getwd()
#setwd("~/git/LSHTM_analysis/mcsm_complex1/Results")
getwd()

#=======================================================
# TASK: read formatted_results_df.csv to complete 
# missing info, adding DUET categories, assigning
# meaningful colnames, etc.

# Requirements:
# input: output of step3b, python processing,
  # path: Data/<drug>/input/processed/<filename>"
# output: NO output as the next scripts refers to this
# for yet more processing
#=======================================================

# specify variables for input and output paths and filenames
homedir = "~"
basedir = "/git/Data/pyrazinamide/input"
inpath = "/processed"
in_filename = "/complex1_formatted_results.csv"
infile = paste0(homedir, basedir, inpath, in_filename)
print(paste0("Input file is:", infile))

#======================================================
#TASK: To tidy the columns so you can generate figures
#=======================================================
####################
#### read file #####: this will be the output from python script (csv file)
####################
data = read.csv(infile
              , header = T
              , stringsAsFactors = FALSE)
dim(data)
str(data)

# clear variables
rm(homedir, basedir, inpath, in_filename, infile)

###########################
##### Data processing #####
###########################

# populate mutation information columns as currently it is empty
head(data$Mutationinformation)
tail(data$Mutationinformation)

# should not be blank: create muation information
data$Mutationinformation = paste0(data$Wild.type, data$Position, data$Mutant.type)

head(data$Mutationinformation)
tail(data$Mutationinformation)
#write.csv(data, 'test.csv')

##########################################
# Remove duplicate SNPs as a sanity check
##########################################
# very important
table(duplicated(data$Mutationinformation))

# extract duplicated entries
dups = data[duplicated(data$Mutationinformation),] #0

# No of dups should match with the no. of TRUE in the above table 
#u_dups = unique(dups$Mutationinformation) #10
sum( table(dups$Mutationinformation) )

#***************************************************************
# select non-duplicated SNPs and create a new df
df = data[!duplicated(data$Mutationinformation),]
#***************************************************************
# sanity check
u = unique(df$Mutationinformation)
u2 = unique(data$Mutationinformation)
table(u%in%u2)

# should all be 1
sum(table(df$Mutationinformation) == 1)

# sort df by Position
# MANUAL CHECKPOINT:  
#foo <- df[order(df$Position),]
#df <- df[order(df$Position),]

# clear variables
rm(u, u2, dups)

####################
#### give meaningful colnames to reflect units to enable correct data type
####################

#=======
#STEP 1
#========
# make a copy of the PredictedAffinityColumn and call it Lig_outcome
df$Lig_outcome = df$PredictedAffinityChange

 #make Predicted...column numeric and outcome column categorical
head(df$PredictedAffinityChange)
df$PredictedAffinityChange = gsub("log.*"
                                  , ""
                                  , df$PredictedAffinityChange)

# sanity checks
head(df$PredictedAffinityChange)

# should be numeric, check and if not make it numeric
is.numeric( df$PredictedAffinityChange )

# change to numeric
df$PredictedAffinityChange = as.numeric(df$PredictedAffinityChange)

# should be TRUE
is.numeric( df$PredictedAffinityChange )

# change the column name to indicate units
n = which(colnames(df) == "PredictedAffinityChange"); n
colnames(df)[n] = "PredAffLog"
colnames(df)[n]

#========
#STEP 2
#========
# make Lig_outcome column categorical showing effect of mutation
head(df$Lig_outcome)
df$Lig_outcome = gsub("^.*-"
                  , "",
                  df$Lig_outcome)
# sanity checks
head(df$Lig_outcome)

# should be factor, check and if not change it to factor
is.factor(df$Lig_outcome) 

# change to factor
df$Lig_outcome = as.factor(df$Lig_outcome)

# should be TRUE
is.factor(df$Lig_outcome) 

#========
#STEP 3
#========
# gsub
head(df$Distancetoligand)
df$Distancetoligand = gsub("&Aring;"
                           , ""
                           , df$Distancetoligand)
# sanity checks
head(df$Distancetoligand)

# should be numeric, check if not change it to numeric
is.numeric(df$Distancetoligand)

# change to numeric
df$Distancetoligand = as.numeric(df$Distancetoligand)

# should be TRUE
is.numeric(df$Distancetoligand)

# change the column name to indicate units
n = which(colnames(df) == "Distancetoligand")
colnames(df)[n] <- "Dis_lig_Ang"
colnames(df)[n]

#========
#STEP 4
#========
#gsub
head(df$DUETstabilitychange)
df$DUETstabilitychange = gsub("Kcal/mol"
                              , ""
                              , df$DUETstabilitychange)
# sanity checks
head(df$DUETstabilitychange)

# should be numeric, check if not change it to numeric
is.numeric(df$DUETstabilitychange)

# change to numeric 
df$DUETstabilitychange = as.numeric(df$DUETstabilitychange)

# should be TRUE
is.numeric(df$DUETstabilitychange)

# change the column name to indicate units
n = which(colnames(df) == "DUETstabilitychange"); n
colnames(df)[n] = "DUETStability_Kcalpermol"
colnames(df)[n]

#========
#STEP 5
#========
# create yet another extra column: classification of DUET stability only
df$DUET_outcome = ifelse(df$DUETStability_Kcalpermol >=0
                         , "Stabilizing"
                         , "Destabilizing") # spelling to be consistent with mcsm

table(df$Lig_outcome)

table(df$DUET_outcome)

#==============================
#FIXME
#Insert a venn diagram
#================================

#========
#STEP 6
#========
# assign wild and mutant colnames correctly

wt = which(colnames(df) == "Wild.type"); wt
colnames(df)[wt] <- "Wild_type"
colnames(df[wt])

mut = which(colnames(df) == "Mutant.type"); mut
colnames(df)[mut] <- "Mutant_type"
colnames(df[mut])

#========
#STEP 7
#========
# create an extra column: maybe useful for some plots
df$WildPos = paste0(df$Wild_type, df$Position)

# clear variables
rm(n, wt, mut)

################ end of data cleaning