#============================================
# TASK: To calculate Allele Frequency and
# Odds Ratio from master data
# and add the calculated params to meta_data extracted from
# pnca_data_extraction.py
#===========================================
homedir = '~'
getwd()
#setwd('~/git/LSHTM_analysis/meta_data_analysis')
setwd(paste0(homedir, '/', 'git/LSHTM_analysis/meta_data_analysis'))
getwd()
  
#%% variable assignment: input and output paths & filenames
drug = 'pyrazinamide'
gene = 'pncA'
gene_match = paste0(gene,'_p.')
print(gene_match)

#=======
# input dir
#=======
# file1: Raw data
#indir = 'git/Data/pyrazinamide/input/original'
indir = paste0('git/Data', '/', drug, '/', 'input/original')
in_filename  = 'original_tanushree_data_v2.csv'
infile = paste0(homedir, '/', indir, '/', in_filename)
print(paste0('Reading infile:', ' ', infile) )

# file2: file to extract valid snps and add calcs to: pnca_metadata.csv {outfile3 from data extraction script}
indir_metadata = paste0('git/Data', '/', drug, '/', 'output')
in_filename_metadata = 'pnca_metadata.csv'
infile_metadata = paste0(homedir, '/', indir_metadata, '/', in_filename_metadata)
print(paste0('Reading metadata infile:', infile_metadata))

#=========
# output dir
#=========
# output filename in respective section at the time of outputting files
#outdir = 'git/Data/pyrazinamide/output'
outdir = paste0('git/Data', '/', drug, '/', 'output')
out_filename = paste0(tolower(gene),'_', 'meta_data_with_AFandOR.csv')
outfile = paste0(homedir, '/', outdir, '/', out_filename)
print(paste0('Output file with full path:', outfile))

#%% end of variable assignment for input and output files

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

raw_data = raw_data_all[,c("id"
                     , "pyrazinamide"
                     , "dr_mutations_pyrazinamide"
                     , "other_mutations_pyrazinamide")]
rm(raw_data_all)

rm(indir, in_filename, infile)

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

total_samples = length(unique(raw_data$id))
print(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
#####
raw_data$all_mutations_pyrazinamide = paste(raw_data$dr_mutations_pyrazinamide
                                            , raw_data$other_mutations_pyrazinamide)
head(raw_data$all_mutations_pyrazinamide)

#####
# 1c: create yet another column that contains all the mutations but in lower case
#####
raw_data$all_muts_pnca = tolower(raw_data$all_mutations_pyrazinamide) 

# sanity checks
#table(grepl("pnca_p",raw_data$all_muts_pnca))
print(paste0('converting gene match:', gene_match, ' ', 'to lowercase'))
gene_match = tolower(gene_match)

table(grepl(gene_match,raw_data$all_muts_pnca))

# sanity check: should be TRUE
#sum(table(grepl("pnca_p",raw_data$all_muts_pnca))) == total_samples
sum(table(grepl(gene_match,raw_data$all_muts_pnca))) == total_samples

# set up variables: can be used for logistic regression as well
i  = "pnca_p.ala134gly" # has a NA, should NOT exist
table(grepl(i,raw_data$all_muts_pnca))

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

mut = grepl(i,raw_data$all_muts_pnca)
dst = raw_data$pyrazinamide
table(mut, dst)

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

#===============
# Step 2: Read valid snps for which OR can be calculated (infile_comp_snps.csv)
#=============== 
print(paste0('Reading metadata infile:', infile_metadata))

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


# clear variables
rm(homedir, in_filename, indir, infile)
rm(indir_metadata, infile_metadata, in_filename_metadata)

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

# drop na from the pyrazinamide colum
pnca_snps_or = pnca_metadata[!is.na(pnca_metadata$pyrazinamide),]

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

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

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

# Define OR function
x = as.numeric(mut)
y = dst
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)
  
  }

dst = raw_data$pyrazinamide
ors = sapply(pnca_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_pnca)
  or(mut,dst)
})

ors

pvals = sapply(pnca_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_pnca)
  fisher.test(mut,dst)$p.value
})

pvals

afs = sapply(pnca_snps_unique,function(m){
  mut = grepl(m,raw_data$all_muts_pnca)
  mean(mut)
})

afs

# check ..hmmm
afs['pnca_p.trp68gly']
afs['pnca_p.gln10pro'] 
afs['pnca_p.leu4ser'] 

plot(density(log(ors)))
plot(-log10(pvals))
hist(log(ors)
     , breaks = 100
     )

# FIXME: could be good to add a sanity check
if (table(names(ors) ==  names(pvals)) & table(names(ors) == names(afs)) & table(names(pvals) == names(afs)) == length(pnca_snps_unique)){
print('PASS: names of ors, pvals and afs match: proceed with combining into a single df')
} else{
  print('FAIL: names of ors, pvals and afs mismatch')
}

# combine
comb_AF_and_OR = data.frame(ors, pvals, afs)
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)){
  print('PASS: rownames and mutaion col values match')
}else{
  print('FAIL: rownames and mutation col values mismatch')
}

############
# Merge 1: 
###########
df1 = pnca_metadata
df2 = comb_AF_and_OR

head(df1$mutation); head(df2$mutation)

# FIXME: newlines
print(paste0('merging two dfs: '
      ,'\ndf1 (big df i.e. meta data) nrows: ', nrow(df1)
      ,'\ndf2 (small df i.e af, or, pval) nrows: ', nrow(df2)
      , 'expected rows in merged df: ', nrow(df1), 'expected cols in merged_df: ', (ncol(df1) + ncol(df2) - 1)))

merged_df = merge(df1 # big file
                  , df2 # small (afor file)
                  , by = "mutation"
                  , all.x = T) # because you want all the entries of the meta data 

# sanity check
if(ncol(merged_df) == (ncol(df1) + ncol(df2) - 1)){
  print(paste0('PASS: no. of cols is as expected: ', ncol(merged_df)))
} else{
  print('FAIL: no.of cols mistmatch')
}

# quick check
i  = "pnca_p.ala134gly" # has all NAs in pyrazinamide, should be NA in ors, etc.
merged_df[merged_df$mutation == i,]

# count na in each column
na_count = sapply(merged_df, function(y) sum(length(which(is.na(y))))); na_count
# only some or and Af should be NA
#Row.names           ors               pvals               afs 
#63                  63               63                  63

# reassign custom colnames
colnames(merged_df)[colnames(merged_df)== "ors"] <- "OR"
colnames(merged_df)[colnames(merged_df)== "afs"] <- "AF"
colnames(merged_df)[colnames(merged_df)== "pvals"] <- "pvalue"

colnames(merged_df)

# add log OR and neglog pvalue
merged_df$logor = log(merged_df$OR)
is.numeric(merged_df$logor)

merged_df$neglog10pvalue = -log10(merged_df$pvalue) 
is.numeric(merged_df$neglog10pvalue)

merged_df$AF_percent = merged_df$AF*100
is.numeric(merged_df$AF_percent)

# check AFs
#i = 'pnca_p.trp68gly'
i = 'pnca_p.gln10pro' 
#i = 'pnca_p.leu4ser'
merged_df[merged_df$mutation == i,]

# FIXME: harcoding (beware!), NOT FATAL though!
ncol_added = 3 

print(paste0('Added', ncol_added, ' ', 'more cols to merged_df i.e log10 OR and -log10 P-val: '
             , 'no. of cols in merged_df now: ', ncol(merged_df)))

#%% write file out: pnca_meta_data_with_AFandOR
print(paste0('writing output file in: '
             , 'Filename: ', out_filename
             , 'Path:', outdir))

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

print(paste0('Finished writing:', out_filename, '\nExpected no. of cols:', ncol(merged_df)))
print('======================================================================')
rm(out_filename)