###########################
# you need merged_df3
# or
# merged_df3_comp
# since these have unique SNPs
# I prefer to use the merged_df3
# because using the _comp dataset means
# we lose some muts and at this level, we should use
# as much info as available
###########################
# uncomment as necessary
#%%%%%%%%%%%%%%%%%%%%%%%%
# REASSIGNMENT
my_df  = merged_df3
#my_df = merged_df3_comp
#%%%%%%%%%%%%%%%%%%%%%%%%
# delete variables not required
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
# quick checks
colnames(my_df)
str(my_df)
###########################
# Data for bfactor figure
# PS average
# Lig average
###########################
head(my_df$Position)
head(my_df$ratioDUET)
# order data frame
df = my_df[order(my_df$Position),]
head(df$Position)
head(df$ratioDUET)
#***********
# PS: average by position
#***********
mean_DUET_by_position <- df %>%
group_by(Position) %>%
summarize(averaged.DUET = mean(ratioDUET))
#***********
# Lig: average by position
#***********
mean_Lig_by_position <- df %>%
group_by(Position) %>%
summarize(averaged.Lig = mean(ratioPredAff))
#***********
# cbind:mean_DUET_by_position and mean_Lig_by_position
#***********
combined = as.data.frame(cbind(mean_DUET_by_position, mean_Lig_by_position ))
# sanity check
# mean_PS_Lig_Bfactor
colnames(combined)
colnames(combined) = c("Position"
, "average_DUETR"
, "Position2"
, "average_PredAffR")
colnames(combined)
identical(combined$Position, combined$Position2)
n = which(colnames(combined) == "Position2"); n
combined_df = combined[,-n]
max(combined_df$average_DUETR) ; min(combined_df$average_DUETR)
max(combined_df$average_PredAffR) ; min(combined_df$average_PredAffR)
#=============
# output csv
#============
outDir = "~/Data/pyrazinamide/input/processed/"
outFile = paste0(outDir, "mean_PS_Lig_Bfactor.csv")
print(paste0("Output file with path will be:","", outFile))
head(combined_df$Position); tail(combined_df$Position)
write.csv(combined_df, outFile
, row.names = F)
getwd()
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
getwd()
########################################################################
# 				Installing and loading required packages 			   #
########################################################################
source("../Header_TT.R")
#source("barplot_colour_function.R")
require(data.table)
require(dplyr)
########################################################################
#		 Read file: call script for combining df for PS		   	   #
########################################################################
source("../combining_two_df.R")
###########################
# This will return:
# df with NA:
# merged_df2
# merged_df3
# df without NA:
# merged_df2_comp
# merged_df3_comp
###########################
#---------------------- PAY ATTENTION
# the above changes the working dir
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
#---------------------- PAY ATTENTION
###########################
# you need merged_df3
# or
# merged_df3_comp
# since these have unique SNPs
# I prefer to use the merged_df3
# because using the _comp dataset means
# we lose some muts and at this level, we should use
# as much info as available
###########################
# uncomment as necessary
#%%%%%%%%%%%%%%%%%%%%%%%%
# REASSIGNMENT
my_df  = merged_df3
#my_df = merged_df3_comp
#%%%%%%%%%%%%%%%%%%%%%%%%
# delete variables not required
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
# quick checks
colnames(my_df)
str(my_df)
###########################
# Data for bfactor figure
# PS average
# Lig average
###########################
head(my_df$Position)
head(my_df$ratioDUET)
# order data frame
df = my_df[order(my_df$Position),]
head(df$Position)
head(df$ratioDUET)
#***********
# PS: average by position
#***********
mean_DUET_by_position <- df %>%
group_by(Position) %>%
summarize(averaged.DUET = mean(ratioDUET))
#***********
# Lig: average by position
#***********
mean_Lig_by_position <- df %>%
group_by(Position) %>%
summarize(averaged.Lig = mean(ratioPredAff))
#***********
# cbind:mean_DUET_by_position and mean_Lig_by_position
#***********
combined = as.data.frame(cbind(mean_DUET_by_position, mean_Lig_by_position ))
# sanity check
# mean_PS_Lig_Bfactor
colnames(combined)
colnames(combined) = c("Position"
, "average_DUETR"
, "Position2"
, "average_PredAffR")
colnames(combined)
identical(combined$Position, combined$Position2)
n = which(colnames(combined) == "Position2"); n
combined_df = combined[,-n]
max(combined_df$average_DUETR) ; min(combined_df$average_DUETR)
max(combined_df$average_PredAffR) ; min(combined_df$average_PredAffR)
#=============
# output csv
#============
outDir = "~/git/Data/pyrazinamide/input/processed/"
outFile = paste0(outDir, "mean_PS_Lig_Bfactor.csv")
print(paste0("Output file with path will be:","", outFile))
head(combined_df$Position); tail(combined_df$Position)
write.csv(combined_df, outFile
, row.names = F)
# read in pdb file complex1
inDir = "~/git/Data/pyrazinamide/input/structure"
inFile = paste0(inDir, "complex1_no_water.pdb")
# read in pdb file complex1
inDir = "~/git/Data/pyrazinamide/input/structure/"
inFile = paste0(inDir, "complex1_no_water.pdb")
complex1 = inFile
my_pdb = read.pdb(complex1
, maxlines = -1
, multi = FALSE
, rm.insert = FALSE
, rm.alt = TRUE
, ATOM.only = FALSE
, hex = FALSE
, verbose = TRUE)
#########################
#3: Read complex pdb file
##########################
source("Header_TT.R")
# list of 8
my_pdb = read.pdb(complex1
, maxlines = -1
, multi = FALSE
, rm.insert = FALSE
, rm.alt = TRUE
, ATOM.only = FALSE
, hex = FALSE
, verbose = TRUE)
rm(inDir, inFile)
#====== end of script
inDir = "~/git/Data/pyrazinamide/input/structure/"
inFile = paste0(inDir, "complex1_no_water.pdb")
complex1 = inFile
complex1 = inFile
my_pdb = read.pdb(complex1
, maxlines = -1
, multi = FALSE
, rm.insert = FALSE
, rm.alt = TRUE
, ATOM.only = FALSE
, hex = FALSE
, verbose = TRUE)
inFile
inDir = "~/git/Data/pyrazinamide/input/structure/"
inFile = paste0(inDir, "complex1_no_water.pdb")
complex1 = inFile
#inFile2 = paste0(inDir, "complex2_no_water.pdb")
#complex2 = inFile2
# list of 8
my_pdb = read.pdb(complex1
, maxlines = -1
, multi = FALSE
, rm.insert = FALSE
, rm.alt = TRUE
, ATOM.only = FALSE
, hex = FALSE
, verbose = TRUE)
rm(inDir, inFile, complex1)
getwd()
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts")
getwd()
source("Header_TT.R")
getwd()
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts")
getwd()
########################################################################
# 				Installing and loading required packages 			               #
########################################################################
source("Header_TT.R")
#########################################################
# TASK: replace B-factors in the pdb file with normalised values
# use the complex file with no water as mCSM lig was
# performed on this file. You can check it in the script: read_pdb file.
#########################################################
###########################
# 2: Read file: average stability values
# or mcsm_normalised file, output of step 4 mcsm pipeline
###########################
inDir = "~/git/Data/pyrazinamide/input/processed/"
inFile = paste0(inDir, "mean_PS_Lig_Bfactor.csv"); inFile
my_df <- read.csv(inFile
#                  , row.names = 1
#                  , stringsAsFactors = F
, header = T)
str(my_df)
source("read_pdb.R") # list of 8
# extract atom list into a variable
# since in the list this corresponds to data frame, variable will be a df
d = my_pdb[[1]]
# make a copy: required for downstream sanity checks
d2 = d
# sanity checks: B factor
max(d$b); min(d$b)
par(oma = c(3,2,3,0)
, mar = c(1,3,5,2)
, mfrow = c(3,2))
#par(mfrow = c(3,2))
#1: Original B-factor
hist(d$b
, xlab = ""
, main = "B-factor")
plot(density(d$b)
, xlab = ""
, main = "B-factor")
# 2: DUET scores
hist(my_df$average_DUETR
, xlab = ""
, main = "Norm_DUET")
plot(density(my_df$average_DUETR)
, xlab = ""
, main = "Norm_DUET")
# Set the margin on all sides
par(oma = c(3,2,3,0)
, mar = c(1,3,5,2)
, mfrow = c(3,2))
#par(mfrow = c(3,2))
#1: Original B-factor
hist(d$b
, xlab = ""
, main = "B-factor")
plot(density(d$b)
, xlab = ""
, main = "B-factor")
# 2: DUET scores
hist(my_df$average_DUETR
, xlab = ""
, main = "Norm_DUET")
plot(density(my_df$average_DUETR)
, xlab = ""
, main = "Norm_DUET")
#=========
# step 1_P1
#=========
# Be brave and replace in place now (don't run sanity check)
# this makes all the B-factor values in the non-matched positions as NA
d$b = my_df$average_DUETR[match(d$resno, my_df$Position)]
#=========
# step 2_P1
#=========
# count NA in Bfactor
b_na = sum(is.na(d$b)) ; b_na
# count number of 0's in Bactor
sum(d$b == 0)
# replace all NA in b factor with 0
d$b[is.na(d$b)] = 0
# sanity check: should be 0
sum(is.na(d$b))
# sanity check: should be True
if (sum(d$b == 0) == b_na){
print ("Sanity check passed: NA's replaced with 0's successfully")
} else {
print("Error: NA replacement NOT successful, Debug code!")
}
max(d$b); min(d$b)
# sanity checks: should be True
if(max(d$b) == max(my_df$average_DUETR)){
print("Sanity check passed: B-factors replaced correctly")
} else {
print ("Error: Debug code please")
}
if (min(d$b) == min(my_df$average_DUETR)){
print("Sanity check passed: B-factors replaced correctly")
} else {
print ("Error: Debug code please")
}
#=========
# step 3_P1
#=========
# sanity check: dim should be same before reassignment
# should be TRUE
dim(d) == dim(d2)
#=========
# step 4_P1
#=========
# assign it back to the pdb file
my_pdb[[1]] = d
max(d$b); min(d$b)
#=========
# step 5_P1
#=========
# output dir
getwd()
outDir = "~/git/Data/pyrazinamide/output/"
getwd()
outFile = paste0(outDir, "complex1_BwithNormDUET.pdb")
outFile = paste0(outDir, "complex1_BwithNormDUET.pdb"); outFile
outDir = "~/git/Data/pyrazinamide/input/structure"
outDir = "~/git/Data/pyrazinamide/input/structure/"
outFile = paste0(outDir, "complex1_BwithNormDUET.pdb"); outFile
write.pdb(my_pdb, outFile)
hist(d$b
, xlab = ""
, main = "repalced-B")
plot(density(d$b)
, xlab = ""
, main = "replaced-B")
# graph titles
mtext(text = "Frequency"
, side = 2
, line = 0
, outer = TRUE)
mtext(text = "DUET_stability"
, side = 3
, line = 0
, outer = TRUE)
#=========================================================
# Processing P2: Replacing  B values with PredAff Scores
#=========================================================
# clear workspace
rm(list = ls())
#=========================================================
# Processing P2: Replacing  B values with PredAff Scores
#=========================================================
# clear workspace
rm(list = ls())
###########################
# 2: Read file: average stability values
# or mcsm_normalised file, output of step 4 mcsm pipeline
###########################
inDir = "~/git/Data/pyrazinamide/input/processed/"
inFile = paste0(inDir, "mean_PS_Lig_Bfactor.csv"); inFile
my_df <- read.csv("../Data/mean_PS_Lig_Bfactor.csv"
#                  , row.names = 1
#                  , stringsAsFactors = F
, header = T)
str(my_df)
#=========================================================
# Processing P2: Replacing B factor with mean ratioLig scores
#=========================================================
#########################
# 3: Read complex pdb file
# form the R script
##########################
source("read_pdb.R") # list of 8
# extract atom list into a vari
inDir = "~/git/Data/pyrazinamide/input/processed/"
inFile = paste0(inDir, "mean_PS_Lig_Bfactor.csv"); inFile
my_df <- read.csv(inFile
#                  , row.names = 1
#                  , stringsAsFactors = F
, header = T)
str(my_df)
# extract atom list into a variable
# since in the list this corresponds to data frame, variable will be a df
d = my_pdb[[1]]
# make a copy: required for downstream sanity checks
d2 = d
# sanity checks: B factor
max(d$b); min(d$b)
par(oma = c(3,2,3,0)
, mar = c(1,3,5,2)
, mfrow = c(3,2))
#par(mfrow = c(3,2))
# 1: Original B-factor
hist(d$b
, xlab = ""
, main = "B-factor")
plot(density(d$b)
, xlab = ""
, main = "B-factor")
# 2: Pred Aff scores
hist(my_df$average_PredAffR
, xlab = ""
, main = "Norm_lig_average")
plot(density(my_df$average_PredAffR)
, xlab = ""
, main = "Norm_lig_average")
# 3: After the following replacement
#********************************
par(oma = c(3,2,3,0)
, mar = c(1,3,5,2)
, mfrow = c(3,2))
#par(mfrow = c(3,2))
# 1: Original B-factor
hist(d$b
, xlab = ""
, main = "B-factor")
plot(density(d$b)
, xlab = ""
, main = "B-factor")
# 2: Pred Aff scores
hist(my_df$average_PredAffR
, xlab = ""
, main = "Norm_lig_average")
plot(density(my_df$average_PredAffR)
, xlab = ""
, main = "Norm_lig_average")
# 3: After the following replacement
#********************************
#=========
# step 1_P2: BE BRAVE and replace in place now (don't run step 0)
#=========
# this makes all the B-factor values in the non-matched positions as NA
d$b = my_df$average_PredAffR[match(d$resno, my_df$Position)]
#=========
# step 2_P2
#=========
# count NA in Bfactor
b_na = sum(is.na(d$b)) ; b_na
# count number of 0's in Bactor
sum(d$b == 0)
# replace all NA in b factor with 0
d$b[is.na(d$b)] = 0
# sanity check: should be 0
sum(is.na(d$b))
if (sum(d$b == 0) == b_na){
print ("Sanity check passed: NA's replaced with 0's successfully")
} else {
print("Error: NA replacement NOT successful, Debug code!")
}
max(d$b); min(d$b)
# sanity checks: should be True
if (max(d$b) == max(my_df$average_PredAffR)){
print("Sanity check passed: B-factors replaced correctly")
} else {
print ("Error: Debug code please")
}
if (min(d$b) == min(my_df$average_PredAffR)){
print("Sanity check passed: B-factors replaced correctly")
} else {
print ("Error: Debug code please")
}
#=========
# step 3_P2
#=========
# sanity check: dim should be same before reassignment
# should be TRUE
dim(d) == dim(d2)
#=========
# step 4_P2
#=========
# assign it back to the pdb file
my_pdb[[1]] = d
max(d$b); min(d$b)
#=========
# step 5_P2
#=========
write.pdb(my_pdb, "Plotting/structure/complex1_BwithNormLIG.pdb")
# output dir
getwd()
# output dir
outDir = "~/git/Data/pyrazinamide/input/structure/"
outFile = paste0(outDir, "complex1_BwithNormLIG.pdb")
outFile = paste0(outDir, "complex1_BwithNormLIG.pdb"); outFile
write.pdb(my_pdb, outFile)