chisq.test
#4) logistic
#5) adjusted logistic?
#6) kinship (separate script)

#======================================
# TEST FOR ONE
i  = "pnca_p.ala134gly" # has a NA, should NOT exist
table(grepl(i,raw_data$all_muts_gene))

i = "pnca_p.trp68gly"
table(grepl(i,raw_data$all_muts_gene))

i = "pnca_p.his51asp"
table(grepl(i,raw_data$all_muts_gene))

# IV
mut = grepl(i,raw_data$all_muts_gene)

# DV
dst = raw_data[[drug]] #or raw_data[,drug]

table(mut, dst)

#===============================================
# calculating OR

#1) chisq : noy accurate for low counts
chisq.test(table(mut,dst))
chisq.test(table(mut,dst))$p.value
chisq.test(table(mut,dst))$statistic

t = chisq.test(table(mut,dst))$statistic; print(t)
names(t)
# remove suffix
#names(t2) = gsub(".X-squared", "", names(t))

#2) modified chisq OR: custom function
#x = as.numeric(mut)
#y = dst
my_chisq_or = function(x,y){
  tab = as.matrix(table(x,y))
  a = tab[2,2]
  if (a==0){ a<-0.5}
  b = tab[2,1]
  if (b==0){ b<-0.5}
  c = tab[1,2]
  if (c==0){ c<-0.5}
  d = tab[1,1]
  if (d==0){ d<-0.5}
  (a/b)/(c/d)
}
my_chisq_or(mut, dst)

#3) fisher
fisher.test(table(mut, dst))

or_fisher = fisher.test(table(mut, dst))$estimate; print(or_fisher); cat(names(or_fisher))
pval_fisher = fisher.test(table(mut, dst))$p.value; print(pval_fisher) # the same one to be used for custom chisq_or
ci_lb_fisher = fisher.test(table(mut, dst))$conf.int[1]; print(ci_lb_fisher)
ci_ub_fisher = fisher.test(table(mut, dst))$conf.int[2]; print(ci_ub_fisher)

#4) logistic
summary(model<-glm(dst ~ mut
                   , family = binomial
                   #, control = glm.control(maxit = 1)
                   #, options(warn = 1)
                   ))
or_logistic = exp(summary(model)$coefficients[2,1]); print(or_logistic)
pval_logistic = summary(model)$coefficients[2,4]; print(pval_logistic)


#5) logistic adjusted: sample id (# identical results as unadjusted)
#c = raw_data$id[grepl(i,raw_data$all_muts_gene)] 
#sid = grepl(paste(c,collapse="|"), raw_data$id) # else warning that pattern length > 1
#table(sid)   
#table(mut, dst, sid)

#summary(model2<-glm(dst ~ mut + sid
#                   , family = binomial
                   ##, control = glm.control(maxit = 1)
                   ##, options(warn = 1)
#                   ))
#or_logistic2 = exp(summary(model2)$coefficients[2,1]); print(or_logistic2)
#pval_logistic2 = summary(model2)$coefficients[2,4]; print(pval_logistic2)

#===============================================

######################
# AF and OR for all muts: sapply
######################
print(table(dst)); print(table(mut)) # must exist
#dst = raw_data[[drug]] #or raw_data[,drug]

# af
afs = sapply(gene_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  mean(mut)
})

#afs
head(afs)

#1) chi square: original
statistic_chi = sapply(gene_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  chisq.test(mut,dst)$statistic
  
})

# statistic_chi: has suffix added of '.X-squared'
stat_chi = statistic_chi

# remove suffix
names(stat_chi) = gsub(".X-squared", "", names(statistic_chi))

if (names(stat_chi)!= names(statistic_chi) && stat_chi == statistic_chi){
  cat('Sanity check passed: suffix removed correctly\n\n'
      , 'names with suffix:', head(names(statistic_chi)), '\n\n'
      , 'names without suffix:', head(names(stat_chi)), '\n\n'
      , 'values in var with suffix:', head(statistic_chi),'\n'
      , 'values in var without suffix:', head(stat_chi)
      )
}else{
  print('FAIL: suffix removal unsuccessful! Please Debug')
}

## pval
pvals_chi = sapply(gene_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  chisq.test(mut,dst)$p.value
})

#pvals_chi
head(pvals_chi)

#2) chi square: custom 
ors_chi_cus = sapply(gene_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  my_chisq_or(mut,dst)
})

#ors_chi_cus
head(ors_chi_cus)

## pval:fisher (use the same one for custom chi sqaure)
pvals_fisher = sapply(gene_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  fisher.test(mut,dst)$p.value
})

#pvals_fisher
head(pvals_fisher)

#3) fisher
odds_ratio_fisher = sapply(gene_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  fisher.test(mut,dst)$estimate;
})

#ors_fisher
head(odds_ratio_fisher)

# statistic_chi: has suffix added of '.X-squared'
head(odds_ratio_fisher)  

# remove suffix
ors_fisher = odds_ratio_fisher
names(ors_fisher) = gsub(".odds ratio", "", names(odds_ratio_fisher))

if (names(ors_fisher)!= names(odds_ratio_fisher) && ors_fisher == odds_ratio_fisher){
  cat('Sanity check passed: suffix removed correctly\n\n'
      , 'names with suffix:', head(names(odds_ratio_fisher)), '\n\n'
      , 'names without suffix:', head(names(ors_fisher)), '\n\n'
      , 'values in var with suffix:', head(odds_ratio_fisher),'\n'
      , 'values in var without suffix:', head(ors_fisher)
  )
}else{
  print('FAIL: suffix removal unsuccessful! Please Debug')
}


## fisher ci (lower)
ci_lb_fisher  = sapply(gene_snps_unique, function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  low_ci = fisher.test(table(mut, dst))$conf.int[1]
  
})

#ci_lb_fisher 
head(ci_lb_fisher)

## fisher ci (upper)
ci_ub_fisher  = sapply(gene_snps_unique, function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  up_ci = fisher.test(table(mut, dst))$conf.int[2]
  
})

#ci_ub_fisher
head(ci_ub_fisher)

#4) logistic or
ors_logistic = sapply(gene_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  #print(table(dst, mut))
  model<-glm(dst ~ mut , family = binomial)
  or_logistic = exp(summary(model)$coefficients[2,1])
  #pval_logistic = summary(model)$coefficients[2,4]
})

#ors_logistic
head(ors_logistic)

## logistic p-value
pvals_logistic = sapply(gene_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_gene)
  #print(table(dst, mut))
  model<-glm(dst ~ mut , family = binomial)
  pval_logistic = summary(model)$coefficients[2,4]
})

#pvals_logistic
head(pvals_logistic)

#=============================================

# check ..(hmmm) perhaps separate script)
#afs['pnca_p.trp68gly']
#afs['pnca_p.gln10pro'] 
#afs['pnca_p.leu4ser'] 
#
#plot(density(log(ors_logistic)))
#plot(-log10(pvals))
#hist(log(ors)
#     , breaks = 100)

# sanity check: if names are equal (just for 3 vars)
all(sapply(list(names(afs)
                , names(pvals_chi)
                , names(statistic_chi) # should return False
                , names(ors_chi_cus)), function (x) x == names(ors_logistic)))

#compare(names(afs)
#        , names(pvals_chi)
#        , names(statistic_chi) #TEST: should return False, but DOESN'T 
#        , names(ors_chi_cus)
#        , names(stat_chi))$result

#=============== Now with all vars

# sanity check: names for all vars
#c = compare( names(afs)
#            , names(stat_chi)
#            , names(statistic_chi) #TEST: should return False, but DOESN'T 
#            , names(pvals_chi)
#            , names(ors_chi_cus)
#            , names(pvals_fisher)
#            , names(ors_fisher)
#            , names(ci_lb_fisher)
#            , names(ci_ub_fisher)
#            , names(ors_logistic)
#            , names(pvals_logistic))$result; c
     
if (all( sapply( list(names(afs)
, names(stat_chi)
#, names(statistic_chi) # TEST should return FALSE if included
, names(pvals_chi)
, names(ors_chi_cus)
, names(pvals_fisher)
, names(ors_fisher)
, names(ci_lb_fisher)
, names(ci_ub_fisher)
, names(pvals_logistic) ), function (x) x == names(ors_logistic)))
){
cat('PASS: names match: proceed with combining into a single df')
} else {
cat('FAIL: names mismatch')
}            

# combine ors, pvals and afs
cat('Combining calculated params into a df: ors, pvals and afs')

comb_AF_and_OR = data.frame(afs
                            , stat_chi
                            , pvals_chi
                            , ors_chi_cus
                            , pvals_fisher
                            , ors_fisher
                            , ci_lb_fisher
                            , ci_ub_fisher
                            , pvals_logistic
                            , ors_logistic)
                       
cat('No. of rows in comb_AF_and_OR: ', nrow(comb_AF_and_OR)
    , '\nNo. of cols in comb_AF_and_OR: ', ncol(comb_AF_and_OR))

cat('Rownames == mutation: ', head(rownames(comb_AF_and_OR)))

# add rownames of comb_AF_and_OR as an extra column 'mutation' to allow merging based on this column
comb_AF_and_OR$mutation = rownames(comb_AF_and_OR)

# sanity check
if (table(rownames(comb_AF_and_OR) == comb_AF_and_OR$mutation)){
  cat('PASS: rownames and mutaion col values match')
}else{
  cat('FAIL: rownames and mutation col values mismatch')
}
#########################################################
# write file out: pnca_AF_OR
#########################################################
cat(paste0('writing output file: '
           , '\nFilename: ', outfile))

write.csv(comb_AF_and_OR, outfile
          , row.names = F)

cat(paste0('Finished writing:'
           , out_filename
           , '\nNo. of rows: ', nrow(comb_AF_and_OR)
           , '\nNo. of cols: ', ncol(comb_AF_and_OR)))
#************************************************
cat('\n======================================================================\n')
rm(out_filename)
cat('End of script: calculated AF, OR, pvalues and saved file')

#########################################################
# 3: Merge meta data file + calculated num params
#########################################################
#df1 = gene_metadata
#df2 = comb_AF_and_OR




# COMMENT: will do the combining with the other OR and AF (in python)