import commit

mcsm_analysis/pyrazinamide/scripts/mcsm/step3c_data_cleaning.R

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+getwd()
+#setwd("~/Documents/git/LSHTM_analysis/mcsm_complex1/Results") # work
+setwd("~/git/LSHTM_analysis/mcsm_complex1/Results") # thinkpad
+#setwd("/Users/tanu/git/LSHTM_analysis/mcsm_complex1/Results") # mac
+getwd()
+
+#=======================================================
+#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("336_complex1_formatted_results.csv"
+              , header = T
+              , stringsAsFactors = FALSE)
+dim(data)
+#335, 10
+str(data)
+
+###########################
+##### 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$Mutationinformation))
+#FALSE   
+#335
+
+#extract duplicated entries
+dups = data[duplicated(data$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) ) #13
+
+rm(dups)
+
+#***************************************************************
+#select non-duplicated SNPs and create a new df
+df = data[!duplicated(data$Mutationinformation),] #309, 10
+#***************************************************************
+#sanity check
+u = unique(df$Mutationinformation)
+u2 = unique(data$Mutationinformation)
+table(u%in%u2)
+#TRUE 
+#309 
+#should all be 1, hence 309 1's
+sum(table(df$Mutationinformation) == 1)
+
+#sort df by Position
+#MANUAL CHECKPOINT:  
+#foo <- df[order(df$Position),]
+#df <- df[order(df$Position),]
+
+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 #335, 11
+
+#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")  #335, 12
+
+table(df$Lig_outcome)
+#Destabilizing   Stabilizing 
+#281             54 
+
+table(df$DUET_outcome)
+#Destabilizing   Stabilizing 
+#288             47 
+#==============================
+#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) #335, 13
+
+#clear variables
+rm(n, wt, mut)
+
+################ end of data cleaning