import commit
This commit is contained in:
commit
bccfe68192
39 changed files with 6837 additions and 0 deletions
512
mcsm_analysis/pyrazinamide/scripts/.Rhistory
Normal file
512
mcsm_analysis/pyrazinamide/scripts/.Rhistory
Normal file
|
|
@ -0,0 +1,512 @@
|
|||
###########################
|
||||
# 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)
|
||||
Loading…
Add table
Add a link
Reference in a new issue