import commit
This commit is contained in:
commit
bccfe68192
39 changed files with 6837 additions and 0 deletions
59
README.md
Normal file
59
README.md
Normal file
|
@ -0,0 +1,59 @@
|
||||||
|
mCSM Analysis
|
||||||
|
=============
|
||||||
|
|
||||||
|
This repo does mCSM analysis using Python (Pandas, numpy), bash and R.
|
||||||
|
|
||||||
|
Requires an additional 'Data' directory (Supplied separately). Batteries not included.
|
||||||
|
|
||||||
|
# directory struc
|
||||||
|
|
||||||
|
# Assumptions
|
||||||
|
|
||||||
|
1. git repos are cloned to `~/git`
|
||||||
|
2. The script
|
||||||
|
|
||||||
|
## LSHTM\_analysis: Repo
|
||||||
|
|
||||||
|
subdirs
|
||||||
|
|
||||||
|
needs to call `../Data/input/original/`
|
||||||
|
|
||||||
|
meta\_data\_analysis/
|
||||||
|
*.R
|
||||||
|
*.py
|
||||||
|
|
||||||
|
needs to call `../Data/input/processed/<drug>`
|
||||||
|
needs to output `../Data/output/results/<drug>`
|
||||||
|
|
||||||
|
mcsm\_analysis/
|
||||||
|
<drug>/ (generated by `meta_data_analysis/pnca_data_extraction.py`. To be replaced with command line args or config option "soon")
|
||||||
|
scripts/ (changed from `Scripts/`)
|
||||||
|
*.R
|
||||||
|
*.py
|
||||||
|
mcsm/
|
||||||
|
*.sh
|
||||||
|
*.py
|
||||||
|
*.R
|
||||||
|
plotting/
|
||||||
|
*.R
|
||||||
|
|
||||||
|
|
||||||
|
Data: Repo:
|
||||||
|
|
||||||
|
# subdirs
|
||||||
|
|
||||||
|
input/
|
||||||
|
original/
|
||||||
|
processed/
|
||||||
|
|
||||||
|
output/
|
||||||
|
<drug>/ (generated by `meta_data_analysis/pnca_data_extraction.py`. To be replaced with command line args or config option "soon")
|
||||||
|
results/
|
||||||
|
*.csv
|
||||||
|
*.xlsx
|
||||||
|
*.doc
|
||||||
|
*.txt
|
||||||
|
plots/
|
||||||
|
structure/
|
||||||
|
|
||||||
|
More docs here as I write them.
|
12
mcsm.conf
Normal file
12
mcsm.conf
Normal file
|
@ -0,0 +1,12 @@
|
||||||
|
# This is not yet used, but will be soon :)
|
||||||
|
[DEFAULT]
|
||||||
|
mcsm_home = /home/tanu/git/github/LSHTM_analysis
|
||||||
|
|
||||||
|
[pyrazinamide]
|
||||||
|
gene = 'pnca'
|
||||||
|
server = 'http://mcsm.melb.ac.uk/'
|
||||||
|
|
||||||
|
[monkeyamide]
|
||||||
|
gene = 'abcs'
|
||||||
|
server = 'http://myserver.local/'
|
||||||
|
|
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)
|
129
mcsm_analysis/pyrazinamide/scripts/Header_TT.R
Normal file
129
mcsm_analysis/pyrazinamide/scripts/Header_TT.R
Normal file
|
@ -0,0 +1,129 @@
|
||||||
|
#########################################################
|
||||||
|
### A) Installing and loading required packages
|
||||||
|
#########################################################
|
||||||
|
|
||||||
|
#if (!require("gplots")) {
|
||||||
|
# install.packages("gplots", dependencies = TRUE)
|
||||||
|
# library(gplots)
|
||||||
|
#}
|
||||||
|
|
||||||
|
if (!require("tidyverse")) {
|
||||||
|
install.packages("tidyverse", dependencies = TRUE)
|
||||||
|
library(tidyverse)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require("ggplot2")) {
|
||||||
|
install.packages("ggplot2", dependencies = TRUE)
|
||||||
|
library(ggplot2)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require("cowplot")) {
|
||||||
|
install.packages("copwplot", dependencies = TRUE)
|
||||||
|
library(ggplot2)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require("ggcorrplot")) {
|
||||||
|
install.packages("ggcorrplot", dependencies = TRUE)
|
||||||
|
library(ggcorrplot)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require("ggpubr")) {
|
||||||
|
install.packages("ggpubr", dependencies = TRUE)
|
||||||
|
library(ggpubr)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require("RColorBrewer")) {
|
||||||
|
install.packages("RColorBrewer", dependencies = TRUE)
|
||||||
|
library(RColorBrewer)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require ("GOplot")) {
|
||||||
|
install.packages("GOplot")
|
||||||
|
library(GOplot)
|
||||||
|
}
|
||||||
|
|
||||||
|
if(!require("VennDiagram")) {
|
||||||
|
|
||||||
|
install.packages("VennDiagram", dependencies = T)
|
||||||
|
library(VennDiagram)
|
||||||
|
}
|
||||||
|
|
||||||
|
if(!require("scales")) {
|
||||||
|
|
||||||
|
install.packages("scales", dependencies = T)
|
||||||
|
library(scales)
|
||||||
|
}
|
||||||
|
|
||||||
|
if(!require("plotrix")) {
|
||||||
|
|
||||||
|
install.packages("plotrix", dependencies = T)
|
||||||
|
library(plotrix)
|
||||||
|
}
|
||||||
|
|
||||||
|
if(!require("stats")) {
|
||||||
|
|
||||||
|
install.packages("stats", dependencies = T)
|
||||||
|
library(stats)
|
||||||
|
}
|
||||||
|
|
||||||
|
if(!require("stats4")) {
|
||||||
|
|
||||||
|
install.packages("stats4", dependencies = T)
|
||||||
|
library(stats4)
|
||||||
|
}
|
||||||
|
|
||||||
|
if(!require("data.table")) {
|
||||||
|
library(stats4)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require("PerformanceAnalytics")){
|
||||||
|
install.packages("PerformanceAnalytics", dependencies = T)
|
||||||
|
library(PerformaceAnalytics)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require ("GGally")){
|
||||||
|
install.packages("GGally")
|
||||||
|
library(GGally)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require ("corrr")){
|
||||||
|
install.packages("corrr")
|
||||||
|
library(corrr)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require ("psych")){
|
||||||
|
install.packages("psych")
|
||||||
|
library(psych)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require ("dplyr")){
|
||||||
|
install.packages("dplyr")
|
||||||
|
library(psych)
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
if (!require ("compare")){
|
||||||
|
install.packages("compare")
|
||||||
|
library(psych)
|
||||||
|
}
|
||||||
|
|
||||||
|
if (!require ("arsenal")){
|
||||||
|
install.packages("arsenal")
|
||||||
|
library(psych)
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
####TIDYVERSE
|
||||||
|
# Install
|
||||||
|
#if(!require(devtools)) install.packages("devtools")
|
||||||
|
#devtools::install_github("kassambara/ggcorrplot")
|
||||||
|
|
||||||
|
library(ggcorrplot)
|
||||||
|
|
||||||
|
|
||||||
|
###for PDB files
|
||||||
|
#install.packages("bio3d")
|
||||||
|
if(!require(bio3d)){
|
||||||
|
install.packages("bio3d")
|
||||||
|
library(bio3d)
|
||||||
|
}
|
27
mcsm_analysis/pyrazinamide/scripts/barplot_colour_function.R
Normal file
27
mcsm_analysis/pyrazinamide/scripts/barplot_colour_function.R
Normal file
|
@ -0,0 +1,27 @@
|
||||||
|
#########################################################
|
||||||
|
# 1b: Define function: coloured barplot by subgroup
|
||||||
|
# LINK: https://stackoverflow.com/questions/49818271/stacked-barplot-with-colour-gradients-for-each-bar
|
||||||
|
#########################################################
|
||||||
|
|
||||||
|
ColourPalleteMulti <- function(df, group, subgroup){
|
||||||
|
|
||||||
|
# Find how many colour categories to create and the number of colours in each
|
||||||
|
categories <- aggregate(as.formula(paste(subgroup, group, sep="~" ))
|
||||||
|
, df
|
||||||
|
, function(x) length(unique(x)))
|
||||||
|
# return(categories) }
|
||||||
|
|
||||||
|
category.start <- (scales::hue_pal(l = 100)(nrow(categories))) # Set the top of the colour pallete
|
||||||
|
|
||||||
|
category.end <- (scales::hue_pal(l = 40)(nrow(categories))) # set the bottom
|
||||||
|
|
||||||
|
#return(category.start); return(category.end)}
|
||||||
|
|
||||||
|
# Build Colour pallette
|
||||||
|
colours <- unlist(lapply(1:nrow(categories),
|
||||||
|
function(i){
|
||||||
|
colorRampPalette(colors = c(category.start[i]
|
||||||
|
, category.end[i]))(categories[i,2])}))
|
||||||
|
return(colours)
|
||||||
|
}
|
||||||
|
#########################################################
|
299
mcsm_analysis/pyrazinamide/scripts/combining_two_df.R
Normal file
299
mcsm_analysis/pyrazinamide/scripts/combining_two_df.R
Normal file
|
@ -0,0 +1,299 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
#########################################################
|
||||||
|
# TASK: To combine mcsm and meta data with af and or
|
||||||
|
#########################################################
|
||||||
|
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Installing and loading required packages #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("Header_TT.R")
|
||||||
|
#require(data.table)
|
||||||
|
#require(arsenal)
|
||||||
|
#require(compare)
|
||||||
|
#library(tidyverse)
|
||||||
|
|
||||||
|
#################################
|
||||||
|
# Read file: normalised file
|
||||||
|
# output of step 4 mcsm_pipeline
|
||||||
|
#################################
|
||||||
|
|
||||||
|
inDir = "~/git/Data/pyrazinamide/input/processed/"
|
||||||
|
inFile = paste0(inDir, "mcsm_complex1_normalised.csv"); inFile
|
||||||
|
|
||||||
|
mcsm_data = read.csv(inFile
|
||||||
|
, row.names = 1
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T)
|
||||||
|
rm(inDir, inFile)
|
||||||
|
|
||||||
|
str(mcsm_data)
|
||||||
|
|
||||||
|
table(mcsm_data$DUET_outcome); sum(table(mcsm_data$DUET_outcome) )
|
||||||
|
|
||||||
|
# spelling Correction 1: DUET
|
||||||
|
mcsm_data$DUET_outcome[mcsm_data$DUET_outcome=='Stabilizing'] <- 'Stabilising'
|
||||||
|
mcsm_data$DUET_outcome[mcsm_data$DUET_outcome=='Destabilizing'] <- 'Destabilising'
|
||||||
|
|
||||||
|
# checks: should be the same as above
|
||||||
|
table(mcsm_data$DUET_outcome); sum(table(mcsm_data$DUET_outcome) )
|
||||||
|
head(mcsm_data$DUET_outcome); tail(mcsm_data$DUET_outcome)
|
||||||
|
|
||||||
|
# spelling Correction 2: Ligand
|
||||||
|
table(mcsm_data$Lig_outcome); sum(table(mcsm_data$Lig_outcome) )
|
||||||
|
|
||||||
|
mcsm_data$Lig_outcome[mcsm_data$Lig_outcome=='Stabilizing'] <- 'Stabilising'
|
||||||
|
mcsm_data$Lig_outcome[mcsm_data$Lig_outcome=='Destabilizing'] <- 'Destabilising'
|
||||||
|
|
||||||
|
# checks: should be the same as above
|
||||||
|
table(mcsm_data$Lig_outcome); sum(table(mcsm_data$Lig_outcome) )
|
||||||
|
head(mcsm_data$Lig_outcome); tail(mcsm_data$Lig_outcome)
|
||||||
|
|
||||||
|
# count na in each column
|
||||||
|
na_count = sapply(mcsm_data, function(y) sum(length(which(is.na(y))))); na_count
|
||||||
|
|
||||||
|
# sort by Mutationinformation
|
||||||
|
mcsm_data = mcsm_data[order(mcsm_data$Mutationinformation),]
|
||||||
|
head(mcsm_data$Mutationinformation)
|
||||||
|
|
||||||
|
# get freq count of positions and add to the df
|
||||||
|
setDT(mcsm_data)[, occurrence := .N, by = .(Position)]
|
||||||
|
|
||||||
|
pos_count_check = data.frame(mcsm_data$Position, mcsm_data$occurrence)
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# 2: Read file: meta data with AFandOR
|
||||||
|
###########################
|
||||||
|
|
||||||
|
inDir = "~/git/Data/pyrazinamide/input/processed/"
|
||||||
|
inFile2 = paste0(inDir, "meta_data_with_AFandOR.csv"); inFile2
|
||||||
|
|
||||||
|
meta_with_afor <- read.csv(inFile2
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T)
|
||||||
|
|
||||||
|
rm(inDir, inFile2)
|
||||||
|
|
||||||
|
str(meta_with_afor)
|
||||||
|
|
||||||
|
# sort by Mutationinformation
|
||||||
|
head(meta_with_afor$Mutationinformation)
|
||||||
|
meta_with_afor = meta_with_afor[order(meta_with_afor$Mutationinformation),]
|
||||||
|
head(meta_with_afor$Mutationinformation)
|
||||||
|
|
||||||
|
# sanity check: should be True for all the mentioned columns
|
||||||
|
#is.numeric(meta_with_afor$OR)
|
||||||
|
na_var = c("AF", "OR", "pvalue", "logor", "neglog10pvalue")
|
||||||
|
|
||||||
|
c1 = NULL
|
||||||
|
for (i in na_var){
|
||||||
|
print(i)
|
||||||
|
c0 = is.numeric(meta_with_afor[,i])
|
||||||
|
c1 = c(c0, c1)
|
||||||
|
if ( all(c1) ){
|
||||||
|
print("Sanity check passed: These are all numeric cols")
|
||||||
|
} else{
|
||||||
|
print("Error: Please check your respective data types")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# If OR, and P value are not numeric, then convert to numeric and then count
|
||||||
|
# else they will say 0
|
||||||
|
na_count = sapply(meta_with_afor, function(y) sum(length(which(is.na(y))))); na_count
|
||||||
|
str(na_count)
|
||||||
|
|
||||||
|
# compare if the No of "NA" are the same for all these cols
|
||||||
|
na_len = NULL
|
||||||
|
for (i in na_var){
|
||||||
|
temp = na_count[[i]]
|
||||||
|
na_len = c(na_len, temp)
|
||||||
|
}
|
||||||
|
|
||||||
|
# extract how many NAs:
|
||||||
|
# should be all TRUE
|
||||||
|
# should be a single number since
|
||||||
|
# all the cols should have "equal" and "same" no. of NAs
|
||||||
|
|
||||||
|
my_nrows = NULL
|
||||||
|
for ( i in 1: (length(na_len)-1) ){
|
||||||
|
#print(compare(na_len[i]), na_len[i+1])
|
||||||
|
c = compare(na_len[i], na_len[i+1])
|
||||||
|
if ( c$result ) {
|
||||||
|
my_nrows = na_len[i] }
|
||||||
|
else {
|
||||||
|
print("Error: Please check your numbers")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
my_nrows
|
||||||
|
|
||||||
|
#=#=#=#=#=#=#=#=#
|
||||||
|
# COMMENT: AF, OR, pvalue, logor and neglog10pvalue
|
||||||
|
# these are the same 7 ones
|
||||||
|
#=#=#=#=#=#=#=#=#
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
#which(is.na(meta_with_afor$OR))
|
||||||
|
|
||||||
|
# initialise an empty df with nrows as extracted above
|
||||||
|
na_count_df = data.frame(matrix(vector(mode = 'numeric'
|
||||||
|
# , length = length(na_var)
|
||||||
|
)
|
||||||
|
, nrow = my_nrows
|
||||||
|
# , ncol = length(na_var)
|
||||||
|
))
|
||||||
|
|
||||||
|
# populate the df with the indices of the cols that are NA
|
||||||
|
for (i in na_var){
|
||||||
|
print(i)
|
||||||
|
na_i = which(is.na(meta_with_afor[i]))
|
||||||
|
na_count_df = cbind(na_count_df, na_i)
|
||||||
|
colnames(na_count_df)[which(na_var == i)] <- i
|
||||||
|
}
|
||||||
|
|
||||||
|
# Now compare these indices to ensure these are the same
|
||||||
|
c2 = NULL
|
||||||
|
for ( i in 1: ( length(na_count_df)-1 ) ) {
|
||||||
|
# print(na_count_df[i] == na_count_df[i+1])
|
||||||
|
c1 = identical(na_count_df[[i]], na_count_df[[i+1]])
|
||||||
|
c2 = c(c1, c2)
|
||||||
|
if ( all(c2) ) {
|
||||||
|
print("Sanity check passed: The indices for AF, OR, etc are all the same")
|
||||||
|
} else {
|
||||||
|
print ("Error: Please check indices which are NA")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
rm( c, c0, c1, c2, i, my_nrows
|
||||||
|
, na_count, na_i, na_len
|
||||||
|
, na_var, temp
|
||||||
|
, na_count_df
|
||||||
|
, pos_count_check )
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# 3:merging two dfs: with NA
|
||||||
|
###########################
|
||||||
|
|
||||||
|
# link col name = Mutationinforamtion
|
||||||
|
head(mcsm_data$Mutationinformation)
|
||||||
|
head(meta_with_afor$Mutationinformation)
|
||||||
|
|
||||||
|
#########
|
||||||
|
# merge 1a: meta data with mcsm
|
||||||
|
#########
|
||||||
|
merged_df2 = merge(x = meta_with_afor
|
||||||
|
,y = mcsm_data
|
||||||
|
, by = "Mutationinformation"
|
||||||
|
, all.y = T)
|
||||||
|
|
||||||
|
head(merged_df2$Position)
|
||||||
|
|
||||||
|
# sort by Position
|
||||||
|
head(merged_df2$Position)
|
||||||
|
merged_df2 = merged_df2[order(merged_df2$Position),]
|
||||||
|
head(merged_df2$Position)
|
||||||
|
|
||||||
|
merged_df2v2 = merge(x = meta_with_afor
|
||||||
|
,y = mcsm_data
|
||||||
|
, by = "Mutationinformation"
|
||||||
|
, all.x = T)
|
||||||
|
#!=!=!=!=!=!=!=!
|
||||||
|
# COMMENT: used all.y since position 186 is not part of the struc,
|
||||||
|
# hence doesn't have a mcsm value
|
||||||
|
# but 186 is associated with with mutation
|
||||||
|
#!=!=!=!=!=!=!=!
|
||||||
|
|
||||||
|
# should be False
|
||||||
|
identical(merged_df2, merged_df2v2)
|
||||||
|
table(merged_df2$Position%in%merged_df2v2$Position)
|
||||||
|
|
||||||
|
rm(merged_df2v2)
|
||||||
|
|
||||||
|
#########
|
||||||
|
# merge 1b:remove duplicate mutation information
|
||||||
|
#########
|
||||||
|
|
||||||
|
#==#=#=#=#=#=#
|
||||||
|
# Cannot trust lineage, country from this df as the same mutation
|
||||||
|
# can have many different lineages
|
||||||
|
# but this should be good for the numerical corr plots
|
||||||
|
#=#=#=#=#=#=#=
|
||||||
|
merged_df3 = merged_df2[!duplicated(merged_df2$Mutationinformation),]
|
||||||
|
head(merged_df3$Position); tail(merged_df3$Position) # should be sorted
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
# nrows of merged_df3 should be the same as the nrows of mcsm_data
|
||||||
|
if(nrow(mcsm_data) == nrow(merged_df3)){
|
||||||
|
print("sanity check: Passed")
|
||||||
|
} else {
|
||||||
|
print("Error!: check data, nrows is not as expected")
|
||||||
|
}
|
||||||
|
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
# uncomment as necessary
|
||||||
|
# only need to run this if merged_df2v2 i.e non structural pos included
|
||||||
|
#mcsm = mcsm_data$Mutationinformation
|
||||||
|
#my_merged = merged_df3$Mutationinformation
|
||||||
|
|
||||||
|
# find the index where it differs
|
||||||
|
#diff_n = which(!my_merged%in%mcsm)
|
||||||
|
|
||||||
|
#check if it is indeed pos 186
|
||||||
|
#merged_df3[diff_n,]
|
||||||
|
|
||||||
|
# remove this entry
|
||||||
|
#merged_df3 = merged_df3[-diff_n,]]
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# 3b :merging two dfs: without NA
|
||||||
|
###########################
|
||||||
|
|
||||||
|
#########
|
||||||
|
# merge 2a:same as merge 1 but excluding NA
|
||||||
|
#########
|
||||||
|
merged_df2_comp = merged_df2[!is.na(merged_df2$AF),]
|
||||||
|
|
||||||
|
#########
|
||||||
|
# merge 2b: remove duplicate mutation information
|
||||||
|
#########
|
||||||
|
merged_df3_comp = merged_df2_comp[!duplicated(merged_df2_comp$Mutationinformation),]
|
||||||
|
|
||||||
|
# alternate way of deriving merged_df3_comp
|
||||||
|
foo = merged_df3[!is.na(merged_df3$AF),]
|
||||||
|
# compare dfs: foo and merged_df3_com
|
||||||
|
all.equal(foo, merged_df3)
|
||||||
|
|
||||||
|
summary(comparedf(foo, merged_df3))
|
||||||
|
|
||||||
|
#=============== end of combining df
|
||||||
|
#clear variables
|
||||||
|
rm(mcsm_data
|
||||||
|
, meta_with_afor
|
||||||
|
, foo)
|
||||||
|
|
||||||
|
#rm(diff_n, my_merged, mcsm)
|
||||||
|
|
||||||
|
#=====================
|
||||||
|
# write_output files
|
||||||
|
#=====================
|
||||||
|
# output dir
|
||||||
|
outDir = "~/git/Data/pyrazinamide/output/"
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
outFile1 = paste0(outDir, "merged_df3.csv"); outFile1
|
||||||
|
write.csv(merged_df3, outFile1)
|
||||||
|
|
||||||
|
#outFile2 = paste0(outDir, "merged_df3_comp.csv"); outFile2
|
||||||
|
#write.csv(merged_df3_comp, outFile2)
|
||||||
|
|
||||||
|
rm(outDir
|
||||||
|
, outFile1
|
||||||
|
# , outFile2
|
||||||
|
)
|
||||||
|
#============================= end of script
|
||||||
|
|
348
mcsm_analysis/pyrazinamide/scripts/combining_two_df_lig.R
Normal file
348
mcsm_analysis/pyrazinamide/scripts/combining_two_df_lig.R
Normal file
|
@ -0,0 +1,348 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
#########################################################
|
||||||
|
# TASK: To combine mcsm and meta data with af and or
|
||||||
|
# by filtering for distance to ligand (<10Ang)
|
||||||
|
#########################################################
|
||||||
|
|
||||||
|
#########################################################
|
||||||
|
# Installing and loading required packages
|
||||||
|
#########################################################
|
||||||
|
|
||||||
|
#source("Header_TT.R")
|
||||||
|
#require(data.table)
|
||||||
|
#require(arsenal)
|
||||||
|
#require(compare)
|
||||||
|
#library(tidyverse)
|
||||||
|
|
||||||
|
#################################
|
||||||
|
# Read file: normalised file
|
||||||
|
# output of step 4 mcsm_pipeline
|
||||||
|
#################################
|
||||||
|
|
||||||
|
inDir = "~/git/Data/pyrazinamide/input/processed/"
|
||||||
|
inFile = paste0(inDir, "mcsm_complex1_normalised.csv"); inFile
|
||||||
|
|
||||||
|
mcsm_data = read.csv(inFile
|
||||||
|
, row.names = 1
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T)
|
||||||
|
rm(inDir, inFile)
|
||||||
|
|
||||||
|
str(mcsm_data)
|
||||||
|
|
||||||
|
table(mcsm_data$DUET_outcome); sum(table(mcsm_data$DUET_outcome) )
|
||||||
|
|
||||||
|
# spelling Correction 1: DUET
|
||||||
|
mcsm_data$DUET_outcome[mcsm_data$DUET_outcome=='Stabilizing'] <- 'Stabilising'
|
||||||
|
mcsm_data$DUET_outcome[mcsm_data$DUET_outcome=='Destabilizing'] <- 'Destabilising'
|
||||||
|
|
||||||
|
# checks
|
||||||
|
table(mcsm_data$DUET_outcome); sum(table(mcsm_data$DUET_outcome) )
|
||||||
|
head(mcsm_data$DUET_outcome); tail(mcsm_data$DUET_outcome)
|
||||||
|
|
||||||
|
# spelling Correction 2: Ligand
|
||||||
|
table(mcsm_data$Lig_outcome); sum(table(mcsm_data$Lig_outcome) )
|
||||||
|
|
||||||
|
mcsm_data$Lig_outcome[mcsm_data$Lig_outcome=='Stabilizing'] <- 'Stabilising'
|
||||||
|
mcsm_data$Lig_outcome[mcsm_data$Lig_outcome=='Destabilizing'] <- 'Destabilising'
|
||||||
|
|
||||||
|
# checks: should be the same as above
|
||||||
|
table(mcsm_data$Lig_outcome); sum(table(mcsm_data$Lig_outcome) )
|
||||||
|
head(mcsm_data$Lig_outcome); tail(mcsm_data$Lig_outcome)
|
||||||
|
|
||||||
|
########################### !!! only for mcsm_lig
|
||||||
|
# 4: Filter/subset data
|
||||||
|
# Lig plots < 10Ang
|
||||||
|
# Filter the lig plots for Dis_to_lig < 10Ang
|
||||||
|
###########################
|
||||||
|
|
||||||
|
# check range of distances
|
||||||
|
max(mcsm_data$Dis_lig_Ang)
|
||||||
|
min(mcsm_data$Dis_lig_Ang)
|
||||||
|
|
||||||
|
# count
|
||||||
|
table(mcsm_data$Dis_lig_Ang<10)
|
||||||
|
|
||||||
|
# subset data to have only values less than 10 Ang
|
||||||
|
mcsm_data2 = subset(mcsm_data, mcsm_data$Dis_lig_Ang < 10)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
max(mcsm_data2$Dis_lig_Ang)
|
||||||
|
min(mcsm_data2$Dis_lig_Ang)
|
||||||
|
|
||||||
|
# count no of unique positions
|
||||||
|
length(unique(mcsm_data2$Position))
|
||||||
|
|
||||||
|
# count no of unique mutations
|
||||||
|
length(unique(mcsm_data2$Mutationinformation))
|
||||||
|
|
||||||
|
# count Destabilisinga and stabilising
|
||||||
|
table(mcsm_data2$Lig_outcome) #{RESULT: no of mutations within 10Ang}
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT: so as not to alter the script
|
||||||
|
mcsm_data = mcsm_data2
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
#############################
|
||||||
|
# Extra sanity check:
|
||||||
|
# for mcsm_lig ONLY
|
||||||
|
# Dis_lig_Ang should be <10
|
||||||
|
#############################
|
||||||
|
|
||||||
|
if (max(mcsm_data$Dis_lig_Ang) < 10){
|
||||||
|
print ("Sanity check passed: lig data is <10Ang")
|
||||||
|
}else{
|
||||||
|
print ("Error: data should be filtered to be within 10Ang")
|
||||||
|
}
|
||||||
|
|
||||||
|
# clear variables
|
||||||
|
rm(mcsm_data2)
|
||||||
|
|
||||||
|
# count na in each column
|
||||||
|
na_count = sapply(mcsm_data, function(y) sum(length(which(is.na(y))))); na_count
|
||||||
|
|
||||||
|
head(mcsm_data$Mutationinformation)
|
||||||
|
mcsm_data[mcsm_data$Mutationinformation=="Q10P",]
|
||||||
|
mcsm_data[mcsm_data$Mutationinformation=="L4S",]
|
||||||
|
|
||||||
|
# sort by Mutationinformation
|
||||||
|
mcsm_data = mcsm_data[order(mcsm_data$Mutationinformation),]
|
||||||
|
head(mcsm_data$Mutationinformation)
|
||||||
|
|
||||||
|
# check
|
||||||
|
mcsm_data[grep("Q10P", mcsm_data$Mutationinformation),]
|
||||||
|
mcsm_data[grep("A102T", mcsm_data$Mutationinformation),]
|
||||||
|
|
||||||
|
# get freq count of positions and add to the df
|
||||||
|
setDT(mcsm_data)[, occurrence := .N, by = .(Position)]
|
||||||
|
|
||||||
|
pos_count_check = data.frame(mcsm_data$Position, mcsm_data$occurrence)
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# 2: Read file: meta data with AFandOR
|
||||||
|
###########################
|
||||||
|
|
||||||
|
inDir = "~/git/Data/pyrazinamide/input/processed/"
|
||||||
|
inFile2 = paste0(inDir, "meta_data_with_AFandOR.csv"); inFile2
|
||||||
|
|
||||||
|
meta_with_afor <- read.csv(inFile2
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T)
|
||||||
|
|
||||||
|
str(meta_with_afor)
|
||||||
|
|
||||||
|
# sort by Mutationinformation
|
||||||
|
head(meta_with_afor$Mutationinformation)
|
||||||
|
meta_with_afor = meta_with_afor[order(meta_with_afor$Mutationinformation),]
|
||||||
|
head(meta_with_afor$Mutationinformation)
|
||||||
|
|
||||||
|
# sanity check: should be True for all the mentioned columns
|
||||||
|
#is.numeric(meta_with_afor$OR)
|
||||||
|
na_var = c("AF", "OR", "pvalue", "logor", "neglog10pvalue")
|
||||||
|
|
||||||
|
c1 = NULL
|
||||||
|
for (i in na_var){
|
||||||
|
print(i)
|
||||||
|
c0 = is.numeric(meta_with_afor[,i])
|
||||||
|
c1 = c(c0, c1)
|
||||||
|
if ( all(c1) ){
|
||||||
|
print("Sanity check passed: These are all numeric cols")
|
||||||
|
} else{
|
||||||
|
print("Error: Please check your respective data types")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
# If OR, and P value are not numeric, then convert to numeric and then count
|
||||||
|
# else they will say 0
|
||||||
|
|
||||||
|
# NOW count na in each column: if you did it before, then
|
||||||
|
# OR and Pvalue column would say 0 na since these were not numeric
|
||||||
|
na_count = sapply(meta_with_afor, function(y) sum(length(which(is.na(y))))); na_count
|
||||||
|
str(na_count)
|
||||||
|
|
||||||
|
# compare if the No of "NA" are the same for all these cols
|
||||||
|
na_len = NULL
|
||||||
|
na_var = c("AF", "OR", "pvalue", "logor", "neglog10pvalue")
|
||||||
|
for (i in na_var){
|
||||||
|
temp = na_count[[i]]
|
||||||
|
na_len = c(na_len, temp)
|
||||||
|
}
|
||||||
|
|
||||||
|
my_nrows = NULL
|
||||||
|
|
||||||
|
for ( i in 1: (length(na_len)-1) ){
|
||||||
|
#print(compare(na_len[i]), na_len[i+1])
|
||||||
|
c = compare(na_len[i], na_len[i+1])
|
||||||
|
if ( c$result ) {
|
||||||
|
my_nrows = na_len[i] }
|
||||||
|
else {
|
||||||
|
print("Error: Please check your numbers")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
my_nrows
|
||||||
|
|
||||||
|
#=#=#=#=#=#=#=#=#
|
||||||
|
# COMMENT: AF, OR, pvalue, logor and neglog10pvalue
|
||||||
|
# all have 81 NA, with pyrazinamide with 960
|
||||||
|
# and these are the same 7 ones
|
||||||
|
#=#=#=#=#=#=#=#=#
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
#which(is.na(meta_with_afor$OR))
|
||||||
|
|
||||||
|
# initialise an empty df with nrows as extracted above
|
||||||
|
na_count_df = data.frame(matrix(vector(mode = 'numeric'
|
||||||
|
# , length = length(na_var)
|
||||||
|
)
|
||||||
|
, nrow = my_nrows
|
||||||
|
# , ncol = length(na_var)
|
||||||
|
))
|
||||||
|
|
||||||
|
# populate the df with the indices of the cols that are NA
|
||||||
|
for (i in na_var){
|
||||||
|
print(i)
|
||||||
|
na_i = which(is.na(meta_with_afor[i]))
|
||||||
|
na_count_df = cbind(na_count_df, na_i)
|
||||||
|
colnames(na_count_df)[which(na_var == i)] <- i
|
||||||
|
}
|
||||||
|
|
||||||
|
# Now compare these indices to ensure these are the same
|
||||||
|
c2 = NULL
|
||||||
|
for ( i in 1: ( length(na_count_df)-1 ) ) {
|
||||||
|
# print(na_count_df[i] == na_count_df[i+1])
|
||||||
|
c1 = identical(na_count_df[[i]], na_count_df[[i+1]])
|
||||||
|
c2 = c(c1, c2)
|
||||||
|
if ( all(c2) ) {
|
||||||
|
print("Sanity check passed: The indices for AF, OR, etc are all the same")
|
||||||
|
} else {
|
||||||
|
print ("Error: Please check indices which are NA")
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
rm( c, c1, c2, i, my_nrows
|
||||||
|
, na_count, na_i, na_len
|
||||||
|
, na_var, temp
|
||||||
|
, na_count_df
|
||||||
|
, pos_count_check )
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# 3:merging two dfs: with NA
|
||||||
|
###########################
|
||||||
|
|
||||||
|
# link col name = Mutationinforamtion
|
||||||
|
head(mcsm_data$Mutationinformation)
|
||||||
|
head(meta_with_afor$Mutationinformation)
|
||||||
|
|
||||||
|
#########
|
||||||
|
# merge 1a: meta data with mcsm
|
||||||
|
#########
|
||||||
|
merged_df2 = merge(x = meta_with_afor
|
||||||
|
, y = mcsm_data
|
||||||
|
, by = "Mutationinformation"
|
||||||
|
, all.y = T)
|
||||||
|
|
||||||
|
head(merged_df2$Position)
|
||||||
|
|
||||||
|
# sort by Position
|
||||||
|
head(merged_df2$Position)
|
||||||
|
merged_df2 = merged_df2[order(merged_df2$Position),]
|
||||||
|
head(merged_df2$Position)
|
||||||
|
|
||||||
|
merged_df2v2 = merge(x = meta_with_afor
|
||||||
|
,y = mcsm_data
|
||||||
|
, by = "Mutationinformation"
|
||||||
|
, all.x = T)
|
||||||
|
|
||||||
|
#!=!=!=!=!=!=!=!
|
||||||
|
# COMMENT: used all.y since position 186 is not part of the struc,
|
||||||
|
# hence doesn't have a mcsm value
|
||||||
|
# but 186 is associated with with mutation
|
||||||
|
#!=!=!=!=!=!=!=!
|
||||||
|
|
||||||
|
# should be False
|
||||||
|
identical(merged_df2, merged_df2v2)
|
||||||
|
table(merged_df2$Position%in%merged_df2v2$Position)
|
||||||
|
|
||||||
|
rm(merged_df2v2)
|
||||||
|
|
||||||
|
#########
|
||||||
|
# merge 1b:remove duplicate mutation information
|
||||||
|
#########
|
||||||
|
|
||||||
|
#==#=#=#=#=#=#
|
||||||
|
# Cannot trust lineage, country from this df as the same mutation
|
||||||
|
# can have many different lineages
|
||||||
|
# but this should be good for the numerical corr plots
|
||||||
|
#=#=#=#=#=#=#=
|
||||||
|
merged_df3 = merged_df2[!duplicated(merged_df2$Mutationinformation),]
|
||||||
|
head(merged_df3$Position) ; tail(merged_df3$Position) # should be sorted
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
# nrows of merged_df3 should be the same as the nrows of mcsm_data
|
||||||
|
if(nrow(mcsm_data) == nrow(merged_df3)){
|
||||||
|
print("sanity check: Passed")
|
||||||
|
} else {
|
||||||
|
print("Error!: check data, nrows is not as expected")
|
||||||
|
}
|
||||||
|
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
# uncomment as necessary
|
||||||
|
# only need to run this if merged_df2v2 i.e non structural pos included
|
||||||
|
#mcsm = mcsm_data$Mutationinformation
|
||||||
|
#my_merged = merged_df3$Mutationinformation
|
||||||
|
|
||||||
|
# find the index where it differs
|
||||||
|
#diff_n = which(!my_merged%in%mcsm)
|
||||||
|
|
||||||
|
#check if it is indeed pos 186
|
||||||
|
#merged_df3[diff_n,]
|
||||||
|
|
||||||
|
# remove this entry
|
||||||
|
#merged_df3 = merged_df3[-diff_n,]
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# 3b :merging two dfs: without NA
|
||||||
|
###########################
|
||||||
|
|
||||||
|
#########
|
||||||
|
# merge 2a:same as merge 1 but excluding NA
|
||||||
|
#########
|
||||||
|
merged_df2_comp = merged_df2[!is.na(merged_df2$AF),]
|
||||||
|
|
||||||
|
#########
|
||||||
|
# merge 2b: remove duplicate mutation information
|
||||||
|
#########
|
||||||
|
merged_df3_comp = merged_df2_comp[!duplicated(merged_df2_comp$Mutationinformation),]
|
||||||
|
|
||||||
|
# FIXME: add this as a sanity check. I have manually checked!
|
||||||
|
|
||||||
|
# alternate way of deriving merged_df3_comp
|
||||||
|
foo = merged_df3[!is.na(merged_df3$AF),]
|
||||||
|
|
||||||
|
# compare dfs: foo and merged_df3_com
|
||||||
|
all.equal(foo, merged_df3)
|
||||||
|
|
||||||
|
summary(comparedf(foo, merged_df3))
|
||||||
|
|
||||||
|
#=============== end of combining df
|
||||||
|
#clear variables
|
||||||
|
rm(mcsm_data
|
||||||
|
, meta_with_afor
|
||||||
|
, foo)
|
||||||
|
|
||||||
|
#rm(diff_n, my_merged, mcsm)
|
||||||
|
|
||||||
|
#===============end of script
|
||||||
|
|
||||||
|
#=====================
|
||||||
|
# write_output files
|
||||||
|
#=====================
|
||||||
|
|
||||||
|
# Not required as this is a subset of the "combining_two_df.R" script
|
||||||
|
|
25
mcsm_analysis/pyrazinamide/scripts/mcsm/step0_check_duplicate_SNPs.sh
Executable file
25
mcsm_analysis/pyrazinamide/scripts/mcsm/step0_check_duplicate_SNPs.sh
Executable file
|
@ -0,0 +1,25 @@
|
||||||
|
#!/bin/bash
|
||||||
|
|
||||||
|
#*************************************
|
||||||
|
# need to be in the correct directory
|
||||||
|
#*************************************
|
||||||
|
##: comments for code
|
||||||
|
#: commented out code
|
||||||
|
|
||||||
|
#**********************************************************************
|
||||||
|
# TASK: Text file containing a list of SNPs; SNP in the format(C2E)
|
||||||
|
# per line. Sort by unique, which automatically removes duplicates.
|
||||||
|
# sace file in current directory
|
||||||
|
#**********************************************************************
|
||||||
|
infile="${HOME}/git/Data/input/processed/pyrazinamide/pnca_mis_SNPs_v2.csv"
|
||||||
|
outfile="${HOME}/git/Data/input/processed/pyrazinamide/pnca_mis_SNPs_v2_unique.csv"
|
||||||
|
|
||||||
|
# sort unique entries and output to current directory
|
||||||
|
sort -u ${infile} > ${outfile}
|
||||||
|
|
||||||
|
# count no. of unique snps mCSM will run on
|
||||||
|
count=$(wc -l < ${outfile})
|
||||||
|
|
||||||
|
# print to console no. of unique snps mCSM will run on
|
||||||
|
echo "${count} unique mutations for mCSM to run on"
|
||||||
|
|
|
@ -0,0 +1,72 @@
|
||||||
|
#!/bin/bash
|
||||||
|
|
||||||
|
#*************************************
|
||||||
|
#need to be in the correct directory
|
||||||
|
#*************************************
|
||||||
|
##: comments for code
|
||||||
|
#: commented out code
|
||||||
|
|
||||||
|
#**********************************************************************
|
||||||
|
# TASK: submit requests using curl: HANDLE redirects and refresh url.
|
||||||
|
# Iterate over mutation file and write/append result urls to a file
|
||||||
|
# result url file: stored in the /Results directory
|
||||||
|
# mutation file: one mutation per line, no chain ID
|
||||||
|
# output: in a file, should be n urls (n=no. of mutations in file)
|
||||||
|
# NOTE: these are just result urls, not actual values for results
|
||||||
|
#**********************************************************************
|
||||||
|
## iterate over mutation file; line by line and submit query using curl
|
||||||
|
filename="../Data/pnca_mis_SNPs_v2_unique.csv"
|
||||||
|
|
||||||
|
## some useful messages
|
||||||
|
echo -n -e "Processing $(wc -l < ${filename}) entries from ${filename}\n"
|
||||||
|
COUNT=0
|
||||||
|
while read -r line; do
|
||||||
|
((COUNT++))
|
||||||
|
mutation="${line}"
|
||||||
|
# echo "${mutation}"
|
||||||
|
pdb='../Data/complex1_no_water.pdb'
|
||||||
|
mutation="${mutation}"
|
||||||
|
chain="A"
|
||||||
|
lig_id="PZA"
|
||||||
|
affin_wt="0.99"
|
||||||
|
host="http://biosig.unimelb.edu.au"
|
||||||
|
call_url="/mcsm_lig/prediction"
|
||||||
|
|
||||||
|
##=========================================
|
||||||
|
##html field_names names required for curl
|
||||||
|
##complex_field:wild=@
|
||||||
|
##mutation_field:mutation=@
|
||||||
|
##chain_field:chain=@
|
||||||
|
##ligand_field:lig_id@
|
||||||
|
##energy_field:affin_wt
|
||||||
|
#=========================================
|
||||||
|
refresh_url=$(curl -L \
|
||||||
|
-sS \
|
||||||
|
-F "wild=@${pdb}" \
|
||||||
|
-F "mutation=${mutation}" \
|
||||||
|
-F "chain=${chain}" \
|
||||||
|
-F "lig_id=${lig_id}" \
|
||||||
|
-F "affin_wt=${affin_wt}" \
|
||||||
|
${host}${call_url} | grep "http-equiv")
|
||||||
|
|
||||||
|
#echo $refresh_url
|
||||||
|
#echo ${host}${refresh_url}
|
||||||
|
|
||||||
|
#use regex to extract the relevant bit from the refresh url
|
||||||
|
#regex:sed -r 's/.*(\/mcsm.*)".*$/\1/g'
|
||||||
|
|
||||||
|
#Now build: result url using host and refresh url and write the urls to a file in the Results dir
|
||||||
|
result_url=$(echo $refresh_url | sed -r 's/.*(\/mcsm.*)".*$/\1/g')
|
||||||
|
sleep 10
|
||||||
|
|
||||||
|
echo -e "${mutation} : processing entry ${COUNT}/$(wc -l < ${filename})..."
|
||||||
|
|
||||||
|
echo -e "${host}${result_url}" >> ../Results/$(wc -l < ${filename})_mCSM_lig_complex1_result_url.txt
|
||||||
|
#echo -n '.'
|
||||||
|
done < "${filename}"
|
||||||
|
|
||||||
|
echo
|
||||||
|
echo "Processing Complete"
|
||||||
|
|
||||||
|
##end of submitting query, receiving result url and storing results url in a file
|
||||||
|
|
|
@ -0,0 +1,59 @@
|
||||||
|
#!/bin/bash
|
||||||
|
#*************************************
|
||||||
|
#need to be in the correct directory
|
||||||
|
#*************************************
|
||||||
|
##: comments for code
|
||||||
|
#: commented out code
|
||||||
|
|
||||||
|
#********************************************************************
|
||||||
|
# TASK: submit result urls and fetch actual results using curl
|
||||||
|
# iterate over each result url from the output of step1 in the stored
|
||||||
|
# in file in /Results.
|
||||||
|
# Use curl to fetch results and extract relevant sections using hxtools
|
||||||
|
# and store these in another file in /Results
|
||||||
|
# This script takes two arguments:
|
||||||
|
# input file: file containing results url
|
||||||
|
# In this case: 336_mCSM_lig_complex1_result_url.txt
|
||||||
|
# output file: name of the file where extracted results will be stored
|
||||||
|
# In this case : it is 336_mCSM_lig_complex1_output_MASTER.txt
|
||||||
|
#*********************************************************************
|
||||||
|
|
||||||
|
#if [ "$#" -ne 2 ]; then
|
||||||
|
#if [ -Z $1 ]; then
|
||||||
|
# echo "
|
||||||
|
# Please provide both Input and Output files.
|
||||||
|
|
||||||
|
# Usage: batch_read_urls.sh INFILE OUTFILE
|
||||||
|
# "
|
||||||
|
# exit 1
|
||||||
|
#fi
|
||||||
|
|
||||||
|
# First argument: Input File
|
||||||
|
# Second argument: Output File
|
||||||
|
#infile=$1
|
||||||
|
#outfile=$2
|
||||||
|
|
||||||
|
infile="${HOME}/git/LSHTM_analysis/mcsm_complex1/Results/336_mCSM_lig_complex1_result_url.txt"
|
||||||
|
outfile="${HOME}/git/LSHTM_analysis/mcsm_complex1/Results/336_mCSM_lig_complex1_output_MASTER.txt"
|
||||||
|
|
||||||
|
echo -n "Processing $(wc -l < ${infile}) entries from ${infile}"
|
||||||
|
echo
|
||||||
|
COUNT=0
|
||||||
|
while read -r line; do
|
||||||
|
#COUNT=$(($COUNT+1))
|
||||||
|
((COUNT++))
|
||||||
|
curl --silent ${line} \
|
||||||
|
| hxnormalize -x \
|
||||||
|
| hxselect -c div.span4 \
|
||||||
|
| hxselect -c div.well \
|
||||||
|
| sed -r -e 's/<[^>]*>//g' \
|
||||||
|
| sed -re 's/ +//g' \
|
||||||
|
>> ${outfile}
|
||||||
|
#| tee -a ${outfile}
|
||||||
|
# echo -n '.'
|
||||||
|
echo -e "Processing entry ${COUNT}/$(wc -l < ${infile})..."
|
||||||
|
|
||||||
|
done < "${infile}"
|
||||||
|
|
||||||
|
echo
|
||||||
|
echo "Processing Complete"
|
|
@ -0,0 +1,52 @@
|
||||||
|
#!/bin/bash
|
||||||
|
#*************************************
|
||||||
|
#need to be in the correct directory
|
||||||
|
#*************************************
|
||||||
|
##: comments for code
|
||||||
|
#: commented out code
|
||||||
|
|
||||||
|
#********************************************************************
|
||||||
|
# TASK: Intermediate results processing
|
||||||
|
# output file has a convenient delimiter of ":" that can be used to
|
||||||
|
# format the file into two columns (col1: field_desc and col2: values)
|
||||||
|
# However the section "PredictedAffinityChange:...." and
|
||||||
|
# "DUETstabilitychange:.." are split over multiple lines and
|
||||||
|
# prevent this from happening.Additionally there are other empty lines
|
||||||
|
# that need to be omiited. In order ensure these sections are not split
|
||||||
|
# over multiple lines, this script is written.
|
||||||
|
#*********************************************************************
|
||||||
|
|
||||||
|
infile="../Results/336_mCSM_lig_complex1_output_processed.txt"
|
||||||
|
|
||||||
|
#sed -i '/PredictedAffinityChange:/ { N; N; N; N; s/\n//g;}' ${infile} \
|
||||||
|
# | sed -i '/DUETstabilitychange:/ {x; N; N; s/\n//g; p;d;}' ${infile}
|
||||||
|
|
||||||
|
# Outputs records separated by a newline, that look something like this:
|
||||||
|
# PredictedAffinityChange:-2.2log(affinityfoldchange)-Destabilizing
|
||||||
|
# Mutationinformation:
|
||||||
|
# Wild-type:L
|
||||||
|
# Position:4
|
||||||
|
# Mutant-type:W
|
||||||
|
# Chain:A
|
||||||
|
# LigandID:PZA
|
||||||
|
# Distancetoligand:15.911Å
|
||||||
|
# DUETstabilitychange:-2.169Kcal/mol
|
||||||
|
#
|
||||||
|
# PredictedAffinityChange:-1.538log(affinityfoldchange)-Destabilizing
|
||||||
|
# (...etc)
|
||||||
|
|
||||||
|
# This script brings everything in a convenient format for further processing in python.
|
||||||
|
# bear in mind, this replaces the file in place, so make sure you retain a copy for your records
|
||||||
|
sed -i '/PredictedAffinityChange/ {
|
||||||
|
N
|
||||||
|
N
|
||||||
|
N
|
||||||
|
N
|
||||||
|
s/\n//g
|
||||||
|
}
|
||||||
|
/DUETstabilitychange:/ {
|
||||||
|
N
|
||||||
|
N
|
||||||
|
s/\n//g
|
||||||
|
}
|
||||||
|
/^$/d' ${infile}
|
29
mcsm_analysis/pyrazinamide/scripts/mcsm/step3b_format_results.py
Executable file
29
mcsm_analysis/pyrazinamide/scripts/mcsm/step3b_format_results.py
Executable file
|
@ -0,0 +1,29 @@
|
||||||
|
#!/usr/bin/python
|
||||||
|
import pandas as pd
|
||||||
|
from collections import defaultdict
|
||||||
|
|
||||||
|
#file = r'../Results/322_mCSM_lig_complex1_output_processed.txt'
|
||||||
|
|
||||||
|
outCols=[
|
||||||
|
'PredictedAffinityChange',
|
||||||
|
'Mutationinformation',
|
||||||
|
'Wild-type',
|
||||||
|
'Position',
|
||||||
|
'Mutant-type',
|
||||||
|
'Chain',
|
||||||
|
'LigandID',
|
||||||
|
'Distancetoligand',
|
||||||
|
'DUETstabilitychange'
|
||||||
|
]
|
||||||
|
|
||||||
|
lines = [line.rstrip('\n') for line in open('../Results/336_mCSM_lig_complex1_output_processed.txt')]
|
||||||
|
|
||||||
|
outputs = defaultdict(list)
|
||||||
|
|
||||||
|
for item in lines:
|
||||||
|
col, val = item.split(':')
|
||||||
|
outputs[col].append(val)
|
||||||
|
|
||||||
|
dfOut=pd.DataFrame(outputs)
|
||||||
|
|
||||||
|
pd.DataFrame.to_csv(dfOut,'../Results/336_complex1_formatted_results.csv', columns=outCols)
|
207
mcsm_analysis/pyrazinamide/scripts/mcsm/step3c_data_cleaning.R
Normal file
207
mcsm_analysis/pyrazinamide/scripts/mcsm/step3c_data_cleaning.R
Normal file
|
@ -0,0 +1,207 @@
|
||||||
|
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$Distancetoligand = gsub("Å"
|
||||||
|
, ""
|
||||||
|
, df$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
|
252
mcsm_analysis/pyrazinamide/scripts/mcsm/step4_normalise.R
Normal file
252
mcsm_analysis/pyrazinamide/scripts/mcsm/step4_normalise.R
Normal file
|
@ -0,0 +1,252 @@
|
||||||
|
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:read cleaned data and perform rescaling
|
||||||
|
# of DUET stability scores
|
||||||
|
# of Pred affinity
|
||||||
|
#compare scaling methods with plots
|
||||||
|
#output normalised file
|
||||||
|
#=======================================================
|
||||||
|
|
||||||
|
####################
|
||||||
|
#### read file #####: this will be the output of my R script that cleans the data columns
|
||||||
|
####################
|
||||||
|
source("../Scripts/step3c_data_cleaning.R")
|
||||||
|
##This will outut two dataframes:
|
||||||
|
##data: unclean data: 335, 10
|
||||||
|
##df : cleaned df 335, 13
|
||||||
|
## you can remove data if you want as you will not need it
|
||||||
|
rm(data)
|
||||||
|
|
||||||
|
colnames(df)
|
||||||
|
|
||||||
|
#===================
|
||||||
|
#3a: PredAffLog
|
||||||
|
#===================
|
||||||
|
n = which(colnames(df) == "PredAffLog"); n
|
||||||
|
group = which(colnames(df) == "Lig_outcome"); group
|
||||||
|
|
||||||
|
#===================================================
|
||||||
|
# order according to PredAffLog values
|
||||||
|
#===================================================
|
||||||
|
# This is because this makes it easier to see the results of rescaling for debugging
|
||||||
|
head(df$PredAffLog)
|
||||||
|
|
||||||
|
#ORDER BY PredAff scrores: negative values at the top and positive at the bottoom
|
||||||
|
df = df[order(df$PredAffLog),]
|
||||||
|
head(df$PredAffLog)
|
||||||
|
|
||||||
|
#sanity checks
|
||||||
|
head(df[,n]) #all negatives
|
||||||
|
tail(df[,n]) #all positives
|
||||||
|
|
||||||
|
#sanity checks
|
||||||
|
mean(df[,n])
|
||||||
|
#-0.9526746
|
||||||
|
|
||||||
|
tapply(df[,n], df[,group], mean)
|
||||||
|
#Destabilizing Stabilizing
|
||||||
|
#-1.2112100 0.3926667
|
||||||
|
#===========================
|
||||||
|
#Same as above: in 2 steps
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
#find range of your data
|
||||||
|
my_min = min(df[,n]); my_min #-3.948
|
||||||
|
my_max = max(df[,n]); my_max #2.23
|
||||||
|
|
||||||
|
#===============================================
|
||||||
|
# WITHIN GROUP rescaling 2: method "ratio"
|
||||||
|
# create column to store the rescaled values
|
||||||
|
# Rescaling separately (Less dangerous)
|
||||||
|
# =====> chosen one:as Nick prefers
|
||||||
|
#===============================================
|
||||||
|
df$ratioPredAff = ifelse(df[,n] < 0
|
||||||
|
, df[,n]/abs(my_min)
|
||||||
|
, df[,n]/my_max
|
||||||
|
)#335 14
|
||||||
|
#sanity checks
|
||||||
|
head(df$ratioPredAff)
|
||||||
|
tail(df$ratioPredAff)
|
||||||
|
|
||||||
|
min(df$ratioPredAff); max(df$ratioPredAff)
|
||||||
|
|
||||||
|
tapply(df$ratioPredAff, df$Lig_outcome, min)
|
||||||
|
#Destabilizing Stabilizing
|
||||||
|
#-1.000000000 0.005381166
|
||||||
|
|
||||||
|
tapply(df$ratioPredAff, df$Lig_outcome, max)
|
||||||
|
#Destabilizing Stabilizing
|
||||||
|
#-0.001266464 1.000000000
|
||||||
|
|
||||||
|
#should be the same as below (281 and 54)
|
||||||
|
sum(df$ratioPredAff < 0); sum(df$ratioPredAff > 0)
|
||||||
|
|
||||||
|
table(df$Lig_outcome)
|
||||||
|
#Destabilizing Stabilizing
|
||||||
|
#281 54
|
||||||
|
|
||||||
|
#===============================================
|
||||||
|
# Hist and density plots to compare the rescaling
|
||||||
|
# methods: Base R
|
||||||
|
#===============================================
|
||||||
|
#uncomment as necessary
|
||||||
|
my_title = "Ligand_stability"
|
||||||
|
#my_title = colnames(df[n])
|
||||||
|
|
||||||
|
# Set the margin on all sides
|
||||||
|
par(oma = c(3,2,3,0)
|
||||||
|
, mar = c(1,3,5,2)
|
||||||
|
, mfrow = c(2,2))
|
||||||
|
|
||||||
|
hist(df[,n]
|
||||||
|
, xlab = ""
|
||||||
|
, main = "Raw values"
|
||||||
|
)
|
||||||
|
|
||||||
|
hist(df$ratioPredAff
|
||||||
|
, xlab = ""
|
||||||
|
, main = "ratio rescaling"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Plot density plots underneath
|
||||||
|
plot(density( df[,n] )
|
||||||
|
, main = "Raw values"
|
||||||
|
)
|
||||||
|
|
||||||
|
plot(density( df$ratioPredAff )
|
||||||
|
, main = "ratio rescaling"
|
||||||
|
)
|
||||||
|
|
||||||
|
# titles
|
||||||
|
mtext(text = "Frequency"
|
||||||
|
, side = 2
|
||||||
|
, line = 0
|
||||||
|
, outer = TRUE)
|
||||||
|
|
||||||
|
mtext(text = my_title
|
||||||
|
, side = 3
|
||||||
|
, line = 0
|
||||||
|
, outer = TRUE)
|
||||||
|
|
||||||
|
|
||||||
|
#clear variables
|
||||||
|
rm(my_min, my_max, my_title, n, group)
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# 3b: DUET stability
|
||||||
|
#===================
|
||||||
|
dim(df) #335, 14
|
||||||
|
|
||||||
|
n = which(colnames(df) == "DUETStability_Kcalpermol"); n # 10
|
||||||
|
group = which(colnames(df) == "DUET_outcome"); group #12
|
||||||
|
|
||||||
|
#===================================================
|
||||||
|
# order according to DUET scores
|
||||||
|
#===================================================
|
||||||
|
# This is because this makes it easier to see the results of rescaling for debugging
|
||||||
|
head(df$DUETStability_Kcalpermol)
|
||||||
|
|
||||||
|
#ORDER BY DUET scores: negative values at the top and positive at the bottom
|
||||||
|
df = df[order(df$DUETStability_Kcalpermol),]
|
||||||
|
|
||||||
|
#sanity checks
|
||||||
|
head(df[,n]) #negatives
|
||||||
|
tail(df[,n]) #positives
|
||||||
|
|
||||||
|
#sanity checks
|
||||||
|
mean(df[,n])
|
||||||
|
#[1] -1.173316
|
||||||
|
|
||||||
|
tapply(df[,n], df[,group], mean)
|
||||||
|
#Destabilizing Stabilizing
|
||||||
|
#-1.4297257 0.3978723
|
||||||
|
|
||||||
|
#===============================================
|
||||||
|
# WITHIN GROUP rescaling 2: method "ratio"
|
||||||
|
# create column to store the rescaled values
|
||||||
|
# Rescaling separately (Less dangerous)
|
||||||
|
# =====> chosen one:as Nick prefers
|
||||||
|
#===============================================
|
||||||
|
#find range of your data
|
||||||
|
my_min = min(df[,n]); my_min #-3.87
|
||||||
|
my_max = max(df[,n]); my_max #1.689
|
||||||
|
|
||||||
|
df$ratioDUET = ifelse(df[,n] < 0
|
||||||
|
, df[,n]/abs(my_min)
|
||||||
|
, df[,n]/my_max
|
||||||
|
) #335, 15
|
||||||
|
#sanity check
|
||||||
|
head(df$ratioDUET)
|
||||||
|
tail(df$ratioDUET)
|
||||||
|
|
||||||
|
min(df$ratioDUET); max(df$ratioDUET)
|
||||||
|
|
||||||
|
#sanity checks
|
||||||
|
tapply(df$ratioDUET, df$DUET_outcome, min)
|
||||||
|
#Destabilizing Stabilizing
|
||||||
|
#-1.00000000 0.01065719
|
||||||
|
|
||||||
|
tapply(df$ratioDUET, df$DUET_outcome, max)
|
||||||
|
#Destabilizing Stabilizing
|
||||||
|
#-0.003875969 1.000000000
|
||||||
|
|
||||||
|
#should be the same as below (267 and 42)
|
||||||
|
sum(df$ratioDUET < 0); sum(df$ratioDUET > 0)
|
||||||
|
|
||||||
|
table(df$DUET_outcome)
|
||||||
|
#Destabilizing Stabilizing
|
||||||
|
#288 47
|
||||||
|
|
||||||
|
#===============================================
|
||||||
|
# Hist and density plots to compare the rescaling
|
||||||
|
# methods: Base R
|
||||||
|
#===============================================
|
||||||
|
#uncomment as necessary
|
||||||
|
|
||||||
|
my_title = "DUET_stability"
|
||||||
|
#my_title = colnames(df[n])
|
||||||
|
|
||||||
|
# Set the margin on all sides
|
||||||
|
par(oma = c(3,2,3,0)
|
||||||
|
, mar = c(1,3,5,2)
|
||||||
|
, mfrow = c(2,2))
|
||||||
|
|
||||||
|
hist(df[,n]
|
||||||
|
, xlab = ""
|
||||||
|
, main = "Raw values"
|
||||||
|
)
|
||||||
|
|
||||||
|
hist(df$ratioDUET
|
||||||
|
, xlab = ""
|
||||||
|
, main = "ratio rescaling"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Plot density plots underneath
|
||||||
|
plot(density( df[,n] )
|
||||||
|
, main = "Raw values"
|
||||||
|
)
|
||||||
|
|
||||||
|
plot(density( df$ratioDUET )
|
||||||
|
, main = "ratio rescaling"
|
||||||
|
)
|
||||||
|
|
||||||
|
# graph titles
|
||||||
|
mtext(text = "Frequency"
|
||||||
|
, side = 2
|
||||||
|
, line = 0
|
||||||
|
, outer = TRUE)
|
||||||
|
|
||||||
|
mtext(text = my_title
|
||||||
|
, side = 3
|
||||||
|
, line = 0
|
||||||
|
, outer = TRUE)
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# write output as csv file
|
||||||
|
#===================
|
||||||
|
write.csv(df, "../Data/mcsm_complex1_normalised.csv", row.names = FALSE) #335, 15
|
131
mcsm_analysis/pyrazinamide/scripts/mcsm_mean_stability.R
Normal file
131
mcsm_analysis/pyrazinamide/scripts/mcsm_mean_stability.R
Normal file
|
@ -0,0 +1,131 @@
|
||||||
|
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)
|
BIN
mcsm_analysis/pyrazinamide/scripts/plotting/.RData
Normal file
BIN
mcsm_analysis/pyrazinamide/scripts/plotting/.RData
Normal file
Binary file not shown.
0
mcsm_analysis/pyrazinamide/scripts/plotting/.Rhistory
Normal file
0
mcsm_analysis/pyrazinamide/scripts/plotting/.Rhistory
Normal file
|
@ -0,0 +1,250 @@
|
||||||
|
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(cowplot)
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for PS #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for OR and stability plots
|
||||||
|
# you need merged_df3_comp
|
||||||
|
# since these are matched
|
||||||
|
# to allow pairwise corr
|
||||||
|
###########################
|
||||||
|
|
||||||
|
# uncomment as necessary
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
my_df = merged_df3_comp
|
||||||
|
#my_df = merged_df3
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
# delete variables not required
|
||||||
|
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||||
|
|
||||||
|
# quick checks
|
||||||
|
colnames(my_df)
|
||||||
|
str(my_df)
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
is.numeric(my_df$OR)
|
||||||
|
#[1] TRUE
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
# FOR PS Plots
|
||||||
|
#<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
PS_df = my_df
|
||||||
|
|
||||||
|
rm(my_df)
|
||||||
|
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> end of section 1
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for lig #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
source("combining_two_df_lig.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for OR and stability plots
|
||||||
|
# you need merged_df3_comp
|
||||||
|
# since these are matched
|
||||||
|
# to allow pairwise corr
|
||||||
|
###########################
|
||||||
|
|
||||||
|
# uncomment as necessary
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
my_df2 = merged_df3_comp
|
||||||
|
#my_df2 = merged_df3
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
# delete variables not required
|
||||||
|
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||||
|
|
||||||
|
# quick checks
|
||||||
|
colnames(my_df2)
|
||||||
|
str(my_df2)
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
is.numeric(my_df2$OR)
|
||||||
|
#[1] TRUE
|
||||||
|
|
||||||
|
# sanity check: should be <10
|
||||||
|
if (max(my_df2$Dis_lig_Ang) < 10){
|
||||||
|
print ("Sanity check passed: lig data is <10Ang")
|
||||||
|
}else{
|
||||||
|
print ("Error: data should be filtered to be within 10Ang")
|
||||||
|
}
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
# FOR Lig Plots
|
||||||
|
#<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
Lig_df = my_df2
|
||||||
|
|
||||||
|
rm(my_df2)
|
||||||
|
|
||||||
|
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> end of section 1
|
||||||
|
|
||||||
|
#############
|
||||||
|
# Plots: Bubble plot
|
||||||
|
# x = Position, Y = stability
|
||||||
|
# size of dots = OR
|
||||||
|
# col: stability
|
||||||
|
#############
|
||||||
|
|
||||||
|
#=================
|
||||||
|
# generate plot 1: DUET vs OR by position as geom_points
|
||||||
|
#=================
|
||||||
|
|
||||||
|
my_ats = 20 # axis text size
|
||||||
|
my_als = 22 # axis label size
|
||||||
|
|
||||||
|
# Spelling Correction: made redundant as already corrected at the source
|
||||||
|
|
||||||
|
#PS_df$DUET_outcome[PS_df$DUET_outcome=='Stabilizing'] <- 'Stabilising'
|
||||||
|
#PS_df$DUET_outcome[PS_df$DUET_outcome=='Destabilizing'] <- 'Destabilising'
|
||||||
|
|
||||||
|
table(PS_df$DUET_outcome) ; sum(table(PS_df$DUET_outcome))
|
||||||
|
|
||||||
|
g = ggplot(PS_df, aes(x = factor(Position)
|
||||||
|
, y = ratioDUET))
|
||||||
|
|
||||||
|
p1 = g +
|
||||||
|
geom_point(aes(col = DUET_outcome
|
||||||
|
, size = OR)) +
|
||||||
|
theme(axis.text.x = element_text(size = my_ats
|
||||||
|
, angle = 90
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0.4)
|
||||||
|
, axis.text.y = element_text(size = my_ats
|
||||||
|
, angle = 0
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0)
|
||||||
|
, axis.title.x = element_text(size = my_als)
|
||||||
|
, axis.title.y = element_text(size = my_als)
|
||||||
|
, legend.text = element_text(size = my_als)
|
||||||
|
, legend.title = element_text(size = my_als) ) +
|
||||||
|
#, legend.key.size = unit(1, "cm")) +
|
||||||
|
labs(title = ""
|
||||||
|
, x = "Position"
|
||||||
|
, y = "DUET(PS)"
|
||||||
|
, size = "Odds Ratio"
|
||||||
|
, colour = "DUET Outcome") +
|
||||||
|
guides(colour = guide_legend(override.aes = list(size=4)))
|
||||||
|
|
||||||
|
p1
|
||||||
|
|
||||||
|
#=================
|
||||||
|
# generate plot 2: Lig vs OR by position as geom_points
|
||||||
|
#=================
|
||||||
|
my_ats = 20 # axis text size
|
||||||
|
my_als = 22 # axis label size
|
||||||
|
|
||||||
|
# Spelling Correction: made redundant as already corrected at the source
|
||||||
|
|
||||||
|
#Lig_df$Lig_outcome[Lig_df$Lig_outcome=='Stabilizing'] <- 'Stabilising'
|
||||||
|
#Lig_df$Lig_outcome[Lig_df$Lig_outcome=='Destabilizing'] <- 'Destabilising'
|
||||||
|
|
||||||
|
table(Lig_df$Lig_outcome)
|
||||||
|
|
||||||
|
g = ggplot(Lig_df, aes(x = factor(Position)
|
||||||
|
, y = ratioPredAff))
|
||||||
|
|
||||||
|
p2 = g +
|
||||||
|
geom_point(aes(col = Lig_outcome
|
||||||
|
, size = OR))+
|
||||||
|
theme(axis.text.x = element_text(size = my_ats
|
||||||
|
, angle = 90
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0.4)
|
||||||
|
, axis.text.y = element_text(size = my_ats
|
||||||
|
, angle = 0
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0)
|
||||||
|
, axis.title.x = element_text(size = my_als)
|
||||||
|
, axis.title.y = element_text(size = my_als)
|
||||||
|
, legend.text = element_text(size = my_als)
|
||||||
|
, legend.title = element_text(size = my_als) ) +
|
||||||
|
#, legend.key.size = unit(1, "cm")) +
|
||||||
|
labs(title = ""
|
||||||
|
, x = "Position"
|
||||||
|
, y = "Ligand Affinity"
|
||||||
|
, size = "Odds Ratio"
|
||||||
|
, colour = "Ligand Outcome"
|
||||||
|
) +
|
||||||
|
guides(colour = guide_legend(override.aes = list(size=4)))
|
||||||
|
|
||||||
|
p2
|
||||||
|
|
||||||
|
#======================
|
||||||
|
#combine using cowplot
|
||||||
|
#======================
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots"
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
svg('PS_Lig_OR_combined.svg', width = 32, height = 12) #inches
|
||||||
|
#png('PS_Lig_OR_combined.png', width = 2800, height = 1080) #300dpi
|
||||||
|
theme_set(theme_gray()) # to preserve default theme
|
||||||
|
|
||||||
|
printFile = cowplot::plot_grid(plot_grid(p1, p2
|
||||||
|
, ncol = 1
|
||||||
|
, align = 'v'
|
||||||
|
, labels = c("A", "B")
|
||||||
|
, label_size = my_als+5))
|
||||||
|
print(printFile)
|
||||||
|
dev.off()
|
||||||
|
|
|
@ -0,0 +1,154 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Installing and loading required packages #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../Header_TT.R")
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for lig #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df_lig.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for Lig plots
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
#############################
|
||||||
|
# Extra sanity check:
|
||||||
|
# for mcsm_lig ONLY
|
||||||
|
# Dis_lig_Ang should be <10
|
||||||
|
#############################
|
||||||
|
|
||||||
|
if (max(my_df$Dis_lig_Ang) < 10){
|
||||||
|
print ("Sanity check passed: lig data is <10Ang")
|
||||||
|
}else{
|
||||||
|
print ("Error: data should be filtered to be within 10Ang")
|
||||||
|
}
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# Plot: Barplot with scores (unordered)
|
||||||
|
# corresponds to Lig_outcome
|
||||||
|
# Stacked Barplot with colours: Lig_outcome @ position coloured by
|
||||||
|
# Lig_outcome. This is a barplot where each bar corresponds
|
||||||
|
# to a SNP and is coloured by its corresponding Lig_outcome.
|
||||||
|
#============================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
#%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
# REASSIGNMENT
|
||||||
|
df = my_df
|
||||||
|
#%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
|
||||||
|
rm(my_df)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
upos = unique(my_df$Position)
|
||||||
|
|
||||||
|
# should be a factor
|
||||||
|
is.factor(df$Lig_outcome)
|
||||||
|
#TRUE
|
||||||
|
|
||||||
|
table(df$Lig_outcome)
|
||||||
|
|
||||||
|
# should be -1 and 1: may not be in this case because you have filtered the data
|
||||||
|
# FIXME: normalisation before or after filtering?
|
||||||
|
min(df$ratioPredAff) #
|
||||||
|
max(df$ratioPredAff) #
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
tapply(df$ratioPredAff, df$Lig_outcome, min)
|
||||||
|
tapply(df$ratioPredAff, df$Lig_outcome, max)
|
||||||
|
|
||||||
|
#******************
|
||||||
|
# generate plot
|
||||||
|
#******************
|
||||||
|
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
my_title = "Ligand affinity"
|
||||||
|
|
||||||
|
# axis label size
|
||||||
|
my_xaxls = 13
|
||||||
|
my_yaxls = 15
|
||||||
|
|
||||||
|
# axes text size
|
||||||
|
my_xaxts = 15
|
||||||
|
my_yaxts = 15
|
||||||
|
|
||||||
|
# no ordering of x-axis
|
||||||
|
g = ggplot(df, aes(factor(Position, ordered = T)))
|
||||||
|
g +
|
||||||
|
geom_bar(aes(fill = Lig_outcome), colour = "grey") +
|
||||||
|
theme( axis.text.x = element_text(size = my_xaxls
|
||||||
|
, angle = 90
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0.4)
|
||||||
|
, axis.text.y = element_text(size = my_yaxls
|
||||||
|
, angle = 0
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0)
|
||||||
|
, axis.title.x = element_text(size = my_xaxts)
|
||||||
|
, axis.title.y = element_text(size = my_yaxts ) ) +
|
||||||
|
labs(title = my_title
|
||||||
|
, x = "Position"
|
||||||
|
, y = "Frequency")
|
||||||
|
|
||||||
|
# for sanity and good practice
|
||||||
|
rm(df)
|
||||||
|
#======================= end of plot
|
||||||
|
# axis colours labels
|
||||||
|
# https://stackoverflow.com/questions/38862303/customize-ggplot2-axis-labels-with-different-colors
|
||||||
|
# https://stackoverflow.com/questions/56543485/plot-coloured-boxes-around-axis-label
|
|
@ -0,0 +1,149 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Installing and loading required packages and functions #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../Header_TT.R")
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for PS #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#==========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for DUET plots
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
# sanity check
|
||||||
|
is.factor(my_df$DUET_outcome)
|
||||||
|
my_df$DUET_outcome = as.factor(my_df$DUET_outcome)
|
||||||
|
is.factor(my_df$DUET_outcome)
|
||||||
|
#[1] TRUE
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# Plot 2: Barplot with scores (unordered)
|
||||||
|
# corresponds to DUET_outcome
|
||||||
|
# Stacked Barplot with colours: DUET_outcome @ position coloured by
|
||||||
|
# DUET outcome. This is a barplot where each bar corresponds
|
||||||
|
# to a SNP and is coloured by its corresponding DUET_outcome
|
||||||
|
#============================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df = my_df
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
rm(my_df)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
upos = unique(df$Position)
|
||||||
|
|
||||||
|
# should be a factor
|
||||||
|
is.factor(my_df$DUET_outcome)
|
||||||
|
#[1] TRUE
|
||||||
|
|
||||||
|
table(my_df$DUET_outcome)
|
||||||
|
|
||||||
|
# should be -1 and 1
|
||||||
|
min(df$ratioDUET)
|
||||||
|
max(df$ratioDUET)
|
||||||
|
|
||||||
|
tapply(df$ratioDUET, df$DUET_outcome, min)
|
||||||
|
tapply(df$ratioDUET, df$DUET_outcome, max)
|
||||||
|
|
||||||
|
#******************
|
||||||
|
# generate plot
|
||||||
|
#******************
|
||||||
|
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
my_title = "Protein stability (DUET)"
|
||||||
|
|
||||||
|
# axis label size
|
||||||
|
my_xaxls = 13
|
||||||
|
my_yaxls = 15
|
||||||
|
|
||||||
|
# axes text size
|
||||||
|
my_xaxts = 15
|
||||||
|
my_yaxts = 15
|
||||||
|
|
||||||
|
# no ordering of x-axis
|
||||||
|
g = ggplot(df, aes(factor(Position, ordered = T)))
|
||||||
|
g +
|
||||||
|
geom_bar(aes(fill = DUET_outcome), colour = "grey") +
|
||||||
|
|
||||||
|
theme( axis.text.x = element_text(size = my_xaxls
|
||||||
|
, angle = 90
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0.4)
|
||||||
|
, axis.text.y = element_text(size = my_yaxls
|
||||||
|
, angle = 0
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0)
|
||||||
|
, axis.title.x = element_text(size = my_xaxts)
|
||||||
|
, axis.title.y = element_text(size = my_yaxts ) ) +
|
||||||
|
labs(title = my_title
|
||||||
|
, x = "Position"
|
||||||
|
, y = "Frequency")
|
||||||
|
|
||||||
|
# for sanity and good practice
|
||||||
|
rm(df)
|
||||||
|
#======================= end of plot
|
||||||
|
# axis colours labels
|
||||||
|
# https://stackoverflow.com/questions/38862303/customize-ggplot2-axis-labels-with-different-colors
|
||||||
|
# https://stackoverflow.com/questions/56543485/plot-coloured-boxes-around-axis-label
|
|
@ -0,0 +1,202 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Installing and loading required packages and functions #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../Header_TT.R")
|
||||||
|
source("../barplot_colour_function.R")
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for lig #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df_lig.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for Lig plots
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
# sanity check
|
||||||
|
is.factor(my_df$Lig_outcome)
|
||||||
|
my_df$Lig_outcome = as.factor(my_df$Ligoutcome)
|
||||||
|
is.factor(my_df$Lig_outcome)
|
||||||
|
#[1] TRUE
|
||||||
|
|
||||||
|
#############################
|
||||||
|
# Extra sanity check:
|
||||||
|
# for mcsm_lig ONLY
|
||||||
|
# Dis_lig_Ang should be <10
|
||||||
|
#############################
|
||||||
|
|
||||||
|
if (max(my_df$Dis_lig_Ang) < 10){
|
||||||
|
print ("Sanity check passed: lig data is <10Ang")
|
||||||
|
}else{
|
||||||
|
print ("Error: data should be filtered to be within 10Ang")
|
||||||
|
}
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# Plot: Barplot with scores (unordered)
|
||||||
|
# corresponds to Lig_outcome
|
||||||
|
# Stacked Barplot with colours: Lig_outcome @ position coloured by
|
||||||
|
# stability scores. This is a barplot where each bar corresponds
|
||||||
|
# to a SNP and is coloured by its corresponding Lig stability value.
|
||||||
|
# Normalised values (range between -1 and 1 ) to aid visualisation
|
||||||
|
# NOTE: since barplot plots discrete values, colour = score, so number of
|
||||||
|
# colours will be equal to the no. of unique normalised scores
|
||||||
|
# rather than a continuous scale
|
||||||
|
# will require generating the colour scale separately.
|
||||||
|
#============================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df = my_df
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
rm(my_df)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
table(df$Lig_outcome)
|
||||||
|
|
||||||
|
# should be -1 and 1: may not be in this case because you have filtered the data
|
||||||
|
# FIXME: normalisation before or after filtering?
|
||||||
|
min(df$ratioPredAff) #
|
||||||
|
max(df$ratioPredAff) #
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
# very important!!!!
|
||||||
|
tapply(df$ratioPredAff, df$Lig_outcome, min)
|
||||||
|
|
||||||
|
tapply(df$ratioPredAff, df$Lig_outcome, max)
|
||||||
|
|
||||||
|
|
||||||
|
#******************
|
||||||
|
# generate plot
|
||||||
|
#******************
|
||||||
|
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
# My colour FUNCTION: based on group and subgroup
|
||||||
|
# in my case;
|
||||||
|
# df = df
|
||||||
|
# group = Lig_outcome
|
||||||
|
# subgroup = normalised score i.e ratioPredAff
|
||||||
|
|
||||||
|
# Prepare data: round off ratioLig scores
|
||||||
|
# round off to 3 significant digits:
|
||||||
|
# 165 if no rounding is performed: used to generate the originalgraph
|
||||||
|
# 156 if rounded to 3 places
|
||||||
|
# FIXME: check if reducing precision creates any ML prob
|
||||||
|
|
||||||
|
# check unique values in normalised data
|
||||||
|
u = unique(df$ratioPredAff)
|
||||||
|
|
||||||
|
# <<<<< -------------------------------------------
|
||||||
|
# Run this section if rounding is to be used
|
||||||
|
# specify number for rounding
|
||||||
|
n = 3
|
||||||
|
df$ratioLigR = round(df$ratioPredAff, n)
|
||||||
|
u = unique(df$ratioLigR) # 156
|
||||||
|
# create an extra column called group which contains the "gp name and score"
|
||||||
|
# so colours can be generated for each unique values in this column
|
||||||
|
my_grp = df$ratioLigR
|
||||||
|
df$group <- paste0(df$Lig_outcome, "_", my_grp, sep = "")
|
||||||
|
|
||||||
|
# else
|
||||||
|
# uncomment the below if rounding is not required
|
||||||
|
|
||||||
|
#my_grp = df$ratioLig
|
||||||
|
#df$group <- paste0(df$Lig_outcome, "_", my_grp, sep = "")
|
||||||
|
|
||||||
|
# <<<<< -----------------------------------------------
|
||||||
|
|
||||||
|
# Call the function to create the palette based on the group defined above
|
||||||
|
colours <- ColourPalleteMulti(df, "Lig_outcome", "my_grp")
|
||||||
|
my_title = "Ligand affinity"
|
||||||
|
|
||||||
|
# axis label size
|
||||||
|
my_xaxls = 13
|
||||||
|
my_yaxls = 15
|
||||||
|
|
||||||
|
# axes text size
|
||||||
|
my_xaxts = 15
|
||||||
|
my_yaxts = 15
|
||||||
|
|
||||||
|
# no ordering of x-axis
|
||||||
|
g = ggplot(df, aes(factor(Position, ordered = T)))
|
||||||
|
g +
|
||||||
|
geom_bar(aes(fill = group), colour = "grey") +
|
||||||
|
scale_fill_manual( values = colours
|
||||||
|
, guide = 'none') +
|
||||||
|
theme( axis.text.x = element_text(size = my_xaxls
|
||||||
|
, angle = 90
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0.4)
|
||||||
|
, axis.text.y = element_text(size = my_yaxls
|
||||||
|
, angle = 0
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0)
|
||||||
|
, axis.title.x = element_text(size = my_xaxts)
|
||||||
|
, axis.title.y = element_text(size = my_yaxts ) ) +
|
||||||
|
labs(title = my_title
|
||||||
|
, x = "Position"
|
||||||
|
, y = "Frequency")
|
||||||
|
|
||||||
|
# for sanity and good practice
|
||||||
|
rm(df)
|
||||||
|
#======================= end of plot
|
||||||
|
# axis colours labels
|
||||||
|
# https://stackoverflow.com/questions/38862303/customize-ggplot2-axis-labels-with-different-colors
|
||||||
|
# https://stackoverflow.com/questions/56543485/plot-coloured-boxes-around-axis-label
|
|
@ -0,0 +1,192 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Installing and loading required packages and functions #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../Header_TT.R")
|
||||||
|
source("../barplot_colour_function.R")
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for PS #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for DUET plots
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
# sanity check
|
||||||
|
is.factor(my_df$DUET_outcome)
|
||||||
|
my_df$DUET_outcome = as.factor(my_df$DUET_outcome)
|
||||||
|
is.factor(my_df$DUET_outcome)
|
||||||
|
#[1] TRUE
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# Barplot with scores (unordered)
|
||||||
|
# corresponds to DUET_outcome
|
||||||
|
# Stacked Barplot with colours: DUET_outcome @ position coloured by
|
||||||
|
# stability scores. This is a barplot where each bar corresponds
|
||||||
|
# to a SNP and is coloured by its corresponding DUET stability value.
|
||||||
|
# Normalised values (range between -1 and 1 ) to aid visualisation
|
||||||
|
# NOTE: since barplot plots discrete values, colour = score, so number of
|
||||||
|
# colours will be equal to the no. of unique normalised scores
|
||||||
|
# rather than a continuous scale
|
||||||
|
# will require generating the colour scale separately.
|
||||||
|
#============================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df = my_df
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
rm(my_df)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
upos = unique(df$Position)
|
||||||
|
|
||||||
|
# should be a factor
|
||||||
|
is.factor(my_df$DUET_outcome)
|
||||||
|
#[1] TRUE
|
||||||
|
|
||||||
|
table(df$DUET_outcome)
|
||||||
|
|
||||||
|
# should be -1 and 1
|
||||||
|
min(df$ratioDUET)
|
||||||
|
max(df$ratioDUET)
|
||||||
|
|
||||||
|
tapply(df$ratioDUET, df$DUET_outcome, min)
|
||||||
|
tapply(df$ratioDUET, df$DUET_outcome, max)
|
||||||
|
|
||||||
|
#******************
|
||||||
|
# generate plot
|
||||||
|
#******************
|
||||||
|
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
# My colour FUNCTION: based on group and subgroup
|
||||||
|
# in my case;
|
||||||
|
# df = df
|
||||||
|
# group = DUET_outcome
|
||||||
|
# subgroup = normalised score i.e ratioDUET
|
||||||
|
|
||||||
|
# Prepare data: round off ratioDUET scores
|
||||||
|
# round off to 3 significant digits:
|
||||||
|
# 323 if no rounding is performed: used to generate the original graph
|
||||||
|
# 287 if rounded to 3 places
|
||||||
|
# FIXME: check if reducing precicion creates any ML prob
|
||||||
|
|
||||||
|
# check unique values in normalised data
|
||||||
|
u = unique(df$ratioDUET)
|
||||||
|
|
||||||
|
# <<<<< -------------------------------------------
|
||||||
|
# Run this section if rounding is to be used
|
||||||
|
# specify number for rounding
|
||||||
|
n = 3
|
||||||
|
df$ratioDUETR = round(df$ratioDUET, n)
|
||||||
|
u = unique(df$ratioDUETR)
|
||||||
|
# create an extra column called group which contains the "gp name and score"
|
||||||
|
# so colours can be generated for each unique values in this column
|
||||||
|
my_grp = df$ratioDUETR
|
||||||
|
df$group <- paste0(df$DUET_outcome, "_", my_grp, sep = "")
|
||||||
|
|
||||||
|
# else
|
||||||
|
# uncomment the below if rounding is not required
|
||||||
|
|
||||||
|
#my_grp = df$ratioDUET
|
||||||
|
#df$group <- paste0(df$DUET_outcome, "_", my_grp, sep = "")
|
||||||
|
|
||||||
|
# <<<<< -----------------------------------------------
|
||||||
|
|
||||||
|
# Call the function to create the palette based on the group defined above
|
||||||
|
colours <- ColourPalleteMulti(df, "DUET_outcome", "my_grp")
|
||||||
|
my_title = "Protein stability (DUET)"
|
||||||
|
|
||||||
|
# axis label size
|
||||||
|
my_xaxls = 13
|
||||||
|
my_yaxls = 15
|
||||||
|
|
||||||
|
# axes text size
|
||||||
|
my_xaxts = 15
|
||||||
|
my_yaxts = 15
|
||||||
|
|
||||||
|
# no ordering of x-axis
|
||||||
|
g = ggplot(df, aes(factor(Position, ordered = T)))
|
||||||
|
g +
|
||||||
|
geom_bar(aes(fill = group), colour = "grey") +
|
||||||
|
scale_fill_manual( values = colours
|
||||||
|
, guide = 'none') +
|
||||||
|
theme( axis.text.x = element_text(size = my_xaxls
|
||||||
|
, angle = 90
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0.4)
|
||||||
|
, axis.text.y = element_text(size = my_yaxls
|
||||||
|
, angle = 0
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0)
|
||||||
|
, axis.title.x = element_text(size = my_xaxts)
|
||||||
|
, axis.title.y = element_text(size = my_yaxts ) ) +
|
||||||
|
labs(title = my_title
|
||||||
|
, x = "Position"
|
||||||
|
, y = "Frequency")
|
||||||
|
|
||||||
|
# for sanity and good practice
|
||||||
|
rm(df)
|
||||||
|
#======================= end of plot
|
||||||
|
# axis colours labels
|
||||||
|
# https://stackoverflow.com/questions/38862303/customize-ggplot2-axis-labels-with-different-colors
|
||||||
|
# https://stackoverflow.com/questions/56543485/plot-coloured-boxes-around-axis-label
|
215
mcsm_analysis/pyrazinamide/scripts/plotting/basic_barplots_LIG.R
Normal file
215
mcsm_analysis/pyrazinamide/scripts/plotting/basic_barplots_LIG.R
Normal file
|
@ -0,0 +1,215 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Installing and loading required packages #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../Header_TT.R")
|
||||||
|
|
||||||
|
#require(data.table)
|
||||||
|
#require(dplyr)
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for lig #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df_lig.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for Lig plots
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
# sanity check
|
||||||
|
is.factor(my_df$Lig_outcome)
|
||||||
|
my_df$Lig_outcome = as.factor(my_df$lig_outcome)
|
||||||
|
is.factor(my_df$Lig_outcome)
|
||||||
|
#[1] TRUE
|
||||||
|
|
||||||
|
#############################
|
||||||
|
# Extra sanity check:
|
||||||
|
# for mcsm_lig ONLY
|
||||||
|
# Dis_lig_Ang should be <10
|
||||||
|
#############################
|
||||||
|
|
||||||
|
if (max(my_df$Dis_lig_Ang) < 10){
|
||||||
|
print ("Sanity check passed: lig data is <10Ang")
|
||||||
|
}else{
|
||||||
|
print ("Error: data should be filtered to be within 10Ang")
|
||||||
|
}
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#===========================
|
||||||
|
# Plot: Basic barplots
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df = my_df
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
rm(my_df)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
str(df)
|
||||||
|
|
||||||
|
if (identical(df$Position, df$position)){
|
||||||
|
print("Sanity check passed: Columns 'Position' and 'position' are identical")
|
||||||
|
} else{
|
||||||
|
print("Error!: Check column names and info contained")
|
||||||
|
}
|
||||||
|
|
||||||
|
#****************
|
||||||
|
# generate plot: No of stabilising and destabilsing muts
|
||||||
|
#****************
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
svg('basic_barplots_LIG.svg')
|
||||||
|
|
||||||
|
my_ats = 25 # axis text size
|
||||||
|
my_als = 22 # axis label size
|
||||||
|
|
||||||
|
# uncomment as necessary for either directly outputting results or
|
||||||
|
# printing on the screen
|
||||||
|
g = ggplot(df, aes(x = Lig_outcome))
|
||||||
|
#prinfFile = g + geom_bar(
|
||||||
|
g + geom_bar(
|
||||||
|
aes(fill = Lig_outcome)
|
||||||
|
, show.legend = TRUE
|
||||||
|
) + geom_label(
|
||||||
|
stat = "count"
|
||||||
|
, aes(label = ..count..)
|
||||||
|
, color = "black"
|
||||||
|
, show.legend = FALSE
|
||||||
|
, size = 10) + theme(
|
||||||
|
axis.text.x = element_blank()
|
||||||
|
, axis.title.x = element_blank()
|
||||||
|
, axis.title.y = element_text(size=my_als)
|
||||||
|
, axis.text.y = element_text(size = my_ats)
|
||||||
|
, legend.position = c(0.73,0.8)
|
||||||
|
, legend.text = element_text(size=my_als-2)
|
||||||
|
, legend.title = element_text(size=my_als)
|
||||||
|
, plot.title = element_blank()
|
||||||
|
) + labs(
|
||||||
|
title = ""
|
||||||
|
, y = "Number of SNPs"
|
||||||
|
#, fill='Ligand Outcome'
|
||||||
|
) + scale_fill_discrete(name = "Ligand Outcome"
|
||||||
|
, labels = c("Destabilising", "Stabilising"))
|
||||||
|
print(prinfFile)
|
||||||
|
dev.off()
|
||||||
|
|
||||||
|
#****************
|
||||||
|
# generate plot: No of positions
|
||||||
|
#****************
|
||||||
|
#get freq count of positions so you can subset freq<1
|
||||||
|
#require(data.table)
|
||||||
|
setDT(df)[, pos_count := .N, by = .(Position)] #169, 36
|
||||||
|
|
||||||
|
head(df$pos_count)
|
||||||
|
table(df$pos_count)
|
||||||
|
# this is cummulative
|
||||||
|
#1 2 3 4 5 6
|
||||||
|
#5 24 36 56 30 18
|
||||||
|
|
||||||
|
# use group by on this
|
||||||
|
snpsBYpos_df <- df %>%
|
||||||
|
group_by(Position) %>%
|
||||||
|
summarize(snpsBYpos = mean(pos_count))
|
||||||
|
|
||||||
|
table(snpsBYpos_df$snpsBYpos)
|
||||||
|
#1 2 3 4 5 6
|
||||||
|
#5 12 12 14 6 3
|
||||||
|
# this is what will get plotted
|
||||||
|
|
||||||
|
svg('position_count_LIG.svg')
|
||||||
|
|
||||||
|
my_ats = 25 # axis text size
|
||||||
|
my_als = 22 # axis label size
|
||||||
|
|
||||||
|
g = ggplot(snpsBYpos_df, aes(x = snpsBYpos))
|
||||||
|
prinfFile = g + geom_bar(
|
||||||
|
#g + geom_bar(
|
||||||
|
aes (alpha = 0.5)
|
||||||
|
, show.legend = FALSE
|
||||||
|
) +
|
||||||
|
geom_label(
|
||||||
|
stat = "count", aes(label = ..count..)
|
||||||
|
, color = "black"
|
||||||
|
, size = 10
|
||||||
|
) +
|
||||||
|
theme(
|
||||||
|
axis.text.x = element_text(
|
||||||
|
size = my_ats
|
||||||
|
, angle = 0
|
||||||
|
)
|
||||||
|
, axis.text.y = element_text(
|
||||||
|
size = my_ats
|
||||||
|
, angle = 0
|
||||||
|
, hjust = 1
|
||||||
|
)
|
||||||
|
, axis.title.x = element_text(size = my_als)
|
||||||
|
, axis.title.y = element_text(size = my_als)
|
||||||
|
, plot.title = element_blank()
|
||||||
|
) +
|
||||||
|
labs(
|
||||||
|
x = "Number of SNPs"
|
||||||
|
, y = "Number of Sites"
|
||||||
|
)
|
||||||
|
print(prinfFile)
|
||||||
|
dev.off()
|
||||||
|
########################################################################
|
||||||
|
# end of Lig barplots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
|
211
mcsm_analysis/pyrazinamide/scripts/plotting/basic_barplots_PS.R
Normal file
211
mcsm_analysis/pyrazinamide/scripts/plotting/basic_barplots_PS.R
Normal file
|
@ -0,0 +1,211 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Installing and loading required packages and functions #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../Header_TT.R")
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for PS #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#==========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for DUET plots
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
# sanity check
|
||||||
|
is.factor(my_df$DUET_outcome)
|
||||||
|
my_df$DUET_outcome = as.factor(my_df$DUET_outcome)
|
||||||
|
is.factor(my_df$DUET_outcome)
|
||||||
|
#[1] TRUE
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#===========================
|
||||||
|
# Plot: Basic barplots
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df = my_df
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
rm(my_df)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
str(df)
|
||||||
|
|
||||||
|
if (identical(df$Position, df$position)){
|
||||||
|
print("Sanity check passed: Columns 'Position' and 'position' are identical")
|
||||||
|
} else{
|
||||||
|
print("Error!: Check column names and info contained")
|
||||||
|
}
|
||||||
|
|
||||||
|
#****************
|
||||||
|
# generate plot: No of stabilising and destabilsing muts
|
||||||
|
#****************
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
svg('basic_barplots_DUET.svg')
|
||||||
|
|
||||||
|
my_ats = 25 # axis text size
|
||||||
|
my_als = 22 # axis label size
|
||||||
|
|
||||||
|
theme_set(theme_grey())
|
||||||
|
|
||||||
|
# uncomment as necessary for either directly outputting results or
|
||||||
|
# printing on the screen
|
||||||
|
g = ggplot(df, aes(x = DUET_outcome))
|
||||||
|
prinfFile = g + geom_bar(
|
||||||
|
#g + geom_bar(
|
||||||
|
aes(fill = DUET_outcome)
|
||||||
|
, show.legend = TRUE
|
||||||
|
) + geom_label(
|
||||||
|
stat = "count"
|
||||||
|
, aes(label = ..count..)
|
||||||
|
, color = "black"
|
||||||
|
, show.legend = FALSE
|
||||||
|
, size = 10) + theme(
|
||||||
|
axis.text.x = element_blank()
|
||||||
|
, axis.title.x = element_blank()
|
||||||
|
, axis.title.y = element_text(size=my_als)
|
||||||
|
, axis.text.y = element_text(size = my_ats)
|
||||||
|
, legend.position = c(0.73,0.8)
|
||||||
|
, legend.text = element_text(size=my_als-2)
|
||||||
|
, legend.title = element_text(size=my_als)
|
||||||
|
, plot.title = element_blank()
|
||||||
|
) + labs(
|
||||||
|
title = ""
|
||||||
|
, y = "Number of SNPs"
|
||||||
|
#, fill='DUET Outcome'
|
||||||
|
) + scale_fill_discrete(name = "DUET Outcome"
|
||||||
|
, labels = c("Destabilising", "Stabilising"))
|
||||||
|
|
||||||
|
print(prinfFile)
|
||||||
|
dev.off()
|
||||||
|
|
||||||
|
#****************
|
||||||
|
# generate plot: No of positions
|
||||||
|
#****************
|
||||||
|
#get freq count of positions so you can subset freq<1
|
||||||
|
#setDT(df)[, .(Freq := .N), by = .(Position)] #189, 36
|
||||||
|
|
||||||
|
setDT(df)[, pos_count := .N, by = .(Position)] #335, 36
|
||||||
|
table(df$pos_count)
|
||||||
|
# this is cummulative
|
||||||
|
#1 2 3 4 5 6
|
||||||
|
#34 76 63 104 40 18
|
||||||
|
|
||||||
|
# use group by on this
|
||||||
|
snpsBYpos_df <- df %>%
|
||||||
|
group_by(Position) %>%
|
||||||
|
summarize(snpsBYpos = mean(pos_count))
|
||||||
|
|
||||||
|
table(snpsBYpos_df$snpsBYpos)
|
||||||
|
#1 2 3 4 5 6
|
||||||
|
#34 38 21 26 8 3
|
||||||
|
|
||||||
|
foo = select(df, Mutationinformation
|
||||||
|
, WildPos
|
||||||
|
, wild_type
|
||||||
|
, mutant_type
|
||||||
|
, mutation_info
|
||||||
|
, position
|
||||||
|
, pos_count) #335, 5
|
||||||
|
|
||||||
|
getwd()
|
||||||
|
write.csv(foo, "../Data/pos_count_freq.csv")
|
||||||
|
|
||||||
|
svg('position_count_DUET.svg')
|
||||||
|
my_ats = 25 # axis text size
|
||||||
|
my_als = 22 # axis label size
|
||||||
|
|
||||||
|
g = ggplot(snpsBYpos_df, aes(x = snpsBYpos))
|
||||||
|
prinfFile = g + geom_bar(
|
||||||
|
#g + geom_bar(
|
||||||
|
aes (alpha = 0.5)
|
||||||
|
, show.legend = FALSE
|
||||||
|
) +
|
||||||
|
geom_label(
|
||||||
|
stat = "count", aes(label = ..count..)
|
||||||
|
, color = "black"
|
||||||
|
, size = 10
|
||||||
|
) +
|
||||||
|
theme(
|
||||||
|
axis.text.x = element_text(
|
||||||
|
size = my_ats
|
||||||
|
, angle = 0
|
||||||
|
)
|
||||||
|
, axis.text.y = element_text(
|
||||||
|
size = my_ats
|
||||||
|
, angle = 0
|
||||||
|
, hjust = 1
|
||||||
|
)
|
||||||
|
, axis.title.x = element_text(size = my_als)
|
||||||
|
, axis.title.y = element_text(size = my_als)
|
||||||
|
, plot.title = element_blank()
|
||||||
|
) +
|
||||||
|
labs(
|
||||||
|
x = "Number of SNPs"
|
||||||
|
, y = "Number of Sites"
|
||||||
|
)
|
||||||
|
print(prinfFile)
|
||||||
|
dev.off()
|
||||||
|
########################################################################
|
||||||
|
# end of DUET barplots #
|
||||||
|
########################################################################
|
||||||
|
|
175
mcsm_analysis/pyrazinamide/scripts/plotting/corr_plots_v3_PS.R
Normal file
175
mcsm_analysis/pyrazinamide/scripts/plotting/corr_plots_v3_PS.R
Normal file
|
@ -0,0 +1,175 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Installing and loading required packages and functions #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../Header_TT.R")
|
||||||
|
|
||||||
|
#source("barplot_colour_function.R")
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for PS #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#==========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for PS Corr plots
|
||||||
|
# you need merged_df3_comp
|
||||||
|
# since these are matched
|
||||||
|
# to allow pairwise corr
|
||||||
|
###########################
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
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)
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#===========================
|
||||||
|
# Plot: Correlation plots
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df = my_df
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
rm(my_df)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
str(df)
|
||||||
|
|
||||||
|
table(df$DUET_outcome)
|
||||||
|
|
||||||
|
# unique positions
|
||||||
|
length(unique(df$Position)) #{RESULT: unique positions for comp data}
|
||||||
|
|
||||||
|
|
||||||
|
# subset data to generate pairwise correlations
|
||||||
|
corr_data = df[, c("ratioDUET"
|
||||||
|
# , "ratioPredAff"
|
||||||
|
# , "DUETStability_Kcalpermol"
|
||||||
|
# , "PredAffLog"
|
||||||
|
# , "OR"
|
||||||
|
, "logor"
|
||||||
|
# , "pvalue"
|
||||||
|
, "neglog10pvalue"
|
||||||
|
, "AF"
|
||||||
|
, "DUET_outcome"
|
||||||
|
# , "Lig_outcome"
|
||||||
|
, "pyrazinamide"
|
||||||
|
)]
|
||||||
|
dim(corr_data)
|
||||||
|
rm(df)
|
||||||
|
|
||||||
|
# assign nice colnames (for display)
|
||||||
|
my_corr_colnames = c("DUET"
|
||||||
|
# , "Ligand Affinity"
|
||||||
|
# , "DUET_raw"
|
||||||
|
# , "Lig_raw"
|
||||||
|
# , "OR"
|
||||||
|
, "Log(Odds Ratio)"
|
||||||
|
# , "P-value"
|
||||||
|
, "-LogP"
|
||||||
|
, "Allele Frequency"
|
||||||
|
, "DUET_outcome"
|
||||||
|
# , "Lig_outcome"
|
||||||
|
, "pyrazinamide")
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
if (length(my_corr_colnames) == length(corr_data)){
|
||||||
|
print("Sanity check passed: corr_data and corr_names match in length")
|
||||||
|
}else{
|
||||||
|
print("Error: length mismatch!")
|
||||||
|
}
|
||||||
|
|
||||||
|
colnames(corr_data)
|
||||||
|
colnames(corr_data) <- my_corr_colnames
|
||||||
|
colnames(corr_data)
|
||||||
|
|
||||||
|
###############
|
||||||
|
# PLOTS: corr
|
||||||
|
# http://www.sthda.com/english/wiki/scatter-plot-matrices-r-base-graphs
|
||||||
|
###############
|
||||||
|
#default pairs plot
|
||||||
|
start = 1
|
||||||
|
end = which(colnames(corr_data) == "pyrazinamide"); end # should be the last column
|
||||||
|
offset = 1
|
||||||
|
|
||||||
|
my_corr = corr_data[start:(end-offset)]
|
||||||
|
head(my_corr)
|
||||||
|
|
||||||
|
#my_cols = c("#f8766d", "#00bfc4")
|
||||||
|
# deep blue :#007d85
|
||||||
|
# deep red: #ae301e
|
||||||
|
|
||||||
|
#==========
|
||||||
|
# psych: ionformative since it draws the ellipsoid
|
||||||
|
# https://jamesmarquezportfolio.com/correlation_matrices_in_r.html
|
||||||
|
# http://www.sthda.com/english/wiki/scatter-plot-matrices-r-base-graphs
|
||||||
|
#==========
|
||||||
|
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots"
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
svg('DUET_corr.svg', width = 15, height = 15)
|
||||||
|
printFile = pairs.panels(my_corr[1:4]
|
||||||
|
, method = "spearman" # correlation method
|
||||||
|
, hist.col = "grey" ##00AFBB
|
||||||
|
, density = TRUE # show density plots
|
||||||
|
, ellipses = F # show correlation ellipses
|
||||||
|
, stars = T
|
||||||
|
, rug = F
|
||||||
|
, breaks = "Sturges"
|
||||||
|
, show.points = T
|
||||||
|
, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr$DUET_outcome))]
|
||||||
|
, pch = 21
|
||||||
|
, jitter = T
|
||||||
|
#, alpha = .05
|
||||||
|
#, points(pch = 19, col = c("#f8766d", "#00bfc4"))
|
||||||
|
, cex = 3
|
||||||
|
, cex.axis = 2.5
|
||||||
|
, cex.labels = 3
|
||||||
|
, cex.cor = 1
|
||||||
|
, smooth = F
|
||||||
|
)
|
||||||
|
|
||||||
|
print(printFile)
|
||||||
|
dev.off()
|
187
mcsm_analysis/pyrazinamide/scripts/plotting/corr_plots_v3_lig.R
Normal file
187
mcsm_analysis/pyrazinamide/scripts/plotting/corr_plots_v3_lig.R
Normal file
|
@ -0,0 +1,187 @@
|
||||||
|
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")
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for lig #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df_lig.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for Lig Corr plots
|
||||||
|
# you need merged_df3_comp
|
||||||
|
# since these are matched
|
||||||
|
# to allow pairwise corr
|
||||||
|
###########################
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
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)
|
||||||
|
|
||||||
|
#############################
|
||||||
|
# Extra sanity check:
|
||||||
|
# for mcsm_lig ONLY
|
||||||
|
# Dis_lig_Ang should be <10
|
||||||
|
#############################
|
||||||
|
|
||||||
|
if (max(my_df$Dis_lig_Ang) < 10){
|
||||||
|
print ("Sanity check passed: lig data is <10Ang")
|
||||||
|
}else{
|
||||||
|
print ("Error: data should be filtered to be within 10Ang")
|
||||||
|
}
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#===========================
|
||||||
|
# Plot: Correlation plots
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df = my_df
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
rm(my_df)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
str(df)
|
||||||
|
|
||||||
|
table(df$Lig_outcome)
|
||||||
|
|
||||||
|
# unique positions
|
||||||
|
length(unique(df$Position)) #{RESULT: unique positions for comp data}
|
||||||
|
|
||||||
|
# subset data to generate pairwise correlations
|
||||||
|
corr_data = df[, c(#"ratioDUET",
|
||||||
|
"ratioPredAff"
|
||||||
|
# , "DUETStability_Kcalpermol"
|
||||||
|
# , "PredAffLog"
|
||||||
|
# , "OR"
|
||||||
|
, "logor"
|
||||||
|
# , "pvalue"
|
||||||
|
, "neglog10pvalue"
|
||||||
|
, "AF"
|
||||||
|
# , "DUET_outcome"
|
||||||
|
, "Lig_outcome"
|
||||||
|
, "pyrazinamide"
|
||||||
|
)]
|
||||||
|
dim(corr_data)
|
||||||
|
rm(df)
|
||||||
|
|
||||||
|
# assign nice colnames (for display)
|
||||||
|
my_corr_colnames = c(#"DUET",
|
||||||
|
"Ligand Affinity"
|
||||||
|
# ,"DUET_raw"
|
||||||
|
# , "Lig_raw"
|
||||||
|
# , "OR"
|
||||||
|
, "Log(Odds Ratio)"
|
||||||
|
# , "P-value"
|
||||||
|
, "-LogP"
|
||||||
|
, "Allele Frequency"
|
||||||
|
# , "DUET_outcome"
|
||||||
|
, "Lig_outcome"
|
||||||
|
, "pyrazinamide")
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
if (length(my_corr_colnames) == length(corr_data)){
|
||||||
|
print("Sanity check passed: corr_data and corr_names match in length")
|
||||||
|
}else{
|
||||||
|
print("Error: length mismatch!")
|
||||||
|
}
|
||||||
|
|
||||||
|
colnames(corr_data)
|
||||||
|
colnames(corr_data) <- my_corr_colnames
|
||||||
|
colnames(corr_data)
|
||||||
|
|
||||||
|
###############
|
||||||
|
# PLOTS: corr
|
||||||
|
# http://www.sthda.com/english/wiki/scatter-plot-matrices-r-base-graphs
|
||||||
|
###############
|
||||||
|
|
||||||
|
# default pairs plot
|
||||||
|
start = 1
|
||||||
|
end = which(colnames(corr_data) == "pyrazinamide"); end # should be the last column
|
||||||
|
offset = 1
|
||||||
|
|
||||||
|
my_corr = corr_data[start:(end-offset)]
|
||||||
|
head(my_corr)
|
||||||
|
|
||||||
|
#my_cols = c("#f8766d", "#00bfc4")
|
||||||
|
# deep blue :#007d85
|
||||||
|
# deep red: #ae301e
|
||||||
|
|
||||||
|
#==========
|
||||||
|
# psych: ionformative since it draws the ellipsoid
|
||||||
|
# https://jamesmarquezportfolio.com/correlation_matrices_in_r.html
|
||||||
|
# http://www.sthda.com/english/wiki/scatter-plot-matrices-r-base-graphs
|
||||||
|
#==========
|
||||||
|
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots"
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
svg('Lig_corr.svg', width = 15, height = 15)
|
||||||
|
printFile = pairs.panels(my_corr[1:4]
|
||||||
|
, method = "spearman" # correlation method
|
||||||
|
, hist.col = "grey" ##00AFBB
|
||||||
|
, density = TRUE # show density plots
|
||||||
|
, ellipses = F # show correlation ellipses
|
||||||
|
, stars = T
|
||||||
|
, rug = F
|
||||||
|
, breaks = "Sturges"
|
||||||
|
, show.points = T
|
||||||
|
, bg = c("#f8766d", "#00bfc4")[unclass(factor(my_corr$Lig_outcome))]
|
||||||
|
, pch = 21
|
||||||
|
, jitter = T
|
||||||
|
# , alpha = .05
|
||||||
|
# , points(pch = 19, col = c("#f8766d", "#00bfc4"))
|
||||||
|
, cex = 3
|
||||||
|
, cex.axis = 2.5
|
||||||
|
, cex.labels = 3
|
||||||
|
, cex.cor = 1
|
||||||
|
, smooth = F
|
||||||
|
)
|
||||||
|
print(printFile)
|
||||||
|
dev.off()
|
||||||
|
|
|
@ -0,0 +1,227 @@
|
||||||
|
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)
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#==========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for plots
|
||||||
|
# you need merged_df2, comprehensive one
|
||||||
|
# since this has one-many relationship
|
||||||
|
# i.e the same SNP can belong to multiple lineages
|
||||||
|
###########################
|
||||||
|
|
||||||
|
# uncomment as necessary
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
my_df = merged_df2
|
||||||
|
#my_df = merged_df2_comp
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
# delete variables not required
|
||||||
|
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||||
|
|
||||||
|
# quick checks
|
||||||
|
colnames(my_df)
|
||||||
|
str(my_df)
|
||||||
|
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
is.factor(my_df$lineage)
|
||||||
|
my_df$lineage = as.factor(my_df$lineage)
|
||||||
|
is.factor(my_df$lineage)
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# Plot: Lineage barplot
|
||||||
|
# x = lineage y = No. of samples
|
||||||
|
# col = Lineage
|
||||||
|
# fill = lineage
|
||||||
|
#============================
|
||||||
|
table(my_df$lineage)
|
||||||
|
|
||||||
|
# lineage1 lineage2 lineage3 lineage4 lineage5 lineage6 lineageBOV
|
||||||
|
#3 104 1293 264 1311 6 6 105
|
||||||
|
|
||||||
|
#===========================
|
||||||
|
# Plot: Lineage Barplots
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df <- my_df
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
rm(my_df)
|
||||||
|
|
||||||
|
# get freq count of positions so you can subset freq<1
|
||||||
|
#setDT(df)[, lineage_count := .N, by = .(lineage)]
|
||||||
|
|
||||||
|
#******************
|
||||||
|
# generate plot: barplot of mutation by lineage
|
||||||
|
#******************
|
||||||
|
sel_lineages = c("lineage1"
|
||||||
|
, "lineage2"
|
||||||
|
, "lineage3"
|
||||||
|
, "lineage4")
|
||||||
|
|
||||||
|
df_lin = subset(df, subset = lineage %in% sel_lineages )
|
||||||
|
|
||||||
|
#FIXME; add sanity check for numbers.
|
||||||
|
# Done this manually
|
||||||
|
|
||||||
|
############################################################
|
||||||
|
|
||||||
|
#########
|
||||||
|
# Data for barplot: Lineage barplot
|
||||||
|
# to show total samples and number of unique mutations
|
||||||
|
# within each linege
|
||||||
|
##########
|
||||||
|
|
||||||
|
# Create df with lineage inform & no. of unique mutations
|
||||||
|
# per lineage and total samples within lineage
|
||||||
|
# this is essentially barplot with two y axis
|
||||||
|
|
||||||
|
bar = bar = as.data.frame(sel_lineages) #4, 1
|
||||||
|
total_snps_u = NULL
|
||||||
|
total_samples = NULL
|
||||||
|
|
||||||
|
for (i in sel_lineages){
|
||||||
|
#print(i)
|
||||||
|
curr_total = length(unique(df$id)[df$lineage==i])
|
||||||
|
total_samples = c(total_samples, curr_total)
|
||||||
|
print(total_samples)
|
||||||
|
|
||||||
|
foo = df[df$lineage==i,]
|
||||||
|
print(paste0(i, "======="))
|
||||||
|
print(length(unique(foo$Mutationinformation)))
|
||||||
|
curr_count = length(unique(foo$Mutationinformation))
|
||||||
|
|
||||||
|
total_snps_u = c(total_snps_u, curr_count)
|
||||||
|
}
|
||||||
|
|
||||||
|
print(total_snps_u)
|
||||||
|
bar$num_snps_u = total_snps_u
|
||||||
|
bar$total_samples = total_samples
|
||||||
|
bar
|
||||||
|
|
||||||
|
#*****************
|
||||||
|
# generate plot: lineage barplot with two y-axis
|
||||||
|
#https://stackoverflow.com/questions/13035295/overlay-bar-graphs-in-ggplot2
|
||||||
|
#*****************
|
||||||
|
|
||||||
|
bar$num_snps_u = y1
|
||||||
|
bar$total_samples = y2
|
||||||
|
sel_lineages = x
|
||||||
|
|
||||||
|
to_plot = data.frame(x = x
|
||||||
|
, y1 = y1
|
||||||
|
, y2 = y2)
|
||||||
|
to_plot
|
||||||
|
|
||||||
|
melted = melt(to_plot, id = "x")
|
||||||
|
melted
|
||||||
|
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
svg('lineage_basic_barplot.svg')
|
||||||
|
|
||||||
|
my_ats = 20 # axis text size
|
||||||
|
my_als = 22 # axis label size
|
||||||
|
|
||||||
|
g = ggplot(melted
|
||||||
|
, aes(x = x
|
||||||
|
, y = value
|
||||||
|
, fill = variable)
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
printFile = g + geom_bar(
|
||||||
|
|
||||||
|
#g + geom_bar(
|
||||||
|
stat = "identity"
|
||||||
|
, position = position_stack(reverse = TRUE)
|
||||||
|
, alpha=.75
|
||||||
|
, colour='grey75'
|
||||||
|
) + theme(
|
||||||
|
axis.text.x = element_text(
|
||||||
|
size = my_ats
|
||||||
|
# , angle= 30
|
||||||
|
)
|
||||||
|
, axis.text.y = element_text(size = my_ats
|
||||||
|
#, angle = 30
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0)
|
||||||
|
, axis.title.x = element_text(
|
||||||
|
size = my_als
|
||||||
|
, colour = 'black'
|
||||||
|
)
|
||||||
|
, axis.title.y = element_text(
|
||||||
|
size = my_als
|
||||||
|
, colour = 'black'
|
||||||
|
)
|
||||||
|
, legend.position = "top"
|
||||||
|
, legend.text = element_text(size = my_als)
|
||||||
|
|
||||||
|
#) + geom_text(
|
||||||
|
) + geom_label(
|
||||||
|
aes(label = value)
|
||||||
|
, size = 5
|
||||||
|
, hjust = 0.5
|
||||||
|
, vjust = 0.5
|
||||||
|
, colour = 'black'
|
||||||
|
, show.legend = FALSE
|
||||||
|
#, check_overlap = TRUE
|
||||||
|
, position = position_stack(reverse = T)
|
||||||
|
#, position = ('
|
||||||
|
|
||||||
|
) + labs(
|
||||||
|
title = ''
|
||||||
|
, x = ''
|
||||||
|
, y = "Number"
|
||||||
|
, fill = 'Variable'
|
||||||
|
, colour = 'black'
|
||||||
|
) + scale_fill_manual(
|
||||||
|
values = c('grey50', 'gray75')
|
||||||
|
, name=''
|
||||||
|
, labels=c('Mutations', 'Total Samples')
|
||||||
|
) + scale_x_discrete(
|
||||||
|
breaks = c('lineage1', 'lineage2', 'lineage3', 'lineage4')
|
||||||
|
, labels = c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4')
|
||||||
|
)
|
||||||
|
print(printFile)
|
||||||
|
dev.off()
|
233
mcsm_analysis/pyrazinamide/scripts/plotting/lineage_dist_LIG.R
Normal file
233
mcsm_analysis/pyrazinamide/scripts/plotting/lineage_dist_LIG.R
Normal file
|
@ -0,0 +1,233 @@
|
||||||
|
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)
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for Lig #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df_lig.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#===========================
|
||||||
|
###########################
|
||||||
|
# Data for plots
|
||||||
|
# you need merged_df2 or merged_df2_comp
|
||||||
|
# since this is one-many relationship
|
||||||
|
# i.e the same SNP can belong to multiple lineages
|
||||||
|
###########################
|
||||||
|
|
||||||
|
# uncomment as necessary
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
my_df = merged_df2
|
||||||
|
#my_df = merged_df2_comp
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
# delete variables not required
|
||||||
|
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||||
|
|
||||||
|
# quick checks
|
||||||
|
colnames(my_df)
|
||||||
|
str(my_df)
|
||||||
|
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
is.factor(my_df$lineage)
|
||||||
|
my_df$lineage = as.factor(my_df$lineage)
|
||||||
|
is.factor(my_df$lineage)
|
||||||
|
|
||||||
|
table(my_df$mutation_info)
|
||||||
|
|
||||||
|
#############################
|
||||||
|
# Extra sanity check:
|
||||||
|
# for mcsm_lig ONLY
|
||||||
|
# Dis_lig_Ang should be <10
|
||||||
|
#############################
|
||||||
|
|
||||||
|
if (max(my_df$Dis_lig_Ang) < 10){
|
||||||
|
print ("Sanity check passed: lig data is <10Ang")
|
||||||
|
}else{
|
||||||
|
print ("Error: data should be filtered to be within 10Ang")
|
||||||
|
}
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# Plot: Lineage Distribution
|
||||||
|
# x = mcsm_values, y = dist
|
||||||
|
# fill = stability
|
||||||
|
#============================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
# subset only lineages1-4
|
||||||
|
sel_lineages = c("lineage1"
|
||||||
|
, "lineage2"
|
||||||
|
, "lineage3"
|
||||||
|
, "lineage4")
|
||||||
|
|
||||||
|
# uncomment as necessary
|
||||||
|
df_lin = subset(my_df, subset = lineage %in% sel_lineages ) #2037 35
|
||||||
|
|
||||||
|
# refactor
|
||||||
|
df_lin$lineage = factor(df_lin$lineage)
|
||||||
|
|
||||||
|
table(df_lin$lineage) #{RESULT: No of samples within lineage}
|
||||||
|
#lineage1 lineage2 lineage3 lineage4
|
||||||
|
#78 961 195 803
|
||||||
|
|
||||||
|
# when merged_df2_comp is used
|
||||||
|
#lineage1 lineage2 lineage3 lineage4
|
||||||
|
#77 955 194 770
|
||||||
|
|
||||||
|
length(unique(df_lin$Mutationinformation))
|
||||||
|
#{Result: No. of unique mutations the 4 lineages contribute to}
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
r1 = 2:5 # when merged_df2 used: because there is missing lineages
|
||||||
|
if(sum(table(my_df$lineage)[r1]) == nrow(df_lin)) {
|
||||||
|
print ("sanity check passed: numbers match")
|
||||||
|
} else{
|
||||||
|
print("Error!: check your numbers")
|
||||||
|
}
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df <- df_lin
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
rm(df_lin)
|
||||||
|
|
||||||
|
#******************
|
||||||
|
# generate distribution plot of lineages
|
||||||
|
#******************
|
||||||
|
# basic: could improve this!
|
||||||
|
library(plotly)
|
||||||
|
library(ggridges)
|
||||||
|
|
||||||
|
fooNames = c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4')
|
||||||
|
names(fooNames) = c('lineage1', 'lineage2', 'lineage3', 'lineage4')
|
||||||
|
|
||||||
|
g <- ggplot(df, aes(x = ratioPredAff)) +
|
||||||
|
geom_density(aes(fill = Lig_outcome)
|
||||||
|
, alpha = 0.5) +
|
||||||
|
facet_wrap( ~ lineage
|
||||||
|
, scales = "free"
|
||||||
|
, labeller = labeller(lineage = fooNames) ) +
|
||||||
|
coord_cartesian(xlim = c(-1, 1)
|
||||||
|
# , ylim = c(0, 6)
|
||||||
|
# , clip = "off"
|
||||||
|
)
|
||||||
|
ggtitle("Kernel Density estimates of Ligand affinity by lineage")
|
||||||
|
|
||||||
|
ggplotly(g)
|
||||||
|
|
||||||
|
# 2 : ggridges (good!)
|
||||||
|
|
||||||
|
my_ats = 15 # axis text size
|
||||||
|
my_als = 20 # axis label size
|
||||||
|
|
||||||
|
fooNames = c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4')
|
||||||
|
names(fooNames) = c('lineage1', 'lineage2', 'lineage3', 'lineage4')
|
||||||
|
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
svg('lineage_dist_LIG.svg')
|
||||||
|
|
||||||
|
printFile = ggplot( df, aes(x = ratioPredAff
|
||||||
|
, y = Lig_outcome) ) +
|
||||||
|
|
||||||
|
geom_density_ridges_gradient( aes(fill = ..x..)
|
||||||
|
, scale = 3
|
||||||
|
, size = 0.3 ) +
|
||||||
|
facet_wrap( ~lineage
|
||||||
|
, scales = "free"
|
||||||
|
# , switch = 'x'
|
||||||
|
, labeller = labeller(lineage = fooNames) ) +
|
||||||
|
coord_cartesian( xlim = c(-1, 1)
|
||||||
|
# , ylim = c(0, 6)
|
||||||
|
# , clip = "off"
|
||||||
|
) +
|
||||||
|
|
||||||
|
scale_fill_gradientn( colours = c("#f8766d", "white", "#00bfc4")
|
||||||
|
, name = "Ligand Affinity" ) +
|
||||||
|
theme( axis.text.x = element_text( size = my_ats
|
||||||
|
, angle = 90
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0.4)
|
||||||
|
# , axis.text.y = element_text( size = my_ats
|
||||||
|
# , angle = 0
|
||||||
|
# , hjust = 1
|
||||||
|
# , vjust = 0)
|
||||||
|
, axis.text.y = element_blank()
|
||||||
|
, axis.title.x = element_blank()
|
||||||
|
, axis.title.y = element_blank()
|
||||||
|
, axis.ticks.y = element_blank()
|
||||||
|
, plot.title = element_blank()
|
||||||
|
, strip.text = element_text(size = my_als)
|
||||||
|
, legend.text = element_text(size = 10)
|
||||||
|
, legend.title = element_text(size = my_als)
|
||||||
|
# , legend.position = c(0.3, 0.8)
|
||||||
|
# , legend.key.height = unit(1, 'mm')
|
||||||
|
)
|
||||||
|
|
||||||
|
print(printFile)
|
||||||
|
dev.off()
|
||||||
|
|
||||||
|
#=!=!=!=!=!=!
|
||||||
|
# COMMENT: When you look at all mutations, the lineage differences disappear...
|
||||||
|
# The pattern we are interested in is possibly only for dr_mutations
|
||||||
|
#=!=!=!=!=!=!
|
||||||
|
|
||||||
|
#===================================================
|
||||||
|
|
||||||
|
# COMPARING DISTRIBUTIONS
|
||||||
|
head(df$lineage)
|
||||||
|
df$lineage = as.character(df$lineage)
|
||||||
|
|
||||||
|
lin1 = df[df$lineage == "lineage1",]$ratioPredAff
|
||||||
|
lin2 = df[df$lineage == "lineage2",]$ratioPredAff
|
||||||
|
lin3 = df[df$lineage == "lineage3",]$ratioPredAff
|
||||||
|
lin4 = df[df$lineage == "lineage4",]$ratioPredAff
|
||||||
|
|
||||||
|
# ks test
|
||||||
|
ks.test(lin1,lin2)
|
||||||
|
ks.test(lin1,lin3)
|
||||||
|
ks.test(lin1,lin4)
|
||||||
|
|
||||||
|
ks.test(lin2,lin3)
|
||||||
|
ks.test(lin2,lin4)
|
||||||
|
|
||||||
|
ks.test(lin3,lin4)
|
||||||
|
|
||||||
|
|
||||||
|
|
212
mcsm_analysis/pyrazinamide/scripts/plotting/lineage_dist_PS.R
Normal file
212
mcsm_analysis/pyrazinamide/scripts/plotting/lineage_dist_PS.R
Normal file
|
@ -0,0 +1,212 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting") # thinkpad
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Installing and loading required packages #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../Header_TT.R")
|
||||||
|
#source("barplot_colour_function.R")
|
||||||
|
#require(data.table)
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# Read file: call script for combining df for PS #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
source("../combining_two_df.R")
|
||||||
|
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
# the above changes the working dir
|
||||||
|
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
|
||||||
|
#---------------------- PAY ATTENTION
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# This will return:
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp
|
||||||
|
# merged_df3_comp
|
||||||
|
#===========================
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# Data for plots
|
||||||
|
# you need merged_df2 or merged_df2_comp
|
||||||
|
# since this is one-many relationship
|
||||||
|
# i.e the same SNP can belong to multiple lineages
|
||||||
|
###########################
|
||||||
|
|
||||||
|
# uncomment as necessary
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
my_df = merged_df2
|
||||||
|
#my_df = merged_df2_comp
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
# delete variables not required
|
||||||
|
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||||
|
|
||||||
|
# quick checks
|
||||||
|
colnames(my_df)
|
||||||
|
str(my_df)
|
||||||
|
|
||||||
|
# Ensure correct data type in columns to plot: need to be factor
|
||||||
|
is.factor(my_df$lineage)
|
||||||
|
my_df$lineage = as.factor(my_df$lineage)
|
||||||
|
is.factor(my_df$lineage)
|
||||||
|
|
||||||
|
table(my_df$mutation_info)
|
||||||
|
|
||||||
|
########################################################################
|
||||||
|
# end of data extraction and cleaning for plots #
|
||||||
|
########################################################################
|
||||||
|
|
||||||
|
#==========================
|
||||||
|
# Plot: Lineage Distribution
|
||||||
|
# x = mcsm_values, y = dist
|
||||||
|
# fill = stability
|
||||||
|
#============================
|
||||||
|
|
||||||
|
#===================
|
||||||
|
# Data for plots
|
||||||
|
#===================
|
||||||
|
|
||||||
|
# subset only lineages1-4
|
||||||
|
sel_lineages = c("lineage1"
|
||||||
|
, "lineage2"
|
||||||
|
, "lineage3"
|
||||||
|
, "lineage4")
|
||||||
|
|
||||||
|
# uncomment as necessary
|
||||||
|
df_lin = subset(my_df, subset = lineage %in% sel_lineages )
|
||||||
|
|
||||||
|
# refactor
|
||||||
|
df_lin$lineage = factor(df_lin$lineage)
|
||||||
|
|
||||||
|
table(df_lin$lineage) #{RESULT: No of samples within lineage}
|
||||||
|
#lineage1 lineage2 lineage3 lineage4
|
||||||
|
#104 1293 264 1311
|
||||||
|
|
||||||
|
# when merged_df2_comp is used
|
||||||
|
#lineage1 lineage2 lineage3 lineage4
|
||||||
|
#99 1275 263 1255
|
||||||
|
|
||||||
|
length(unique(df_lin$Mutationinformation))
|
||||||
|
#{Result: No. of unique mutations the 4 lineages contribute to}
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
r1 = 2:5 # when merged_df2 used: because there is missing lineages
|
||||||
|
if(sum(table(my_df$lineage)[r1]) == nrow(df_lin)) {
|
||||||
|
print ("sanity check passed: numbers match")
|
||||||
|
} else{
|
||||||
|
print("Error!: check your numbers")
|
||||||
|
}
|
||||||
|
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
# REASSIGNMENT
|
||||||
|
df <- df_lin
|
||||||
|
#<<<<<<<<<<<<<<<<<<<<<<<<<
|
||||||
|
|
||||||
|
rm(df_lin)
|
||||||
|
|
||||||
|
#******************
|
||||||
|
# generate distribution plot of lineages
|
||||||
|
#******************
|
||||||
|
# basic: could improve this!
|
||||||
|
library(plotly)
|
||||||
|
library(ggridges)
|
||||||
|
|
||||||
|
g <- ggplot(df, aes(x = ratioDUET)) +
|
||||||
|
geom_density(aes(fill = DUET_outcome)
|
||||||
|
, alpha = 0.5) + facet_wrap(~ lineage,
|
||||||
|
scales = "free") +
|
||||||
|
ggtitle("Kernel Density estimates of Protein stability by lineage")
|
||||||
|
|
||||||
|
ggplotly(g)
|
||||||
|
|
||||||
|
# 2 : ggridges (good!)
|
||||||
|
|
||||||
|
my_ats = 15 # axis text size
|
||||||
|
my_als = 20 # axis label size
|
||||||
|
|
||||||
|
fooNames=c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4')
|
||||||
|
names(fooNames)=c('lineage1', 'lineage2', 'lineage3', 'lineage4')
|
||||||
|
|
||||||
|
# set output dir for plots
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/Data/pyrazinamide/output/plots")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
svg('lineage_dist_PS.svg')
|
||||||
|
|
||||||
|
printFile = ggplot( df, aes(x = ratioDUET
|
||||||
|
, y = DUET_outcome) )+
|
||||||
|
|
||||||
|
#printFile=geom_density_ridges_gradient(
|
||||||
|
geom_density_ridges_gradient( aes(fill = ..x..)
|
||||||
|
, scale = 3
|
||||||
|
, size = 0.3 ) +
|
||||||
|
facet_wrap( ~lineage
|
||||||
|
, scales = "free"
|
||||||
|
# , switch = 'x'
|
||||||
|
, labeller = labeller(lineage = fooNames) ) +
|
||||||
|
coord_cartesian( xlim = c(-1, 1)
|
||||||
|
# , ylim = c(0, 6)
|
||||||
|
# , clip = "off"
|
||||||
|
) +
|
||||||
|
scale_fill_gradientn( colours = c("#f8766d", "white", "#00bfc4")
|
||||||
|
, name = "DUET" ) +
|
||||||
|
theme( axis.text.x = element_text( size = my_ats
|
||||||
|
, angle = 90
|
||||||
|
, hjust = 1
|
||||||
|
, vjust = 0.4)
|
||||||
|
# , axis.text.y = element_text( size = my_ats
|
||||||
|
# , angle = 0
|
||||||
|
# , hjust = 1
|
||||||
|
# , vjust = 0)
|
||||||
|
, axis.text.y = element_blank()
|
||||||
|
, axis.title.x = element_blank()
|
||||||
|
, axis.title.y = element_blank()
|
||||||
|
, axis.ticks.y = element_blank()
|
||||||
|
, plot.title = element_blank()
|
||||||
|
, strip.text = element_text(size=my_als)
|
||||||
|
, legend.text = element_text(size=10)
|
||||||
|
, legend.title = element_text(size=my_als)
|
||||||
|
# , legend.position = c(0.3, 0.8)
|
||||||
|
# , legend.key.height = unit(1, 'mm')
|
||||||
|
)
|
||||||
|
|
||||||
|
print(printFile)
|
||||||
|
dev.off()
|
||||||
|
|
||||||
|
#=!=!=!=!=!=!
|
||||||
|
# COMMENT: When you look at all mutations, the lineage differences disappear...
|
||||||
|
# The pattern we are interested in is possibly only for dr_mutations
|
||||||
|
#=!=!=!=!=!=!
|
||||||
|
#===================================================
|
||||||
|
|
||||||
|
# COMPARING DISTRIBUTIONS
|
||||||
|
head(df$lineage)
|
||||||
|
df$lineage = as.character(df$lineage)
|
||||||
|
|
||||||
|
lin1 = df[df$lineage == "lineage1",]$ratioDUET
|
||||||
|
lin2 = df[df$lineage == "lineage2",]$ratioDUET
|
||||||
|
lin3 = df[df$lineage == "lineage3",]$ratioDUET
|
||||||
|
lin4 = df[df$lineage == "lineage4",]$ratioDUET
|
||||||
|
|
||||||
|
# ks test
|
||||||
|
ks.test(lin1,lin2)
|
||||||
|
ks.test(lin1,lin3)
|
||||||
|
ks.test(lin1,lin4)
|
||||||
|
|
||||||
|
ks.test(lin2,lin3)
|
||||||
|
ks.test(lin2,lin4)
|
||||||
|
|
||||||
|
ks.test(lin3,lin4)
|
||||||
|
|
||||||
|
|
||||||
|
|
27
mcsm_analysis/pyrazinamide/scripts/read_pdb.R
Normal file
27
mcsm_analysis/pyrazinamide/scripts/read_pdb.R
Normal file
|
@ -0,0 +1,27 @@
|
||||||
|
#########################
|
||||||
|
#3: Read complex pdb file
|
||||||
|
##########################
|
||||||
|
source("Header_TT.R")
|
||||||
|
# This script only reads the pdb file of your complex
|
||||||
|
|
||||||
|
# read in pdb file complex1
|
||||||
|
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)
|
||||||
|
#====== end of script
|
||||||
|
|
386
mcsm_analysis/pyrazinamide/scripts/replaceBfactor_pdb.R
Normal file
386
mcsm_analysis/pyrazinamide/scripts/replaceBfactor_pdb.R
Normal file
|
@ -0,0 +1,386 @@
|
||||||
|
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)
|
||||||
|
|
||||||
|
#=========================================================
|
||||||
|
# Processing P1: Replacing B factor with mean ratioDUET scores
|
||||||
|
#=========================================================
|
||||||
|
|
||||||
|
#########################
|
||||||
|
# Read complex pdb file
|
||||||
|
# form the R script
|
||||||
|
##########################
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
#*******************************************
|
||||||
|
# plot histograms for inspection
|
||||||
|
# 1: original B-factors
|
||||||
|
# 2: original DUET Scores
|
||||||
|
# 3: replaced B-factors with DUET Scores
|
||||||
|
#*********************************************
|
||||||
|
# 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")
|
||||||
|
|
||||||
|
# 3: After the following replacement
|
||||||
|
#********************************
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# step 0_P1: DONT RUN once you have double checked the matched output
|
||||||
|
#=========
|
||||||
|
# sanity check: match and assign to a separate column to double check
|
||||||
|
# colnames(my_df)
|
||||||
|
# d$ratioDUET = my_df$averge_DUETR[match(d$resno, my_df$Position)]
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# 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)
|
||||||
|
#table(d$b)
|
||||||
|
|
||||||
|
# replace all NA in b factor with 0
|
||||||
|
d$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$b)
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# step 5_P1
|
||||||
|
#=========
|
||||||
|
# output dir
|
||||||
|
getwd()
|
||||||
|
outDir = "~/git/Data/pyrazinamide/input/structure/"
|
||||||
|
|
||||||
|
outFile = paste0(outDir, "complex1_BwithNormDUET.pdb"); outFile
|
||||||
|
write.pdb(my_pdb, outFile)
|
||||||
|
|
||||||
|
#********************************
|
||||||
|
# Add the 3rd histogram and density plots for comparisons
|
||||||
|
#********************************
|
||||||
|
# Plots continued...
|
||||||
|
# 3: hist and density of replaced B-factors with DUET Scores
|
||||||
|
hist(d$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)
|
||||||
|
#********************************
|
||||||
|
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
# NOTE: This replaced B-factor distribution has the same
|
||||||
|
# x-axis as the PredAff normalised values, but the distribution
|
||||||
|
# is affected since 0 is overinflated. This is because all the positions
|
||||||
|
# where there are no SNPs have been assigned 0.
|
||||||
|
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#######################################################################
|
||||||
|
#====================== end of section 1 ==============================
|
||||||
|
#######################################################################
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#=========================================================
|
||||||
|
# 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(inFile
|
||||||
|
# , row.names = 1
|
||||||
|
# , stringsAsFactors = F
|
||||||
|
, header = T)
|
||||||
|
str(my_df)
|
||||||
|
#rm(inDir, inFile)
|
||||||
|
|
||||||
|
#########################
|
||||||
|
# 3: Read complex pdb file
|
||||||
|
# form the R script
|
||||||
|
##########################
|
||||||
|
|
||||||
|
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$b)
|
||||||
|
|
||||||
|
#*******************************************
|
||||||
|
# plot histograms for inspection
|
||||||
|
# 1: original B-factors
|
||||||
|
# 2: original Pred Aff Scores
|
||||||
|
# 3: replaced B-factors with PredAff Scores
|
||||||
|
#********************************************
|
||||||
|
# Set the margin on all sides
|
||||||
|
par(oma = c(3,2,3,0)
|
||||||
|
, mar = c(1,3,5,2)
|
||||||
|
, mfrow = c(3,2))
|
||||||
|
#par(mfrow = c(3,2))
|
||||||
|
|
||||||
|
# 1: Original B-factor
|
||||||
|
hist(d$b
|
||||||
|
, xlab = ""
|
||||||
|
, main = "B-factor")
|
||||||
|
|
||||||
|
plot(density(d$b)
|
||||||
|
, xlab = ""
|
||||||
|
, main = "B-factor")
|
||||||
|
|
||||||
|
# 2: Pred Aff scores
|
||||||
|
hist(my_df$average_PredAffR
|
||||||
|
, xlab = ""
|
||||||
|
, main = "Norm_lig_average")
|
||||||
|
|
||||||
|
plot(density(my_df$average_PredAffR)
|
||||||
|
, xlab = ""
|
||||||
|
, main = "Norm_lig_average")
|
||||||
|
|
||||||
|
# 3: After the following replacement
|
||||||
|
#********************************
|
||||||
|
|
||||||
|
#=================================================
|
||||||
|
# Processing P2: Replacing B values with ratioPredAff scores
|
||||||
|
#=================================================
|
||||||
|
# use match to perform this replacement linking with "position no"
|
||||||
|
# in the pdb file, this corresponds to column "resno"
|
||||||
|
# in my_df, this corresponds to column "Position"
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# step 0_P2: DONT RUN once you have double checked the matched output
|
||||||
|
#=========
|
||||||
|
# sanity check: match and assign to a separate column to double check
|
||||||
|
# colnames(my_df)
|
||||||
|
# d$ratioPredAff = my_df$average_PredAffR[match(d$resno, my_df$Position)] #1384, 17
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# step 1_P2: BE BRAVE and replace in place now (don't run step 0)
|
||||||
|
#=========
|
||||||
|
# this makes all the B-factor values in the non-matched positions as NA
|
||||||
|
d$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$b == 0)
|
||||||
|
#table(d$b)
|
||||||
|
|
||||||
|
# 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
|
||||||
|
#=========
|
||||||
|
|
||||||
|
# output dir
|
||||||
|
outDir = "~/git/Data/pyrazinamide/input/structure/"
|
||||||
|
outFile = paste0(outDir, "complex1_BwithNormLIG.pdb"); outFile
|
||||||
|
write.pdb(my_pdb, outFile)
|
||||||
|
|
||||||
|
#********************************
|
||||||
|
# Add the 3rd histogram and density plots for comparisons
|
||||||
|
#********************************
|
||||||
|
# Plots continued...
|
||||||
|
# 3: hist and density of replaced B-factors with PredAff Scores
|
||||||
|
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 = "Lig_stability"
|
||||||
|
, side = 3
|
||||||
|
, line = 0
|
||||||
|
, outer = TRUE)
|
||||||
|
|
||||||
|
#********************************
|
||||||
|
|
||||||
|
###########
|
||||||
|
# end of output files with Bfactors
|
||||||
|
##########
|
257
mcsm_analysis/pyrazinamide/scripts/source_data_checks.R
Normal file
257
mcsm_analysis/pyrazinamide/scripts/source_data_checks.R
Normal file
|
@ -0,0 +1,257 @@
|
||||||
|
getwd()
|
||||||
|
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
#########################################################
|
||||||
|
# 1: Installing and loading required packages #
|
||||||
|
#########################################################
|
||||||
|
|
||||||
|
source("Header_TT.R")
|
||||||
|
#source("barplot_colour_function.R")
|
||||||
|
|
||||||
|
##########################################################
|
||||||
|
# Checking: Entire data frame and for PS #
|
||||||
|
##########################################################
|
||||||
|
|
||||||
|
###########################
|
||||||
|
#2) Read file: combined one from the script
|
||||||
|
###########################
|
||||||
|
source("combining_two_df.R")
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2
|
||||||
|
# merged_df3:
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp:
|
||||||
|
# merged_df3_comp:
|
||||||
|
|
||||||
|
######################
|
||||||
|
# You need to check it
|
||||||
|
# with the merged_df3
|
||||||
|
########################
|
||||||
|
|
||||||
|
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
# REASSIGNMENT
|
||||||
|
my_df = merged_df3
|
||||||
|
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
|
||||||
|
#clear variables
|
||||||
|
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||||
|
|
||||||
|
# should be true
|
||||||
|
identical(my_df$Position, my_df$position)
|
||||||
|
|
||||||
|
#################################
|
||||||
|
# Read file: normalised file
|
||||||
|
# output of step 4 mcsm_pipeline
|
||||||
|
#################################
|
||||||
|
|
||||||
|
|
||||||
|
inDir = "~/git/Data/pyrazinamide/input/processed/"
|
||||||
|
inFile = paste0(inDir, "mcsm_complex1_normalised.csv"); inFile
|
||||||
|
|
||||||
|
mcsm_data <- read.csv(inFile
|
||||||
|
, row.names = 1
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T)
|
||||||
|
str(mcsm_data)
|
||||||
|
my_colnames = colnames(mcsm_data)
|
||||||
|
|
||||||
|
#====================================
|
||||||
|
# subset my_df to include only the columns in mcsm data
|
||||||
|
my_df2 = my_df[my_colnames]
|
||||||
|
#====================================
|
||||||
|
# compare the two
|
||||||
|
head(mcsm_data$Mutationinformation)
|
||||||
|
head(mcsm_data$Position)
|
||||||
|
|
||||||
|
head(my_df2$Mutationinformation)
|
||||||
|
head(my_df2$Position)
|
||||||
|
|
||||||
|
# sort mcsm data by Mutationinformation
|
||||||
|
mcsm_data_s = mcsm_data[order(mcsm_data$Mutationinformation),]
|
||||||
|
head(mcsm_data_s$Mutationinformation)
|
||||||
|
head(mcsm_data_s$Position)
|
||||||
|
|
||||||
|
# now compare: should be True, but is false....
|
||||||
|
# possibly due to rownames!?!
|
||||||
|
identical(mcsm_data_s, my_df2)
|
||||||
|
|
||||||
|
# from library dplyr
|
||||||
|
setdiff(mcsm_data_s, my_df2)
|
||||||
|
|
||||||
|
#from lib compare
|
||||||
|
compare(mcsm_data_s, my_df2) # seems rownames are the problem
|
||||||
|
|
||||||
|
# FIXME: automate this
|
||||||
|
# write files: checked using meld and files are indeed identical
|
||||||
|
#write.csv(mcsm_data_s, "mcsm_data_s.csv", row.names = F)
|
||||||
|
#write.csv(my_df2, "my_df2.csv", row.names = F)
|
||||||
|
|
||||||
|
|
||||||
|
#====================================================== end of section 1
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
##########################################################
|
||||||
|
# Checking: LIG(Filtered dataframe) #
|
||||||
|
##########################################################
|
||||||
|
|
||||||
|
# clear workspace
|
||||||
|
rm(list = ls())
|
||||||
|
|
||||||
|
###########################
|
||||||
|
#3) Read file: combined_lig from the script
|
||||||
|
###########################
|
||||||
|
source("combining_two_df_lig.R")
|
||||||
|
|
||||||
|
# df with NA:
|
||||||
|
# merged_df2 :
|
||||||
|
# merged_df3:
|
||||||
|
|
||||||
|
# df without NA:
|
||||||
|
# merged_df2_comp:
|
||||||
|
# merged_df3_comp:
|
||||||
|
|
||||||
|
######################
|
||||||
|
# You need to check it
|
||||||
|
# with the merged_df3
|
||||||
|
########################
|
||||||
|
|
||||||
|
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
# REASSIGNMENT
|
||||||
|
my_df = merged_df3
|
||||||
|
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||||
|
|
||||||
|
#clear variables
|
||||||
|
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
|
||||||
|
|
||||||
|
# should be true
|
||||||
|
identical(my_df$Position, my_df$position)
|
||||||
|
|
||||||
|
#################################
|
||||||
|
# Read file: normalised file
|
||||||
|
# output of step 4 mcsm_pipeline
|
||||||
|
#################################
|
||||||
|
|
||||||
|
inDir = "~/git/Data/pyrazinamide/input/processed/"
|
||||||
|
inFile = paste0(inDir, "mcsm_complex1_normalised.csv"); inFile
|
||||||
|
|
||||||
|
mcsm_data <- read.csv(inFile
|
||||||
|
, row.names = 1
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T)
|
||||||
|
str(mcsm_data)
|
||||||
|
|
||||||
|
###########################
|
||||||
|
# 4a: Filter/subset data: ONLY for LIGand analysis
|
||||||
|
# Lig plots < 10Ang
|
||||||
|
# Filter the lig plots for Dis_to_lig < 10Ang
|
||||||
|
###########################
|
||||||
|
# sanity checks
|
||||||
|
upos = unique(mcsm_data$Position)
|
||||||
|
|
||||||
|
# check range of distances
|
||||||
|
max(mcsm_data$Dis_lig_Ang)
|
||||||
|
min(mcsm_data$Dis_lig_Ang)
|
||||||
|
|
||||||
|
# Lig filtered: subset data to have only values less than 10 Ang
|
||||||
|
mcsm_data2 = subset(mcsm_data, mcsm_data$Dis_lig_Ang < 10)
|
||||||
|
|
||||||
|
rm(mcsm_data) #to avoid confusion
|
||||||
|
|
||||||
|
table(mcsm_data2$Dis_lig_Ang<10)
|
||||||
|
table(mcsm_data2$Dis_lig_Ang>10)
|
||||||
|
|
||||||
|
max(mcsm_data2$Dis_lig_Ang)
|
||||||
|
min(mcsm_data2$Dis_lig_Ang)
|
||||||
|
|
||||||
|
upos_f = unique(mcsm_data2$Position); upos_f
|
||||||
|
|
||||||
|
# colnames of df that you will need to subset the bigger df from
|
||||||
|
my_colnames = colnames(mcsm_data2)
|
||||||
|
#====================================
|
||||||
|
# subset bigger df i.e my_df to include only the columns in mcsm data2
|
||||||
|
my_df2 = my_df[my_colnames]
|
||||||
|
|
||||||
|
rm(my_df) #to avoid confusion
|
||||||
|
#====================================
|
||||||
|
# compare the two
|
||||||
|
head(mcsm_data2$Mutationinformation)
|
||||||
|
head(mcsm_data2$Position)
|
||||||
|
|
||||||
|
head(my_df2$Mutationinformation)
|
||||||
|
head(my_df2$Position)
|
||||||
|
|
||||||
|
# sort mcsm data by Mutationinformation
|
||||||
|
mcsm_data2_s = mcsm_data2[order(mcsm_data2$Mutationinformation),]
|
||||||
|
head(mcsm_data2_s$Mutationinformation)
|
||||||
|
head(mcsm_data2_s$Position)
|
||||||
|
|
||||||
|
# now compare: should be True, but is false....
|
||||||
|
# possibly due to rownames!?!
|
||||||
|
identical(mcsm_data2_s, my_df2)
|
||||||
|
|
||||||
|
# from library dplyr
|
||||||
|
setdiff(mcsm_data2_s, my_df2)
|
||||||
|
|
||||||
|
# from library compare
|
||||||
|
compare(mcsm_data2_s, my_df2) # seems rownames are the problem
|
||||||
|
|
||||||
|
#FIXME: automate this
|
||||||
|
# write files: checked using meld and files are indeed identical
|
||||||
|
#write.csv(mcsm_data2_s, "mcsm_data2_s.csv", row.names = F)
|
||||||
|
#write.csv(my_df2, "my_df2.csv", row.names = F)
|
||||||
|
|
||||||
|
|
||||||
|
##########################################################
|
||||||
|
# extract and write output file for SNP posn: all #
|
||||||
|
##########################################################
|
||||||
|
|
||||||
|
head(merged_df3$Position)
|
||||||
|
|
||||||
|
foo = merged_df3[order(merged_df3$Position),]
|
||||||
|
head(foo$Position)
|
||||||
|
|
||||||
|
snp_pos_unique = unique(foo$Position); snp_pos_unique
|
||||||
|
|
||||||
|
# sanity check:
|
||||||
|
table(snp_pos_unique == combined_df$Position)
|
||||||
|
|
||||||
|
#=====================
|
||||||
|
# write_output files
|
||||||
|
#=====================
|
||||||
|
outDir = "~/Data/pyrazinamide/input/processed/"
|
||||||
|
|
||||||
|
|
||||||
|
outFile1 = paste0(outDir, "snp_pos_unique.txt"); outFile1
|
||||||
|
print(paste0("Output file name and path will be:","", outFile1))
|
||||||
|
|
||||||
|
write.table(snp_pos_unique
|
||||||
|
, outFile1
|
||||||
|
, row.names = F
|
||||||
|
, col.names = F)
|
||||||
|
|
||||||
|
##############################################################
|
||||||
|
# extract and write output file for SNP posn: complete only #
|
||||||
|
##############################################################
|
||||||
|
head(merged_df3_comp$Position)
|
||||||
|
|
||||||
|
foo = merged_df3_comp[order(merged_df3_comp$Position),]
|
||||||
|
head(foo$Position)
|
||||||
|
|
||||||
|
snp_pos_unique = unique(foo$Position); snp_pos_unique
|
||||||
|
|
||||||
|
# outDir = "~/Data/pyrazinamide/input/processed/" # already set
|
||||||
|
|
||||||
|
outFile2 = paste0(outDir, "snp_pos_unique_comp.txt")
|
||||||
|
print(paste0("Output file name and path will be:", outFile2))
|
||||||
|
|
||||||
|
write.table(snp_pos_unique
|
||||||
|
, outFile2
|
||||||
|
, row.names = F
|
||||||
|
, col.names = F)
|
||||||
|
#============================== end of script
|
||||||
|
|
||||||
|
|
512
meta_data_analysis/.Rhistory
Normal file
512
meta_data_analysis/.Rhistory
Normal file
|
@ -0,0 +1,512 @@
|
||||||
|
, stringsAsFactors = F)
|
||||||
|
x = as.numeric(grepl(i,raw_data$all_muts_pza))
|
||||||
|
# DV: pyrazinamide 0 or 1
|
||||||
|
y = as.numeric(raw_data$pyrazinamide)
|
||||||
|
table(y,x)
|
||||||
|
# run glm model
|
||||||
|
model = glm(y ~ x, family = binomial)
|
||||||
|
#model = glm(y ~ x, family = binomial(link = "logit"))
|
||||||
|
summary(model)
|
||||||
|
#**********
|
||||||
|
# extract relevant model output
|
||||||
|
#**********
|
||||||
|
# extract log OR i.e the Beta estimate of the logistic model for a given snp
|
||||||
|
my_logor = summary(model)$coefficients[2,1]
|
||||||
|
print(paste0('Beta:', my_logor))
|
||||||
|
# extract SE of the logistic model for a given snp
|
||||||
|
my_se = summary(model)$coefficients[2,2]
|
||||||
|
print(paste0('SE:', my_se))
|
||||||
|
# extract Z of the logistic model for a given snp
|
||||||
|
my_zval = summary(model)$coefficients[2,3]
|
||||||
|
print(paste0('Z-value:', my_zval))
|
||||||
|
# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
|
||||||
|
#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
|
||||||
|
my_or = exp(summary(model)$coefficients[2,1])
|
||||||
|
print(paste0('OR:', my_or))
|
||||||
|
# sanity check : should be True
|
||||||
|
log(my_or) == my_logor
|
||||||
|
# extract P-value of the logistic model for a given snp
|
||||||
|
my_pval = summary(model)$coefficients[2,4]
|
||||||
|
print(paste0('P-value:', my_pval))
|
||||||
|
# extract confint interval of snp (2 steps, since the output is a named number)
|
||||||
|
ci_mod = exp(confint(model))[2,]
|
||||||
|
my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
|
||||||
|
print(paste0('CI:', my_ci))
|
||||||
|
#*************
|
||||||
|
# Assign the regression output in the original df
|
||||||
|
# you can use ('=' or '<-/->')
|
||||||
|
#*************
|
||||||
|
#pnca_snps_or$logistic_logOR[pnca_snps_or$Mutationinformation == i] = my_logor
|
||||||
|
my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$Mutationinformation == i]
|
||||||
|
my_logor
|
||||||
|
pnca_snps_or$Mutationinformation == i
|
||||||
|
View(pnca_snps_or)
|
||||||
|
#===============
|
||||||
|
# Step 4: Calculate for one snp
|
||||||
|
# using i, when you run the loop, it is easy
|
||||||
|
#===============
|
||||||
|
i = "pnca_p.trp68gly"
|
||||||
|
pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T) #2133
|
||||||
|
# uncomment as necessary
|
||||||
|
pnca_snps_or = pnca_snps_or[1:5,]
|
||||||
|
pnca_snps_or = pnca_snps_or[c(1:5),]
|
||||||
|
#===============
|
||||||
|
pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T) #2133
|
||||||
|
pnca_snps_or = pnca_snps_or[1:5,]
|
||||||
|
pnca_snps_or = pnca_snps_or[c(1:5),]
|
||||||
|
pnca_snps_or = pnca_snps_or[1:5]
|
||||||
|
pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T) #2133
|
||||||
|
pnca_snps_or = pnca_snps_or[1:5]
|
||||||
|
pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T) #2133
|
||||||
|
foo = pnca_snps_or[c(1:5,)]
|
||||||
|
foo = pnca_snps_or[c(1:5),]
|
||||||
|
foo = as.data.frame(pnca_snps_or[c(1:5),])
|
||||||
|
View(foo)
|
||||||
|
# create an empty dataframe
|
||||||
|
pnca_snps_or = as.data.frame(pnca_snps_or[c(1:5),])
|
||||||
|
# IV: corresponds to each unique snp (extracted using grep)
|
||||||
|
x = as.numeric(grepl(i,raw_data$all_muts_pza))
|
||||||
|
# DV: pyrazinamide 0 or 1
|
||||||
|
y = as.numeric(raw_data$pyrazinamide)
|
||||||
|
table(y,x)
|
||||||
|
# run glm model
|
||||||
|
model = glm(y ~ x, family = binomial)
|
||||||
|
#model = glm(y ~ x, family = binomial(link = "logit"))
|
||||||
|
summary(model)
|
||||||
|
my_logor = summary(model)$coefficients[2,1]
|
||||||
|
print(paste0('Beta:', my_logor))
|
||||||
|
# extract SE of the logistic model for a given snp
|
||||||
|
my_se = summary(model)$coefficients[2,2]
|
||||||
|
print(paste0('SE:', my_se))
|
||||||
|
# extract Z of the logistic model for a given snp
|
||||||
|
my_zval = summary(model)$coefficients[2,3]
|
||||||
|
print(paste0('Z-value:', my_zval))
|
||||||
|
# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
|
||||||
|
#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
|
||||||
|
my_or = exp(summary(model)$coefficients[2,1])
|
||||||
|
print(paste0('OR:', my_or))
|
||||||
|
# sanity check : should be True
|
||||||
|
log(my_or) == my_logor
|
||||||
|
# extract P-value of the logistic model for a given snp
|
||||||
|
my_pval = summary(model)$coefficients[2,4]
|
||||||
|
print(paste0('P-value:', my_pval))
|
||||||
|
# extract confint interval of snp (2 steps, since the output is a named number)
|
||||||
|
ci_mod = exp(confint(model))[2,]
|
||||||
|
my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
|
||||||
|
print(paste0('CI:', my_ci))
|
||||||
|
#*************
|
||||||
|
# Assign the regression output in the original df
|
||||||
|
# you can use ('=' or '<-/->')
|
||||||
|
#*************
|
||||||
|
#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
|
||||||
|
my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
|
||||||
|
my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
|
||||||
|
my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
|
||||||
|
my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
|
||||||
|
#===============
|
||||||
|
# Step 4: Iterate through this unique list
|
||||||
|
# and calculate OR, but only for one snp
|
||||||
|
# this is test before you apply it all others
|
||||||
|
#===============
|
||||||
|
pnca_snps_or$mutation == i
|
||||||
|
View(pnca_snps_or)
|
||||||
|
# create an empty dataframe
|
||||||
|
pnca_snps_or = data.frame(mutation = i)
|
||||||
|
my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
|
||||||
|
my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
|
||||||
|
my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
|
||||||
|
my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
|
||||||
|
View(pnca_snps_or_copy)
|
||||||
|
#===============
|
||||||
|
# Step 4: Iterate through this unique list
|
||||||
|
# and calculate OR, but only for one snp
|
||||||
|
# this is test before you apply it all others
|
||||||
|
#===============
|
||||||
|
#reset original df so you don't make a mistake
|
||||||
|
pnca_snps_or = pnca_snps_or_copy
|
||||||
|
for (i in pnca_snps_unique){
|
||||||
|
print(i)
|
||||||
|
}
|
||||||
|
pnca_snps_or = pnca_snps_or_copy #2133, 1
|
||||||
|
#........................................
|
||||||
|
# create an empty dataframe : uncomment as necessary
|
||||||
|
pnca_snps_or = data.frame(mutation = c(i, "blank_mut")
|
||||||
|
#........................................
|
||||||
|
# create an empty dataframe : uncomment as necessary
|
||||||
|
pnca_snps_or = data.frame(mutation = c(i, "blank_mut"))
|
||||||
|
#........................................
|
||||||
|
# create an empty dataframe : uncomment as necessary
|
||||||
|
pnca_snps_or = data.frame(mutation = c(i, "blank_mut"))
|
||||||
|
View(pnca_snps_or)
|
||||||
|
# IV: corresponds to each unique snp (extracted using grep)
|
||||||
|
x = as.numeric(grepl(i,raw_data$all_muts_pza))
|
||||||
|
# DV: pyrazinamide 0 or 1
|
||||||
|
y = as.numeric(raw_data$pyrazinamide)
|
||||||
|
table(y,x)
|
||||||
|
# run glm model
|
||||||
|
model = glm(y ~ x, family = binomial)
|
||||||
|
#model = glm(y ~ x, family = binomial(link = "logit"))
|
||||||
|
summary(model)
|
||||||
|
#**********
|
||||||
|
# extract relevant model output
|
||||||
|
#**********
|
||||||
|
# extract log OR i.e the Beta estimate of the logistic model for a given snp
|
||||||
|
my_logor = summary(model)$coefficients[2,1]
|
||||||
|
print(paste0('Beta:', my_logor))
|
||||||
|
# extract SE of the logistic model for a given snp
|
||||||
|
my_se = summary(model)$coefficients[2,2]
|
||||||
|
print(paste0('SE:', my_se))
|
||||||
|
# extract Z of the logistic model for a given snp
|
||||||
|
my_zval = summary(model)$coefficients[2,3]
|
||||||
|
print(paste0('Z-value:', my_zval))
|
||||||
|
# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
|
||||||
|
#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
|
||||||
|
my_or = exp(summary(model)$coefficients[2,1])
|
||||||
|
print(paste0('OR:', my_or))
|
||||||
|
# sanity check : should be True
|
||||||
|
log(my_or) == my_logor
|
||||||
|
# extract P-value of the logistic model for a given snp
|
||||||
|
my_pval = summary(model)$coefficients[2,4]
|
||||||
|
print(paste0('P-value:', my_pval))
|
||||||
|
# extract confint interval of snp (2 steps, since the output is a named number)
|
||||||
|
ci_mod = exp(confint(model))[2,]
|
||||||
|
my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
|
||||||
|
print(paste0('CI:', my_ci))
|
||||||
|
#*************
|
||||||
|
# Assign the regression output in the original df
|
||||||
|
# you can use ('=' or '<-/->')
|
||||||
|
#*************
|
||||||
|
#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
|
||||||
|
my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
|
||||||
|
my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
|
||||||
|
my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
|
||||||
|
my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
|
||||||
|
View(pnca_snps_or)
|
||||||
|
pnca_snps_or = pnca_snps_or_copy #2133, 1
|
||||||
|
for (i in pnca_snps_unique){
|
||||||
|
print(i)
|
||||||
|
#*************
|
||||||
|
# start logistic regression model building
|
||||||
|
#*************
|
||||||
|
# set the IV and DV for the logistic regression model
|
||||||
|
# IV: corresponds to each unique snp (extracted using grep)
|
||||||
|
x = as.numeric(grepl(i,raw_data$all_muts_pza))
|
||||||
|
# DV: pyrazinamide 0 or 1
|
||||||
|
y = as.numeric(raw_data$pyrazinamide)
|
||||||
|
table(y,x)
|
||||||
|
# run glm model
|
||||||
|
model = glm(y ~ x, family = binomial)
|
||||||
|
#model = glm(y ~ x, family = binomial(link = "logit"))
|
||||||
|
summary(model)
|
||||||
|
#**********
|
||||||
|
# extract relevant model output
|
||||||
|
#**********
|
||||||
|
# extract log OR i.e the Beta estimate of the logistic model for a given snp
|
||||||
|
my_logor = summary(model)$coefficients[2,1]
|
||||||
|
print(paste0('Beta:', my_logor))
|
||||||
|
# extract SE of the logistic model for a given snp
|
||||||
|
my_se = summary(model)$coefficients[2,2]
|
||||||
|
print(paste0('SE:', my_se))
|
||||||
|
# extract Z of the logistic model for a given snp
|
||||||
|
my_zval = summary(model)$coefficients[2,3]
|
||||||
|
print(paste0('Z-value:', my_zval))
|
||||||
|
# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
|
||||||
|
#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
|
||||||
|
my_or = exp(summary(model)$coefficients[2,1])
|
||||||
|
print(paste0('OR:', my_or))
|
||||||
|
# sanity check : should be True
|
||||||
|
log(my_or) == my_logor
|
||||||
|
# extract P-value of the logistic model for a given snp
|
||||||
|
my_pval = summary(model)$coefficients[2,4]
|
||||||
|
print(paste0('P-value:', my_pval))
|
||||||
|
# extract confint interval of snp (2 steps, since the output is a named number)
|
||||||
|
ci_mod = exp(confint(model))[2,]
|
||||||
|
my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
|
||||||
|
print(paste0('CI:', my_ci))
|
||||||
|
#*************
|
||||||
|
# Assign the regression output in the original df
|
||||||
|
# you can use ('=' or '<-/->')
|
||||||
|
#*************
|
||||||
|
#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
|
||||||
|
my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
|
||||||
|
my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
|
||||||
|
my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
|
||||||
|
my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
|
||||||
|
}
|
||||||
|
warnings()
|
||||||
|
View(pnca_snps_or)
|
||||||
|
View(pnca_snps_or_copy)
|
||||||
|
#sanity check
|
||||||
|
pnca_snps_or$mutation == i1
|
||||||
|
#sanity check
|
||||||
|
pnca_snps_or[pnca_snps_or$mutation == i1]
|
||||||
|
pnca_snps_or[pnca_snps_or$mutation == i2]
|
||||||
|
pnca_snps_or[pnca_snps_or$mutation == i2,]
|
||||||
|
pnca_snps_or1 = unique(pnca_snps_or)
|
||||||
|
write.csv(pnca_snps_or1, "../Data_original/valid_pnca_snps_with_OR.csv")
|
||||||
|
# you only need it for the unique mutations
|
||||||
|
pnca_snps_or = unique(pnca_snps_or) #2133, 1
|
||||||
|
for (i in pnca_snps_unique){
|
||||||
|
print(i)
|
||||||
|
#*************
|
||||||
|
# start logistic regression model building
|
||||||
|
#*************
|
||||||
|
# set the IV and DV for the logistic regression model
|
||||||
|
# IV: corresponds to each unique snp (extracted using grep)
|
||||||
|
x = as.numeric(grepl(i,raw_data$all_muts_pza))
|
||||||
|
# DV: pyrazinamide 0 or 1
|
||||||
|
y = as.numeric(raw_data$pyrazinamide)
|
||||||
|
table(y,x)
|
||||||
|
# run glm model
|
||||||
|
model = glm(y ~ x, family = binomial)
|
||||||
|
#model = glm(y ~ x, family = binomial(link = "logit"))
|
||||||
|
summary(model)
|
||||||
|
#**********
|
||||||
|
# extract relevant model output
|
||||||
|
#**********
|
||||||
|
# extract log OR i.e the Beta estimate of the logistic model for a given snp
|
||||||
|
my_logor = summary(model)$coefficients[2,1]
|
||||||
|
print(paste0('Beta:', my_logor))
|
||||||
|
# extract SE of the logistic model for a given snp
|
||||||
|
my_se = summary(model)$coefficients[2,2]
|
||||||
|
print(paste0('SE:', my_se))
|
||||||
|
# extract Z of the logistic model for a given snp
|
||||||
|
my_zval = summary(model)$coefficients[2,3]
|
||||||
|
print(paste0('Z-value:', my_zval))
|
||||||
|
# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
|
||||||
|
#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
|
||||||
|
my_or = exp(summary(model)$coefficients[2,1])
|
||||||
|
print(paste0('OR:', my_or))
|
||||||
|
# sanity check : should be True
|
||||||
|
log(my_or) == my_logor
|
||||||
|
# extract P-value of the logistic model for a given snp
|
||||||
|
my_pval = summary(model)$coefficients[2,4]
|
||||||
|
print(paste0('P-value:', my_pval))
|
||||||
|
# extract confint interval of snp (2 steps, since the output is a named number)
|
||||||
|
ci_mod = exp(confint(model))[2,]
|
||||||
|
my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
|
||||||
|
print(paste0('CI:', my_ci))
|
||||||
|
#*************
|
||||||
|
# Assign the regression output in the original df
|
||||||
|
# you can use ('=' or '<-/->')
|
||||||
|
#*************
|
||||||
|
#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
|
||||||
|
my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
|
||||||
|
my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
|
||||||
|
my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
|
||||||
|
my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
|
||||||
|
}
|
||||||
|
View(pnca_snps_or)
|
||||||
|
2.290256e+01
|
||||||
|
1.561132e+06
|
||||||
|
3.242285e-04
|
||||||
|
#sanity check
|
||||||
|
pnca_snps_or[pnca_snps_or$mutation == i1]
|
||||||
|
pnca_snps_or[pnca_snps_or$mutation == i2,]
|
||||||
|
write.csv(pnca_snps_or1, "../Data_original/valid_pnca_snps_with_OR.csv")
|
||||||
|
my_data = read.csv("../Data_original/meta_pza_with_AF.csv"
|
||||||
|
, stringsAsFactors = FALSE) #11374, 19
|
||||||
|
View(my_data)
|
||||||
|
# remove the first column
|
||||||
|
my_data = my_data[-1] #11374, 18
|
||||||
|
# check if first col is 'id': should be TRUE
|
||||||
|
colnames(my_data)[1] == 'id'
|
||||||
|
# sanity check
|
||||||
|
snps_all = unique(my_data$mutation)# 337
|
||||||
|
pnca_snps_or = snps_all
|
||||||
|
pnca_snps_or = as.data.frame(snps_all)
|
||||||
|
View(pnca_snps_or)
|
||||||
|
snps_all[-"true_wt"]
|
||||||
|
pnca_snps_or = as.data.frame(snps_all[-c(1,1)])
|
||||||
|
View(pnca_snps_or)
|
||||||
|
snps_all = as.data.frame(snps_all)
|
||||||
|
View(snps_all)
|
||||||
|
#remove true_wt entry
|
||||||
|
w1 = which(rownames(snps_all) == "true_wt")
|
||||||
|
View(snps_all)
|
||||||
|
#remove true_wt entry
|
||||||
|
w1 = which(snps_all$snps_all == "true_wt")
|
||||||
|
rm(pnca_snps_or)
|
||||||
|
pnca_snps_or = snps_all[-w1]
|
||||||
|
pnca_snps_or = snps_all[,-w1]
|
||||||
|
pnca_snps_or = as.data.frame(snps_all[-c(1,1)])
|
||||||
|
#remove true_wt entry
|
||||||
|
w1 = which(snps_all) == "true_wt"
|
||||||
|
pnca_snps_or = as.data.frame(snps_all[-c(1,1)])
|
||||||
|
my_data = read.csv("../Data_original/meta_pza_with_AF.csv"
|
||||||
|
, stringsAsFactors = FALSE) #11374, 19
|
||||||
|
# remove the first column
|
||||||
|
my_data = my_data[-1] #11374, 18
|
||||||
|
# check if first col is 'id': should be TRUE
|
||||||
|
colnames(my_data)[1] == 'id'
|
||||||
|
# sanity check
|
||||||
|
snps_all = unique(my_data$mutation)# 337
|
||||||
|
snps_all = as.data.frame(snps_all)
|
||||||
|
snps_all[-c(1,1)]
|
||||||
|
pnca_snps_or = as.data.frame(snps_all[-c(1,1)])
|
||||||
|
pnca_snps_or = as.data.frame(snps_all[, -c(1,1)])
|
||||||
|
#remove true_wt entry
|
||||||
|
#w1 = which(snps_all) == "true_wt"
|
||||||
|
pnca_snps_or = snps_all
|
||||||
|
pnca_snps_or = pnca_snps_or_copy
|
||||||
|
#remove true_wt entry
|
||||||
|
#w1 = which(snps_all) == "true_wt"
|
||||||
|
pnca_snps_or = snps_all
|
||||||
|
pnca_snps_or -> pnca_snps_or_copy
|
||||||
|
#===============
|
||||||
|
# Step 4: Iterate through this unique list
|
||||||
|
# and calculate OR for each snp
|
||||||
|
# and assign to the pnca_snps_or df that has
|
||||||
|
# each row as a unique snp
|
||||||
|
#===============
|
||||||
|
# reset original df so you don't make a mistake: IMPORTANT
|
||||||
|
pnca_snps_or = pnca_snps_or_copy #2133, 1
|
||||||
|
# you only need it for the unique mutations
|
||||||
|
pnca_snps_or = unique(pnca_snps_or) #337, 1
|
||||||
|
for (i in pnca_snps_unique){
|
||||||
|
print(i)
|
||||||
|
#*************
|
||||||
|
# start logistic regression model building
|
||||||
|
#*************
|
||||||
|
# set the IV and DV for the logistic regression model
|
||||||
|
# IV: corresponds to each unique snp (extracted using grep)
|
||||||
|
x = as.numeric(grepl(i,raw_data$all_muts_pza))
|
||||||
|
# DV: pyrazinamide 0 or 1
|
||||||
|
y = as.numeric(raw_data$pyrazinamide)
|
||||||
|
table(y,x)
|
||||||
|
# run glm model
|
||||||
|
model = glm(y ~ x, family = binomial)
|
||||||
|
#model = glm(y ~ x, family = binomial(link = "logit"))
|
||||||
|
summary(model)
|
||||||
|
#**********
|
||||||
|
# extract relevant model output
|
||||||
|
#**********
|
||||||
|
# extract log OR i.e the Beta estimate of the logistic model for a given snp
|
||||||
|
my_logor = summary(model)$coefficients[2,1]
|
||||||
|
print(paste0('Beta:', my_logor))
|
||||||
|
# extract SE of the logistic model for a given snp
|
||||||
|
my_se = summary(model)$coefficients[2,2]
|
||||||
|
print(paste0('SE:', my_se))
|
||||||
|
# extract Z of the logistic model for a given snp
|
||||||
|
my_zval = summary(model)$coefficients[2,3]
|
||||||
|
print(paste0('Z-value:', my_zval))
|
||||||
|
# Dervive OR i.e exp(my_logor) from the logistic model for a given snp
|
||||||
|
#my_or = round(exp(summary(model)$coefficients[2,1]), roundto)
|
||||||
|
my_or = exp(summary(model)$coefficients[2,1])
|
||||||
|
print(paste0('OR:', my_or))
|
||||||
|
# sanity check : should be True
|
||||||
|
log(my_or) == my_logor
|
||||||
|
# extract P-value of the logistic model for a given snp
|
||||||
|
my_pval = summary(model)$coefficients[2,4]
|
||||||
|
print(paste0('P-value:', my_pval))
|
||||||
|
# extract confint interval of snp (2 steps, since the output is a named number)
|
||||||
|
ci_mod = exp(confint(model))[2,]
|
||||||
|
my_ci = paste(ci_mod[["2.5 %"]], ",", ci_mod[["97.5 %"]])
|
||||||
|
print(paste0('CI:', my_ci))
|
||||||
|
#*************
|
||||||
|
# Assign the regression output in the original df
|
||||||
|
# you can use ('=' or '<-/->')
|
||||||
|
#*************
|
||||||
|
#pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i] = my_logor
|
||||||
|
my_logor -> pnca_snps_or$logistic_logOR[pnca_snps_or$mutation == i]
|
||||||
|
my_or -> pnca_snps_or$OR[pnca_snps_or$mutation == i]
|
||||||
|
my_pval -> pnca_snps_or$pvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_zval -> pnca_snps_or$zvalue[pnca_snps_or$mutation == i]
|
||||||
|
my_se -> pnca_snps_or$logistic_se[pnca_snps_or$mutation == i]
|
||||||
|
my_ci -> pnca_snps_or$ci[pnca_snps_or$mutation == i]
|
||||||
|
}
|
||||||
|
getwd()
|
||||||
|
#setwd("~/Documents/git/LSHTM_Y1_PNCA/meta_data_analysis") # work
|
||||||
|
setwd("~/git/LSHTM_Y1_PNCA/meta_data_analysis") # thinkpad
|
||||||
|
#setwd("/Users/tanu/git/LSHTM_Y1_PNCA/meta_data_analysis") # mac
|
||||||
|
getwd()
|
||||||
|
#===============
|
||||||
|
# Step 1: read raw data
|
||||||
|
#===============
|
||||||
|
raw_data<-read.csv("../Data_original/original_tanushree_data_v2.csv"
|
||||||
|
,stringsAsFactors = F)[,c("id","pyrazinamide","dr_mutations_pyrazinamide","other_mutations_pyrazinamide")]#19265, 4
|
||||||
|
raw_data<-raw_data[!is.na(raw_data$pyrazinamide),]#12511, 4
|
||||||
|
# combine the two mutation columns
|
||||||
|
raw_data$all_mutations_pyrazinamide<-paste(raw_data$dr_mutations_pyrazinamide, raw_data$other_mutations_pyrazinamide)#12511, 5
|
||||||
|
head(raw_data$all_mutations_pyrazinamide)
|
||||||
|
# create yet another column that contains all the mutations but in lower case
|
||||||
|
raw_data$all_muts_pza = tolower(raw_data$all_mutations_pyrazinamide) #12511, 6
|
||||||
|
table(grepl("pnca_p",raw_data$all_muts_pza))
|
||||||
|
#FALSE TRUE
|
||||||
|
#10603 1908
|
||||||
|
pnca_snps_or = read.csv("../Data_original/pnca_snps_for_or_calcs.csv"
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T) #2133
|
||||||
|
# subset a snall section to test
|
||||||
|
#pnca_snps_or_copy = pnca_snps_or
|
||||||
|
#pnca_snps_or = pnca_snps_or_copy
|
||||||
|
pnca_snps_unique = unique(pnca_snps_or$mutation) #293
|
||||||
|
i2 = "pnca_p.trp68gly" # Should exist
|
||||||
|
grep(i2, pnca_snps_unique)
|
||||||
|
my_data = read.csv("../Data_original/meta_pza_with_AF.csv"
|
||||||
|
, stringsAsFactors = FALSE) #11374, 19
|
||||||
|
# remove the first column
|
||||||
|
my_data = my_data[-1] #11374, 18
|
||||||
|
# check if first col is 'id': should be TRUE
|
||||||
|
colnames(my_data)[1] == 'id'
|
||||||
|
# sanity check
|
||||||
|
head(my_data$mutation)
|
||||||
|
my_data = unique(my_data$mutation)
|
||||||
|
my_data[!duplicated(my_data$mutation)]
|
||||||
|
my_data_unique = my_data[!duplicated(my_data$mutation),]
|
||||||
|
my_data[!duplicated('mutation'),]
|
||||||
|
my_data_unique = my_data[!duplicated(my_data[,'mutation']),]
|
||||||
|
my_data_unique = my_data[!duplicated(my_data['mutation']),]
|
||||||
|
getwd()
|
||||||
|
setwd("/git/LSHTM_analysis/meta_data_analysis")
|
||||||
|
getwd()
|
||||||
|
getwd()
|
||||||
|
setwd("/git/github/LSHTM_analysis/meta_data_analysis")
|
||||||
|
getwd()
|
||||||
|
#===============
|
||||||
|
# Step 1: read GWAS raw data stored in Data_original/
|
||||||
|
#===============
|
||||||
|
infile = read.csv("../Data_original", file.choose(), stringsAsFactors = F))
|
||||||
|
c = file.choose()
|
||||||
|
c = file.choose(../Data_original)
|
||||||
|
c = read.csv(file.choose(), stringsAsFactors = F)
|
||||||
|
#===============
|
||||||
|
# Step 1: read GWAS raw data stored in Data_original/
|
||||||
|
#===============
|
||||||
|
infile = read.csv(file.choose(), stringsAsFactors = F))
|
||||||
|
c = read.csv(file.choose(), stringsAsFactors = F)
|
||||||
|
#===============
|
||||||
|
# Step 1: read GWAS raw data stored in Data_original/
|
||||||
|
#===============
|
||||||
|
infile = read.csv(file.choose(), stringsAsFactors = F)
|
||||||
|
#===============
|
||||||
|
# Step 1: read GWAS raw data stored in Data_original/
|
||||||
|
#===============
|
||||||
|
infile = read.csv(file.choose(), stringsAsFactors = F)
|
||||||
|
raw_data = infile[,c("id","pyrazinamide","dr_mutations_pyrazinamide","other_mutations_pyrazinamide")]
|
||||||
|
outdir = paste0("../mcsm_analysis",drug,"/Data/")
|
||||||
|
# define output variables
|
||||||
|
drug = 'pyrazinamide'
|
||||||
|
outdir = paste0("../mcsm_analysis",drug,"/Data/")
|
||||||
|
outdir = paste0("../mcsm_analysis/",drug,"/Data/")
|
||||||
|
outFile = "meta_data_with_AFandOR.csv"
|
||||||
|
output_filename = paste0(outdir, outFile)
|
||||||
|
output_filename
|
BIN
meta_data_analysis/__pycache__/reference_dict.cpython-37.pyc
Normal file
BIN
meta_data_analysis/__pycache__/reference_dict.cpython-37.pyc
Normal file
Binary file not shown.
7
meta_data_analysis/init_data_dirs.py
Executable file
7
meta_data_analysis/init_data_dirs.py
Executable file
|
@ -0,0 +1,7 @@
|
||||||
|
#!/usr/bin/python3
|
||||||
|
# Initialise a blank 'Data' directory and drug subdirs etc.
|
||||||
|
# TODO:
|
||||||
|
# - Read base dir from config file
|
||||||
|
# - Create eg: '~/git/Data/{original,processed}
|
||||||
|
# - Create eg: '~/git/Data/processed/' + drug (for each drug)
|
||||||
|
# - Create eg: '~/git/Data/output/' + drug + '{plots, structure}'
|
241
meta_data_analysis/pnca_AF_and_OR_calcs.R
Normal file
241
meta_data_analysis/pnca_AF_and_OR_calcs.R
Normal file
|
@ -0,0 +1,241 @@
|
||||||
|
getwd()
|
||||||
|
setwd("/git/github/git/LSHTM_analysis/meta_data_analysis")
|
||||||
|
getwd()
|
||||||
|
|
||||||
|
#===============
|
||||||
|
# Step 1: read GWAS raw data stored in Data_original/
|
||||||
|
#===============
|
||||||
|
infile = read.csv(file.choose(), stringsAsFactors = F)
|
||||||
|
|
||||||
|
raw_data = infile[,c("id"
|
||||||
|
, "pyrazinamide"
|
||||||
|
, "dr_mutations_pyrazinamide"
|
||||||
|
, "other_mutations_pyrazinamide")]
|
||||||
|
|
||||||
|
#####
|
||||||
|
# 1a: exclude na
|
||||||
|
#####
|
||||||
|
raw_data = raw_data[!is.na(raw_data$pyrazinamide),]
|
||||||
|
|
||||||
|
total_samples = length(unique(raw_data$id))
|
||||||
|
print(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$all_muts_pnca))
|
||||||
|
|
||||||
|
# sanity check: should be TRUE
|
||||||
|
sum(table(grepl("pnca_p",raw_data$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)
|
||||||
|
|
||||||
|
###### read list of muts to calculate OR for (fname3 from pnca_data_extraction.py)
|
||||||
|
pnca_snps_or = read.csv(file.choose()
|
||||||
|
, stringsAsFactors = F
|
||||||
|
, header = T)
|
||||||
|
|
||||||
|
# extract unique snps to iterate over for AF and OR calcs
|
||||||
|
# total no of unique snps
|
||||||
|
# AF and OR calculations
|
||||||
|
|
||||||
|
pnca_snps_unique = unique(pnca_snps_or$mutation)
|
||||||
|
|
||||||
|
# Define OR function
|
||||||
|
x = as.numeric(mut)
|
||||||
|
y = dst
|
||||||
|
or = function(x,y){
|
||||||
|
tab = as.matrix(table(x,y))
|
||||||
|
a = tab[2,2]
|
||||||
|
if (a==0){ a<-0.5}
|
||||||
|
b = tab[2,1]
|
||||||
|
if (b==0){ b<-0.5}
|
||||||
|
c = tab[1,2]
|
||||||
|
if (c==0){ c<-0.5}
|
||||||
|
d = tab[1,1]
|
||||||
|
if (d==0){ d<-0.5}
|
||||||
|
(a/b)/(c/d)
|
||||||
|
|
||||||
|
}
|
||||||
|
|
||||||
|
dst = raw_data$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
|
||||||
|
# )
|
||||||
|
|
||||||
|
# subset df cols to add to the calc param df
|
||||||
|
pnca_snps_cols = pnca_snps_or[c('mutation_info', 'mutation', 'Mutationinformation')]
|
||||||
|
pnca_snps_cols = pnca_snps_cols[!duplicated(pnca_snps_cols$mutation),]
|
||||||
|
|
||||||
|
rownames(pnca_snps_cols) = pnca_snps_cols$mutation
|
||||||
|
head(rownames(pnca_snps_cols))
|
||||||
|
#snps_with_AF_and_OR
|
||||||
|
|
||||||
|
# combine
|
||||||
|
comb_AF_and_OR = data.frame(ors, pvals, afs)
|
||||||
|
head(rownames(comb_AF_and_OR))
|
||||||
|
|
||||||
|
# sanity checks: should be the same
|
||||||
|
dim(comb_AF_and_OR); dim(pnca_snps_cols)
|
||||||
|
table(rownames(comb_AF_and_OR)%in%rownames(pnca_snps_cols))
|
||||||
|
|
||||||
|
table(rownames(pnca_snps_cols)%in%rownames(comb_AF_and_OR))
|
||||||
|
|
||||||
|
# merge the above two df whose dim you checked
|
||||||
|
snps_with_AF_and_OR = merge(comb_AF_and_OR, pnca_snps_cols
|
||||||
|
, by = "row.names"
|
||||||
|
# , all.x = T
|
||||||
|
)
|
||||||
|
|
||||||
|
#rm(pnca_snps_cols, pnca_snps_or, raw_data)
|
||||||
|
|
||||||
|
#===============
|
||||||
|
# Step 3: Read data file where you will add the calculated OR
|
||||||
|
# Note: this is the big file with one-many relationship between snps and lineages
|
||||||
|
# i.e fname4 from 'pnca_extraction.py'
|
||||||
|
#===============
|
||||||
|
my_data = read.csv(file.choose()
|
||||||
|
, row.names = 1
|
||||||
|
, stringsAsFactors = FALSE)
|
||||||
|
|
||||||
|
head(my_data)
|
||||||
|
length(unique(my_data$id))
|
||||||
|
|
||||||
|
# check if first col is 'id': should be TRUE
|
||||||
|
colnames(my_data)[1] == 'id'
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
head(my_data$mutation)
|
||||||
|
|
||||||
|
# FILES TO MERGE:
|
||||||
|
# comb_AF_and_OR: file containing OR
|
||||||
|
# my_data = big meta data file
|
||||||
|
# linking column: mutation
|
||||||
|
|
||||||
|
head(my_data)
|
||||||
|
merged_df = merge(my_data # big file
|
||||||
|
, snps_with_AF_and_OR # small (afor file)
|
||||||
|
, by = "mutation"
|
||||||
|
, all.x = T) # because you want all the entries of the meta data
|
||||||
|
|
||||||
|
# sanity checks: should be True
|
||||||
|
# FIXME: I have checked this manually, but make it so it is a pass or a fail!
|
||||||
|
comb_AF_and_OR[rownames(comb_AF_and_OR) == "pnca_p.gln10pro",]$ors
|
||||||
|
merged_df[merged_df$Mutationinformation.x == "Q10P",]$ors
|
||||||
|
|
||||||
|
merged_df[merged_df$Mutationinformation.x == "Q10P",]
|
||||||
|
|
||||||
|
# sanity check: very important!
|
||||||
|
colnames(merged_df)
|
||||||
|
|
||||||
|
table(merged_df$mutation_info.x == merged_df$mutation_info.y)
|
||||||
|
|
||||||
|
#FIXME: what happened to other 7 and FALSE
|
||||||
|
table(merged_df$Mutationinformation.x == merged_df$Mutationinformation.y)
|
||||||
|
|
||||||
|
# problem
|
||||||
|
identical(merged_df$Mutationinformation.x, merged_df$Mutationinformation.y)
|
||||||
|
|
||||||
|
#merged_df[merged_df$Mutationinformation.x != merged_df$Mutationinformation.y,]
|
||||||
|
|
||||||
|
#throw away the y because that is a smaller df
|
||||||
|
d1 = which(colnames(merged_df) == "mutation_info.y") #21
|
||||||
|
d2 = which(colnames(merged_df) == "Mutationinformation.y") #22
|
||||||
|
|
||||||
|
merged_df2 = merged_df[-c(d1, d2)] #3093 20
|
||||||
|
colnames(merged_df2)
|
||||||
|
|
||||||
|
# rename cols
|
||||||
|
colnames(merged_df2)[colnames(merged_df2)== "mutation_info.x"] <- "mutation_info"
|
||||||
|
colnames(merged_df2)[colnames(merged_df2)== "Mutationinformation.x"] <- "Mutationinformation"
|
||||||
|
|
||||||
|
colnames(merged_df2)
|
||||||
|
|
||||||
|
# should be 0
|
||||||
|
sum(is.na(merged_df2$Mutationinformation))
|
||||||
|
|
||||||
|
# count na in each column
|
||||||
|
na_count = sapply(merged_df2, function(y) sum(length(which(is.na(y))))); na_count
|
||||||
|
# only some or and Af should be NA
|
||||||
|
#Row.names ors pvals afs
|
||||||
|
#81 81 81 81
|
||||||
|
|
||||||
|
|
||||||
|
colnames(merged_df2)[colnames(merged_df2)== "ors"] <- "OR"
|
||||||
|
colnames(merged_df2)[colnames(merged_df2)== "afs"] <- "AF"
|
||||||
|
colnames(merged_df2)[colnames(merged_df2)== "pvals"] <- "pvalue"
|
||||||
|
|
||||||
|
colnames(merged_df2)
|
||||||
|
|
||||||
|
# add log OR and neglog pvalue
|
||||||
|
merged_df2$logor = log(merged_df2$OR)
|
||||||
|
is.numeric(merged_df2$logor)
|
||||||
|
|
||||||
|
merged_df2$neglog10pvalue = -log10(merged_df2$pvalue)
|
||||||
|
is.numeric(merged_df2$neglog10pvalue)
|
||||||
|
|
||||||
|
# write file out
|
||||||
|
#write.csv(merged_df, "../Data/meta_data_with_AFandOR_JP_TT.csv")
|
||||||
|
|
||||||
|
# define output variables
|
||||||
|
drug = 'pyrazinamide'
|
||||||
|
out_dir = paste0("../mcsm_analysis/",drug,"/Data/")
|
||||||
|
outFile = "meta_data_with_AFandOR.csv"
|
||||||
|
output_filename = paste0(outdir, outFile)
|
||||||
|
|
||||||
|
write.csv(merged_df2, output_filename
|
||||||
|
, row.names = F)
|
626
meta_data_analysis/pnca_data_extraction.py
Executable file
626
meta_data_analysis/pnca_data_extraction.py
Executable file
|
@ -0,0 +1,626 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Tue Aug 6 12:56:03 2019
|
||||||
|
|
||||||
|
@author: tanu
|
||||||
|
"""
|
||||||
|
|
||||||
|
# FIXME: include error checking to enure you only
|
||||||
|
# concentrate on positions that have structural info?
|
||||||
|
|
||||||
|
#%% load libraries
|
||||||
|
###################
|
||||||
|
# load libraries
|
||||||
|
import os, sys
|
||||||
|
import pandas as pd
|
||||||
|
#import numpy as np
|
||||||
|
|
||||||
|
#from pandas.api.types import is_string_dtype
|
||||||
|
#from pandas.api.types import is_numeric_dtype
|
||||||
|
|
||||||
|
# to create dir
|
||||||
|
#my_dir = os.path.expanduser('~/some_dir')
|
||||||
|
#make sure mcsm_analysis/ exists
|
||||||
|
#or specify the output directory
|
||||||
|
|
||||||
|
#%%
|
||||||
|
#%%
|
||||||
|
#%%
|
||||||
|
#========================================================
|
||||||
|
# TASK: extract ALL pncA mutations from GWAS data
|
||||||
|
#========================================================
|
||||||
|
#%%
|
||||||
|
####################
|
||||||
|
# my working dir
|
||||||
|
os.getcwd()
|
||||||
|
homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
|
||||||
|
os.chdir(homedir + '/git/LSHTM_analysis/meta_data_analysis')
|
||||||
|
os.getcwd()
|
||||||
|
#%%
|
||||||
|
from reference_dict import my_aa_dict #CHECK DIR STRUC THERE!
|
||||||
|
#%%
|
||||||
|
#NOTE: Out_dir MUST exis
|
||||||
|
# User defined dir strpyrazinamide
|
||||||
|
drug = 'pyrazinamide'
|
||||||
|
gene = 'pnca'
|
||||||
|
out_dir = homedir + '/git/LSHTM_analysis/mcsm_analysis/'
|
||||||
|
# = out_dir + drug
|
||||||
|
data_dir = homedir + '/git/Data'
|
||||||
|
|
||||||
|
if not os.path.exists(data_dir):
|
||||||
|
print('Error!', data_dir, 'does not exist. Please ensure it exists and contains the appropriate raw data')
|
||||||
|
os.makedirs(data_dir)
|
||||||
|
die()
|
||||||
|
|
||||||
|
if not os.path.exists(out_dir):
|
||||||
|
print('Error!', out_dir, 'does not exist. Please create it')
|
||||||
|
exit()
|
||||||
|
|
||||||
|
#if not os.path.exists(work_dir):
|
||||||
|
# print('creating dir that does not exist', 'dir_name:', work_dir)
|
||||||
|
# os.makedirs(work_dir)
|
||||||
|
else:
|
||||||
|
print('Dir exists: Carrying on')
|
||||||
|
|
||||||
|
# now create sub dir structure within work_dir
|
||||||
|
# pyrazinamide/mcsm_analysis
|
||||||
|
|
||||||
|
# we need three dir
|
||||||
|
# Data
|
||||||
|
# Scripts
|
||||||
|
# Plotting
|
||||||
|
# Results
|
||||||
|
# Plots
|
||||||
|
|
||||||
|
# create a list of dir names
|
||||||
|
#dir_names = ['Data', 'Scripts', 'Results']
|
||||||
|
|
||||||
|
|
||||||
|
#for i in dir_names:
|
||||||
|
# this_dir = (work_dir + '/' + i)
|
||||||
|
# if not os.path.exists(this_dir):
|
||||||
|
# print('creating dir that does not exist:', this_dir)
|
||||||
|
# os.makedirs(this_dir)
|
||||||
|
#else:
|
||||||
|
# print('Dir exists: Carrying on')
|
||||||
|
|
||||||
|
# Create sub dirs
|
||||||
|
# 1)
|
||||||
|
# Scripts
|
||||||
|
# Plotting
|
||||||
|
#subdir_plotting = work_dir + '/Scripts/Plotting'
|
||||||
|
#if not os.path.exists(subdir_plotting):
|
||||||
|
# print('creating dir that does not exist:', subdir_plotting)
|
||||||
|
# os.makedirs(subdir_plotting)
|
||||||
|
#else:
|
||||||
|
# print('Dir exists: Carrying on')
|
||||||
|
|
||||||
|
# 2)
|
||||||
|
# Results
|
||||||
|
# Plots
|
||||||
|
#subdir_plots = work_dir + '/Results/Plots'
|
||||||
|
#if not os.path.exists(subdir_plots):
|
||||||
|
# print('creating dir that does not exist:', subdir_plots)
|
||||||
|
# os.makedirs(subdir_plots)
|
||||||
|
#else:
|
||||||
|
# print('Dir exists: Carrying on')
|
||||||
|
|
||||||
|
# clear varaibles
|
||||||
|
#del(dir_names, drug, i, subdir_plots, subdir_plotting)
|
||||||
|
|
||||||
|
#exit()
|
||||||
|
#%%
|
||||||
|
#==============================================================================
|
||||||
|
############
|
||||||
|
# STEP 1: Read file original_tanushree_data_v2.csv
|
||||||
|
############
|
||||||
|
data_file = data_dir + '/input/original/original_tanushree_data_v2.csv'
|
||||||
|
meta_data = pd.read_csv(data_file, sep = ',')
|
||||||
|
|
||||||
|
# column names
|
||||||
|
list(meta_data.columns)
|
||||||
|
|
||||||
|
# extract elevant columns to extract from meta data related to the pyrazinamide
|
||||||
|
meta_data = meta_data[['id'
|
||||||
|
,'country'
|
||||||
|
,'lineage'
|
||||||
|
,'sublineage'
|
||||||
|
,'drtype'
|
||||||
|
, 'pyrazinamide'
|
||||||
|
, 'dr_mutations_pyrazinamide'
|
||||||
|
, 'other_mutations_pyrazinamide'
|
||||||
|
]]
|
||||||
|
|
||||||
|
# checks
|
||||||
|
total_samples = meta_data['id'].nunique() # 19265
|
||||||
|
|
||||||
|
# counts NA per column
|
||||||
|
meta_data.isna().sum()
|
||||||
|
|
||||||
|
# glance
|
||||||
|
meta_data.head()
|
||||||
|
|
||||||
|
# equivalent of table in R
|
||||||
|
# pyrazinamide counts
|
||||||
|
meta_data.pyrazinamide.value_counts()
|
||||||
|
|
||||||
|
#%%
|
||||||
|
############
|
||||||
|
# STEP 2: extract entries containing selected genes:
|
||||||
|
# pyrazinamide: pnca_p.
|
||||||
|
# in the dr_mutations and other mutations"
|
||||||
|
# as we are interested in the mutations in the protein coding region only
|
||||||
|
# (corresponding to a structure)
|
||||||
|
# and drop the entries with NA
|
||||||
|
#############
|
||||||
|
meta_pza = meta_data.loc[meta_data.dr_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
|
||||||
|
meta_pza = meta_data.loc[meta_data.other_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
|
||||||
|
|
||||||
|
del(meta_pza)
|
||||||
|
|
||||||
|
##########################
|
||||||
|
# pyrazinamide: pnca_p.
|
||||||
|
##########################
|
||||||
|
meta_data_pnca = meta_data[['id'
|
||||||
|
,'country'
|
||||||
|
,'lineage'
|
||||||
|
,'sublineage'
|
||||||
|
,'drtype'
|
||||||
|
, 'pyrazinamide'
|
||||||
|
, 'dr_mutations_pyrazinamide'
|
||||||
|
, 'other_mutations_pyrazinamide'
|
||||||
|
]]
|
||||||
|
|
||||||
|
del(meta_data)
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
|
||||||
|
# dr_mutations only
|
||||||
|
meta_pnca_dr = meta_data_pnca.loc[meta_data_pnca.dr_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
|
||||||
|
meta_pnca_dr['id'].nunique()
|
||||||
|
del(meta_pnca_dr)
|
||||||
|
|
||||||
|
# other mutations
|
||||||
|
meta_pnca_other = meta_data_pnca.loc[meta_data_pnca.other_mutations_pyrazinamide.str.contains('pncA_p.*', na = False)]
|
||||||
|
meta_pnca_other['id'].nunique()
|
||||||
|
del(meta_pnca_other)
|
||||||
|
|
||||||
|
# Now extract "all" mutations
|
||||||
|
meta_pnca_all = meta_data_pnca.loc[meta_data_pnca.dr_mutations_pyrazinamide.str.contains('pncA_p.*') | meta_data_pnca.other_mutations_pyrazinamide.str.contains('pncA_p.*') ]
|
||||||
|
|
||||||
|
meta_pnca_all['id'].nunique()
|
||||||
|
pnca_samples = len(meta_pnca_all)
|
||||||
|
pnca_na = meta_pnca_all['pyrazinamide'].isna().sum()
|
||||||
|
comp_pnca_samples = pnca_samples - pnca_na
|
||||||
|
|
||||||
|
#=#=#=#=#=#=#
|
||||||
|
# COMMENT: use it later to check number of complete samples from LF data
|
||||||
|
#=#=#=#=#=#=#
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
meta_pnca_all.dr_mutations_pyrazinamide.value_counts()
|
||||||
|
meta_pnca_all.other_mutations_pyrazinamide.value_counts()
|
||||||
|
|
||||||
|
# more sanity checks
|
||||||
|
# !CAUTION!: muts will change depending on your gene
|
||||||
|
|
||||||
|
# dr muts : insert
|
||||||
|
meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.Gln10Pro')] #
|
||||||
|
meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.Phe106Leu')] # empty
|
||||||
|
meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.Val139Leu')]
|
||||||
|
|
||||||
|
meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.')] # exists # rows
|
||||||
|
m = meta_pnca_all.loc[meta_pnca_all.dr_mutations_pyrazinamide.str.contains('pncA_p.')] # exists # rows
|
||||||
|
|
||||||
|
# other_muts
|
||||||
|
meta_pnca_all.loc[meta_pnca_all.other_mutations_pyrazinamide.str.contains('pncA_p.Gln10Pro*')] # empty
|
||||||
|
meta_pnca_all.loc[meta_pnca_all.other_mutations_pyrazinamide.str.contains('pncA_p.Phe106Leu')]
|
||||||
|
|
||||||
|
#=#=#=#=#=#=#=#=#=#
|
||||||
|
# FIXME
|
||||||
|
# COMMENTS: both mutations columns are separated by ;
|
||||||
|
# CHECK if there are mutations that exist both in dr and other_muts!
|
||||||
|
# doesn't make any sense for the same mut to exist in both, I would have thought!
|
||||||
|
#=#=#=#=#=#=#=#=#=#
|
||||||
|
|
||||||
|
# remove not required variables
|
||||||
|
del(meta_data_pnca)
|
||||||
|
|
||||||
|
############
|
||||||
|
# STEP 3: split the columns of
|
||||||
|
# a) dr_mutation_... (;) as
|
||||||
|
# the column has snps related to multiple genes.
|
||||||
|
# useful links
|
||||||
|
# https://stackoverflow.com/questions/17116814/pandas-how-do-i-split-text-in-a-column-into-multiple-rows
|
||||||
|
# this one works beautifully
|
||||||
|
# https://stackoverflow.com/questions/12680754/split-explode-pandas-dataframe-string-entry-to-separate-rows
|
||||||
|
############
|
||||||
|
|
||||||
|
# sanity check: counts NA per column afer subsetted df: i.e in meta_pza(with pncA_p. extracted mutations)
|
||||||
|
meta_pnca_all.isna().sum()
|
||||||
|
|
||||||
|
#=#=#=#=#=#=#=#=#=#
|
||||||
|
# COMMENT: no NA's in dr_mutations/other_mutations_columns
|
||||||
|
#=#=#=#=#=#=#=#=#=#
|
||||||
|
# define the split function
|
||||||
|
def tidy_split(df, column, sep='|', keep=False):
|
||||||
|
"""
|
||||||
|
Split the values of a column and expand so the new DataFrame has one split
|
||||||
|
value per row. Filters rows where the column is missing.
|
||||||
|
|
||||||
|
Params
|
||||||
|
------
|
||||||
|
df : pandas.DataFrame
|
||||||
|
dataframe with the column to split and expand
|
||||||
|
column : str
|
||||||
|
the column to split and expand
|
||||||
|
sep : str
|
||||||
|
the string used to split the column's values
|
||||||
|
keep : bool
|
||||||
|
whether to retain the presplit value as it's own row
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
pandas.DataFrame
|
||||||
|
Returns a dataframe with the same columns as `df`.
|
||||||
|
"""
|
||||||
|
indexes = list()
|
||||||
|
new_values = list()
|
||||||
|
#df = df.dropna(subset=[column])#<<<<<<-----see this incase you need to uncomment based on use case
|
||||||
|
for i, presplit in enumerate(df[column].astype(str)):
|
||||||
|
values = presplit.split(sep)
|
||||||
|
if keep and len(values) > 1:
|
||||||
|
indexes.append(i)
|
||||||
|
new_values.append(presplit)
|
||||||
|
for value in values:
|
||||||
|
indexes.append(i)
|
||||||
|
new_values.append(value)
|
||||||
|
new_df = df.iloc[indexes, :].copy()
|
||||||
|
new_df[column] = new_values
|
||||||
|
return new_df
|
||||||
|
|
||||||
|
########
|
||||||
|
# 3a: call tidy_split() on 'dr_mutations_pyrazinamide' column and remove leading white spaces
|
||||||
|
#https://stackoverflow.com/questions/41476150/removing-space-from-dataframe-columns-in-pandas
|
||||||
|
########
|
||||||
|
meta_pnca_WF0 = tidy_split(meta_pnca_all, 'dr_mutations_pyrazinamide', sep = ';')
|
||||||
|
|
||||||
|
# remove leading white space else these are counted as distinct mutations as well
|
||||||
|
meta_pnca_WF0['dr_mutations_pyrazinamide'] = meta_pnca_WF0['dr_mutations_pyrazinamide'].str.lstrip()
|
||||||
|
|
||||||
|
########
|
||||||
|
# 3b: call function on 'other_mutations_pyrazinamide' column and remove leading white spaces
|
||||||
|
########
|
||||||
|
meta_pnca_WF1 = tidy_split(meta_pnca_WF0, 'other_mutations_pyrazinamide', sep = ';')
|
||||||
|
|
||||||
|
# remove the leading white spaces in the column
|
||||||
|
meta_pnca_WF1['other_mutations_pyrazinamide'] = meta_pnca_WF1['other_mutations_pyrazinamide'].str.strip()
|
||||||
|
|
||||||
|
##########
|
||||||
|
# Step 4: Reshape data so that all mutations are in one column and the
|
||||||
|
# annotations for the mutation reflect the column name
|
||||||
|
# LINK: http://www.datasciencemadesimple.com/reshape-wide-long-pandas-python-melt-function/
|
||||||
|
|
||||||
|
# data frame “df” is passed to melt() function
|
||||||
|
# id_vars is the variable which need to be left unaltered
|
||||||
|
# var_name are the column names so we named it as 'mutation_info'
|
||||||
|
# value_name are its values so we named it as 'mutation'
|
||||||
|
##########
|
||||||
|
meta_pnca_WF1.columns
|
||||||
|
|
||||||
|
meta_pnca_LF0 = pd.melt(meta_pnca_WF1
|
||||||
|
, id_vars = ['id', 'country', 'lineage', 'sublineage', 'drtype', 'pyrazinamide']
|
||||||
|
, var_name = 'mutation_info'
|
||||||
|
, value_name = 'mutation')
|
||||||
|
|
||||||
|
# sanity check: should be true
|
||||||
|
if len(meta_pnca_LF0) == len(meta_pnca_WF1)*2:
|
||||||
|
print('sanity check passed: Long format df has the expected length')
|
||||||
|
else:
|
||||||
|
print("Sanity check failed: Debug please!")
|
||||||
|
|
||||||
|
###########
|
||||||
|
# Step 5: This is still dirty data. Filter LF data so that you only have
|
||||||
|
# mutations corresponding to pnca_p.
|
||||||
|
# this will be your list you run OR calcs
|
||||||
|
###########
|
||||||
|
meta_pnca_LF1 = meta_pnca_LF0[meta_pnca_LF0['mutation'].str.contains('pncA_p.*')]
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
# unique samples
|
||||||
|
meta_pnca_LF1['id'].nunique()
|
||||||
|
if len(meta_pnca_all) == meta_pnca_LF1['id'].nunique():
|
||||||
|
print("Sanity check passed: No of samples with pncA mutations match")
|
||||||
|
else:
|
||||||
|
print("Sanity check failed: Debug please!")
|
||||||
|
|
||||||
|
# count if all the mutations are indeed in the protein coding region
|
||||||
|
# i.e begin with pnca_p
|
||||||
|
meta_pnca_LF1['mutation'].str.count('pncA_p.').sum() # 3093
|
||||||
|
|
||||||
|
# should be true.
|
||||||
|
# and check against the length of the df, which should match
|
||||||
|
if len(meta_pnca_LF1) == meta_pnca_LF1['mutation'].str.count('pncA_p.').sum():
|
||||||
|
print("Sanity check passed: Long format data containing pnca mutations indeed correspond to pncA_p region")
|
||||||
|
else:
|
||||||
|
print("Sanity check failed: Debug please!")
|
||||||
|
|
||||||
|
###########
|
||||||
|
# Step 6: Filter dataframe with "na" in the drug column
|
||||||
|
# This is because for OR, you can't use the snps that have the
|
||||||
|
# NA in the specified drug column
|
||||||
|
# it creates problems when performing calcs in R inside the loop
|
||||||
|
# so best to filter it here
|
||||||
|
###########
|
||||||
|
# NOT NEEDED FOR all snps, only for extracting valid OR snps
|
||||||
|
del (meta_pnca_WF0, meta_pnca_WF1, meta_pnca_LF0, meta_pnca_all)
|
||||||
|
|
||||||
|
###########
|
||||||
|
# Step 7: count unique pncA_p mutations (all and comp cases)
|
||||||
|
###########
|
||||||
|
meta_pnca_LF1['mutation'].nunique()
|
||||||
|
meta_pnca_LF1.groupby('mutation_info').nunique()
|
||||||
|
|
||||||
|
meta_pnca_LF1['id'].nunique()
|
||||||
|
meta_pnca_LF1['mutation'].nunique()
|
||||||
|
meta_pnca_LF1.groupby('id').nunique()
|
||||||
|
|
||||||
|
###########
|
||||||
|
# Step 8: convert all snps only (IN LOWERCASE)
|
||||||
|
# because my mcsm file intergated has lowercase
|
||||||
|
###########
|
||||||
|
# convert mutation to lower case as it needs to exactly match the dict key
|
||||||
|
#meta_pnca_LF1['mutation'] = meta_pnca_LF1.mutation.str.lower() # WARNINGS: suggested to use .loc
|
||||||
|
meta_pnca_LF1['mutation'] = meta_pnca_LF1.loc[:, 'mutation'].str.lower()
|
||||||
|
|
||||||
|
###########
|
||||||
|
# Step 9 : Split 'mutation' column into three: wild_type, position and
|
||||||
|
# mutant_type separately. Then map three letter code to one from the
|
||||||
|
# referece_dict imported pncaeady. First convert to mutation to lowercase
|
||||||
|
# to allow to match entries from dict
|
||||||
|
###########
|
||||||
|
#=======
|
||||||
|
# Step 9a: iterate through the dict, create a lookup dict i.e
|
||||||
|
# lookup_dict = {three_letter_code: one_letter_code}.
|
||||||
|
# lookup dict should be the key and the value (you want to create a column for)
|
||||||
|
# Then use this to perform the mapping separetly for wild type and mutant cols.
|
||||||
|
# The three letter code is extracted using a regex match from the dataframe and then converted
|
||||||
|
# to 'pandas series'since map only works in pandas series
|
||||||
|
#=======
|
||||||
|
# initialise a sub dict that is a lookup dict for three letter code to one
|
||||||
|
lookup_dict = dict()
|
||||||
|
for k, v in my_aa_dict.items():
|
||||||
|
lookup_dict[k] = v['one_letter_code']
|
||||||
|
wt = meta_pnca_LF1['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
|
||||||
|
meta_pnca_LF1['wild_type'] = wt.map(lookup_dict)
|
||||||
|
mut = meta_pnca_LF1['mutation'].str.extract('\d+(\w{3})$').squeeze()
|
||||||
|
meta_pnca_LF1['mutant_type'] = mut.map(lookup_dict)
|
||||||
|
|
||||||
|
# extract position info from mutation column separetly using regex
|
||||||
|
meta_pnca_LF1['position'] = meta_pnca_LF1['mutation'].str.extract(r'(\d+)')
|
||||||
|
|
||||||
|
# clear variables
|
||||||
|
del(k, v, wt, mut, lookup_dict)
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# Step 9b: iterate through the dict, create a lookup dict that i.e
|
||||||
|
# lookup_dict = {three_letter_code: aa_prop_water}
|
||||||
|
# Do this for both wild_type and mutant as above.
|
||||||
|
#=========
|
||||||
|
# initialise a sub dict that is lookup dict for three letter code to aa prop
|
||||||
|
lookup_dict = dict()
|
||||||
|
|
||||||
|
for k, v in my_aa_dict.items():
|
||||||
|
lookup_dict[k] = v['aa_prop_water']
|
||||||
|
#print(lookup_dict)
|
||||||
|
wt = meta_pnca_LF1['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
|
||||||
|
meta_pnca_LF1['wt_prop_water'] = wt.map(lookup_dict)
|
||||||
|
mut = meta_pnca_LF1['mutation'].str.extract('\d+(\w{3})$').squeeze()
|
||||||
|
meta_pnca_LF1['mut_prop_water'] = mut.map(lookup_dict)
|
||||||
|
|
||||||
|
# added two more cols
|
||||||
|
|
||||||
|
# clear variables
|
||||||
|
del(k, v, wt, mut, lookup_dict)
|
||||||
|
|
||||||
|
#========
|
||||||
|
# Step 9c: iterate through the dict, create a lookup dict that i.e
|
||||||
|
# lookup_dict = {three_letter_code: aa_prop_polarity}
|
||||||
|
# Do this for both wild_type and mutant as above.
|
||||||
|
#=========
|
||||||
|
# initialise a sub dict that is lookup dict for three letter code to aa prop
|
||||||
|
lookup_dict = dict()
|
||||||
|
|
||||||
|
for k, v in my_aa_dict.items():
|
||||||
|
lookup_dict[k] = v['aa_prop_polarity']
|
||||||
|
#print(lookup_dict)
|
||||||
|
wt = meta_pnca_LF1['mutation'].str.extract('pnca_p.(\w{3})').squeeze() # converts to a series that map works on
|
||||||
|
meta_pnca_LF1['wt_prop_polarity'] = wt.map(lookup_dict)
|
||||||
|
mut = meta_pnca_LF1['mutation'].str.extract(r'\d+(\w{3})$').squeeze()
|
||||||
|
meta_pnca_LF1['mut_prop_polarity'] = mut.map(lookup_dict)
|
||||||
|
|
||||||
|
# added two more cols
|
||||||
|
|
||||||
|
# clear variables
|
||||||
|
del(k, v, wt, mut, lookup_dict)
|
||||||
|
|
||||||
|
########
|
||||||
|
# Step 10: combine the wild_type+poistion+mutant_type columns to generate
|
||||||
|
# Mutationinformation (matches mCSM output field)
|
||||||
|
# Remember to use .map(str) for int col types to allow string concatenation
|
||||||
|
#########
|
||||||
|
meta_pnca_LF1['Mutationinformation'] = meta_pnca_LF1['wild_type'] + meta_pnca_LF1.position.map(str) + meta_pnca_LF1['mutant_type']
|
||||||
|
|
||||||
|
#=#=#=#=#=#=#
|
||||||
|
# Step 11:
|
||||||
|
# COMMENT: there is more processing in the older version of this script
|
||||||
|
# consult if necessary
|
||||||
|
# possibly due to the presence of true_wt
|
||||||
|
# since this file doesn't contain any true_wt, we won't need it(hopefully!)
|
||||||
|
#=#=#=#=#=#=#
|
||||||
|
|
||||||
|
#%%
|
||||||
|
###########
|
||||||
|
# Step 12: Output files for only SNPs to run mCSM
|
||||||
|
###########
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# Step 12a: all SNPs to run mCSM
|
||||||
|
#=========
|
||||||
|
snps_only = pd.DataFrame(meta_pnca_LF1['Mutationinformation'].unique())
|
||||||
|
pos_only = pd.DataFrame(meta_pnca_LF1['position'].unique())
|
||||||
|
|
||||||
|
# assign meaningful colnames
|
||||||
|
#snps_only.rename({0 : 'all_pnca_snps'}, axis = 1, inplace = True)
|
||||||
|
#list(snps_only.columns)
|
||||||
|
snps_only.isna().sum() # should be 0
|
||||||
|
|
||||||
|
# output csv: all SNPS for mCSM analysis
|
||||||
|
# specify variable name for output file
|
||||||
|
gene = 'pnca'
|
||||||
|
#drug = 'pyrazinamide'
|
||||||
|
my_fname1 = '_snps_'
|
||||||
|
nrows = len(snps_only)
|
||||||
|
|
||||||
|
#output_file_path = '/home/tanu/git/Data/input/processed/pyrazinamide/'
|
||||||
|
#output_file_path = work_dir + '/Data/'
|
||||||
|
output_file_path = data_dir + '/input/processed/' + drug + '/'
|
||||||
|
|
||||||
|
if not os.path.exists(output_file_path):
|
||||||
|
print( output_file_path, 'does not exist. Creating')
|
||||||
|
os.makedirs(output_file_path)
|
||||||
|
exit()
|
||||||
|
|
||||||
|
output_filename = output_file_path + gene + my_fname1 + str(nrows) + '.csv'
|
||||||
|
print(output_filename) #<<<- check
|
||||||
|
|
||||||
|
# write to csv: without column or row names
|
||||||
|
# Bad practice: numbers at the start of a filename
|
||||||
|
snps_only.to_csv(output_filename, header = False, index = False)
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# Step 12b: all snps with annotation
|
||||||
|
#=========
|
||||||
|
# all snps, selected cols
|
||||||
|
#pnca_snps_ALL = meta_pnca_LF1[['id','country','lineage', 'sublineage'
|
||||||
|
# , 'drtype', 'pyrazinamide'
|
||||||
|
# , 'mutation_info', 'mutation', 'Mutationinformation']]
|
||||||
|
|
||||||
|
#len(pnca_snps_ALL)
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
#meta_pnca_LF1['mutation'].nunique()
|
||||||
|
|
||||||
|
# output csv: WITH column but WITHOUT row names(all snps with meta data)
|
||||||
|
# specify variable name for output file
|
||||||
|
#gene = 'pnca'
|
||||||
|
#drug = 'pyrazinamide'
|
||||||
|
#my_fname2 = '_snps_with_metadata_'
|
||||||
|
#nrows = len(pnca_snps_ALL)
|
||||||
|
|
||||||
|
#output_file_path = work_dir + '/Data/'
|
||||||
|
#output_filename = output_file_path + gene + my_fname2 + str(nrows) + '.csv'
|
||||||
|
#print(output_filename) #<<<- check
|
||||||
|
|
||||||
|
# write out file
|
||||||
|
#pnca_snps_ALL.to_csv(output_filename, header = True, index = False)
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# Step 12c: comp snps for OR calcs with annotation
|
||||||
|
#=========
|
||||||
|
# remove all NA's from pyrazinamide column from LF1
|
||||||
|
|
||||||
|
# counts NA per column
|
||||||
|
meta_pnca_LF1.isna().sum()
|
||||||
|
|
||||||
|
# remove NA
|
||||||
|
meta_pnca_LF2 = meta_pnca_LF1.dropna(subset=['pyrazinamide'])
|
||||||
|
|
||||||
|
# sanity checks
|
||||||
|
# should be True
|
||||||
|
len(meta_pnca_LF2) == len(meta_pnca_LF1) - meta_pnca_LF1['pyrazinamide'].isna().sum()
|
||||||
|
|
||||||
|
# unique counts
|
||||||
|
meta_pnca_LF2['mutation'].nunique()
|
||||||
|
|
||||||
|
meta_pnca_LF2.groupby('mutation_info').nunique()
|
||||||
|
|
||||||
|
# sanity check
|
||||||
|
meta_pnca_LF2['id'].nunique()
|
||||||
|
|
||||||
|
# should be True
|
||||||
|
if meta_pnca_LF2['id'].nunique() == comp_pnca_samples:
|
||||||
|
print ('sanity check passed: complete numbers match')
|
||||||
|
else:
|
||||||
|
print('Error: Please Debug!')
|
||||||
|
|
||||||
|
# value counts
|
||||||
|
meta_pnca_LF2.mutation.value_counts()
|
||||||
|
#meta_pnca_LF2.groupby(['mutation_info', 'mutation']).size()
|
||||||
|
|
||||||
|
# valid/comp snps
|
||||||
|
# uncomment as necessary
|
||||||
|
pnca_snps_COMP = pd.DataFrame(meta_pnca_LF2['Mutationinformation'].unique())
|
||||||
|
len(pnca_snps_COMP)
|
||||||
|
|
||||||
|
# output csv: WITH column but WITHOUT row names (COMP snps with meta data)
|
||||||
|
# specify variable name for output file
|
||||||
|
|
||||||
|
gene = 'pnca'
|
||||||
|
#drug = 'pyrazinamide'
|
||||||
|
my_fname3 = '_comp_snps_with_metadata_'
|
||||||
|
nrows = len(pnca_snps_COMP)
|
||||||
|
|
||||||
|
#output_filename = output_file_path + gene + my_fname3 + str(nrows) + '.csv'
|
||||||
|
#print(output_filename) #<<<-check
|
||||||
|
|
||||||
|
# write out file
|
||||||
|
#pnca_snps_COMP.to_csv(output_filename, header = True, index = False)
|
||||||
|
|
||||||
|
|
||||||
|
#=========
|
||||||
|
# Step 12d: comp snps only
|
||||||
|
#=========
|
||||||
|
# output csv: comp SNPS for info (i.e snps for which OR exist)
|
||||||
|
# specify variable name for output file
|
||||||
|
|
||||||
|
snps_only = pd.DataFrame(meta_pnca_LF2['Mutationinformation'].unique())
|
||||||
|
|
||||||
|
gene = 'pnca'
|
||||||
|
#drug = 'pyrazinamide'
|
||||||
|
my_fname1 = '_comp_snps_'
|
||||||
|
nrows = len(snps_only)
|
||||||
|
|
||||||
|
output_filename = output_file_path + gene + my_fname1 + str(nrows) + '.csv'
|
||||||
|
print(output_filename) #<<<- check
|
||||||
|
|
||||||
|
# write to csv: without column or row names
|
||||||
|
snps_only.to_csv(output_filename, header = False, index = False)
|
||||||
|
|
||||||
|
|
||||||
|
#=#=#=#=#=#=#=#
|
||||||
|
# COMMENT: LF1 is the file to extract all unique snps for mcsm
|
||||||
|
# but you have that already in file called pnca_snps...
|
||||||
|
# LF2: is the file for extracting snps tested for DS and hence OR calcs
|
||||||
|
#=#=#=#=#=#=#=#
|
||||||
|
|
||||||
|
###########
|
||||||
|
# Step 13 : Output the whole df i.e
|
||||||
|
# file for meta_data which is now formatted with
|
||||||
|
# each row as a unique snp rather than the original version where
|
||||||
|
# each row is a unique id
|
||||||
|
# ***** This is the file you will ADD the AF and OR calculations to *****
|
||||||
|
###########
|
||||||
|
|
||||||
|
# output csv: the entire DF
|
||||||
|
# specify variable name for output file
|
||||||
|
gene = 'pnca'
|
||||||
|
#drug = 'pyrazinamide'
|
||||||
|
my_fname4 = '_metadata'
|
||||||
|
#nrows = len(meta_pnca_LF1)
|
||||||
|
output_filename = output_file_path + gene + my_fname4 + '.csv'
|
||||||
|
print(output_filename) #<<<-check
|
||||||
|
|
||||||
|
# write out file
|
||||||
|
meta_pnca_LF1.to_csv(output_filename)
|
121
meta_data_analysis/reference_dict.py
Executable file
121
meta_data_analysis/reference_dict.py
Executable file
|
@ -0,0 +1,121 @@
|
||||||
|
#!/usr/bin/env python3
|
||||||
|
# -*- coding: utf-8 -*-
|
||||||
|
"""
|
||||||
|
Created on Tue Jun 18 11:32:28 2019
|
||||||
|
|
||||||
|
@author: tanushree
|
||||||
|
"""
|
||||||
|
############################################
|
||||||
|
#load libraries
|
||||||
|
import pandas as pd
|
||||||
|
import os
|
||||||
|
#############################################
|
||||||
|
|
||||||
|
#!#########################!
|
||||||
|
# REQUIREMNETS:
|
||||||
|
# Data_original/ must exist
|
||||||
|
# containing GWAS data
|
||||||
|
#!#########################!
|
||||||
|
|
||||||
|
print(os.getcwd())
|
||||||
|
homedir = os.path.expanduser('~') # spyder/python doesn't recognise tilde
|
||||||
|
os.chdir(homedir + '/git/Data/input/original')
|
||||||
|
print(os.getcwd())
|
||||||
|
#==========
|
||||||
|
#read file
|
||||||
|
#==========
|
||||||
|
my_aa = pd.read_csv('aa_codes.csv') #20, 6
|
||||||
|
#assign the one_letter code as the row names so that it is easier to create a dict of dicts using index
|
||||||
|
#my_aa = pd.read_csv('aa_codes.csv', index_col = 0) #20, 6 #a way to it since it is the first column
|
||||||
|
my_aa = my_aa.set_index('three_letter_code_lower') #20, 5
|
||||||
|
|
||||||
|
#=========================================================
|
||||||
|
#convert file to dict of dicts
|
||||||
|
#=========================================================
|
||||||
|
#convert each row into a dict of dicts so that there are 20 aa and 5 keys within
|
||||||
|
#with your choice of column name that you have assigned to index as the "primary key".
|
||||||
|
#using 'index' creates a dict of dicts
|
||||||
|
#using 'records' creates a list of dicts
|
||||||
|
my_aa_dict = my_aa.to_dict('index') #20, with 5 subkeys
|
||||||
|
|
||||||
|
#================================================
|
||||||
|
#dict of aa with their corresponding properties
|
||||||
|
#This is defined twice
|
||||||
|
#================================================
|
||||||
|
#7 categories: no overlap
|
||||||
|
qualities1 = { ('R', 'H', 'K'): 'Basic'
|
||||||
|
, ('D', 'E'): 'Acidic'
|
||||||
|
, ('N', 'Q'): 'Amidic'
|
||||||
|
, ('G', 'A', 'V', 'L', 'I', 'P'): 'Hydrophobic'
|
||||||
|
, ('S', 'T'): 'Hydroxylic'
|
||||||
|
, ('F', 'W', 'Y'): 'Aromatic'
|
||||||
|
, ('C', 'M'): 'Sulphur'
|
||||||
|
}
|
||||||
|
|
||||||
|
#9 categories: allowing for overlap
|
||||||
|
qualities2 = { ('R', 'H', 'K'): 'Basic'
|
||||||
|
, ('D', 'E'): 'Acidc'
|
||||||
|
, ('S', 'T', 'N', 'Q'): 'Polar'
|
||||||
|
, ('V', 'I', 'L', 'M', 'F', 'Y', 'W'): 'Hydrophobic'
|
||||||
|
, ('S', 'T', 'H', 'N', 'Q', 'E', 'D', 'K', 'R'): 'Hydrophilic'
|
||||||
|
, ('S', 'G', 'A', 'P'): 'Small'
|
||||||
|
, ('F', 'W', 'Y', 'H'): 'Aromatic'
|
||||||
|
, ('V', 'I', 'L', 'M'): 'Aliphatic'
|
||||||
|
, ('C', 'G', 'P'): 'Special'
|
||||||
|
}
|
||||||
|
|
||||||
|
qualities_taylor = { ('R', 'H', 'K'): 'Basic'
|
||||||
|
, ('D', 'E'): 'Acidc'
|
||||||
|
, ('S', 'T', 'N', 'Q', 'C', 'Y', 'W', 'H', 'K', 'R', 'D', 'E'): 'Polar'
|
||||||
|
, ('V', 'I', 'L', 'M', 'F', 'Y', 'W', 'C', 'A', 'G', 'T', 'H'): 'Hydrophobic'
|
||||||
|
#, ('S', 'T', 'H', 'N', 'Q', 'E', 'D', 'K', 'R'): 'Hydrophilic', #C, W, y MISSING FROM POLAR!
|
||||||
|
, ('S', 'G', 'A', 'P', 'C', 'T', 'N', 'D', 'V'): 'Small'
|
||||||
|
, ('F', 'W', 'Y', 'H'): 'Aromatic'
|
||||||
|
, ('V', 'I', 'L', 'M'): 'Aliphatic' #although M is not strictly in the circle!
|
||||||
|
, ('C', 'G', 'P'): 'Special'
|
||||||
|
}
|
||||||
|
|
||||||
|
qualities_water = { ('D', 'E', 'N', 'P', 'Q', 'R', 'S'): 'hydrophilic'
|
||||||
|
, ('A', 'C', 'F', 'G', 'H', 'I', 'K', 'L', 'M', 'T', 'V', 'W', 'X', 'Y'): 'hydrophobic'
|
||||||
|
}
|
||||||
|
|
||||||
|
qualities_polarity = { ('D', 'E'): 'acidic'
|
||||||
|
, ('H', 'K', 'R'): 'basic'
|
||||||
|
, ('C', 'G', 'N', 'Q', 'S', 'T', 'Y'): 'neutral'
|
||||||
|
, ('A', 'F', 'I', 'L', 'M', 'P', 'V', 'W'): 'non-polar'
|
||||||
|
}
|
||||||
|
|
||||||
|
#==============================================================================
|
||||||
|
#adding amino acid properties to my dict of dicts
|
||||||
|
for k, v in my_aa_dict.items():
|
||||||
|
#print (k,v)
|
||||||
|
v['aa_prop1'] = str() #initialise keys
|
||||||
|
v['aa_prop2'] = list() #initialise keys (allows for overalpping properties)
|
||||||
|
v['aa_taylor'] = list() #initialise keys (allows for overalpping properties)
|
||||||
|
v['aa_prop_water'] = str() #initialise keys
|
||||||
|
v['aa_prop_polarity'] = str() #initialise keys
|
||||||
|
|
||||||
|
for group in qualities1:
|
||||||
|
if v['one_letter_code'] in group:
|
||||||
|
v['aa_prop1']+= qualities1[group] # += for str concat
|
||||||
|
|
||||||
|
for group in qualities2:
|
||||||
|
if v['one_letter_code'] in group:
|
||||||
|
v['aa_prop2'].append(qualities2[group]) # append to list
|
||||||
|
|
||||||
|
for group in qualities_taylor:
|
||||||
|
if v['one_letter_code'] in group:
|
||||||
|
v['aa_taylor'].append(qualities_taylor[group]) # append to list
|
||||||
|
|
||||||
|
for group in qualities_water:
|
||||||
|
if v['one_letter_code'] in group:
|
||||||
|
v['aa_prop_water']+= qualities_water[group] # += for str concat
|
||||||
|
|
||||||
|
for group in qualities_polarity:
|
||||||
|
if v['one_letter_code'] in group:
|
||||||
|
v['aa_prop_polarity']+= qualities_polarity[group] # += for str concat
|
||||||
|
|
||||||
|
#COMMENT:VOILA!!! my_aa_dict is now a dict of dicts containing all associated properties for each aa
|
||||||
|
#==============================================================================
|
||||||
|
|
||||||
|
|
40
mk-drug-dirs.sh
Executable file
40
mk-drug-dirs.sh
Executable file
|
@ -0,0 +1,40 @@
|
||||||
|
#!/bin/bash
|
||||||
|
# Create a drug directory structure for processing
|
||||||
|
#
|
||||||
|
#
|
||||||
|
# Structure:
|
||||||
|
#
|
||||||
|
# $DATA_DIR/$DRUG/input
|
||||||
|
# |- original
|
||||||
|
# |- processed
|
||||||
|
# |- structure
|
||||||
|
#
|
||||||
|
# $DATA_DIR/$DRUG/output
|
||||||
|
# |- plots
|
||||||
|
# |- structure
|
||||||
|
|
||||||
|
DATA_DIR=~/git/Data
|
||||||
|
|
||||||
|
if [[ $1 == '' ]]; then
|
||||||
|
echo "usage: mk-drug-dirs.sh <drug name>";
|
||||||
|
exit;
|
||||||
|
else
|
||||||
|
DRUG=$1
|
||||||
|
echo Creating structure for: $DRUG
|
||||||
|
|
||||||
|
if [ -d $DATA_DIR ]
|
||||||
|
then
|
||||||
|
echo Doing creation in $DATA_DIR
|
||||||
|
mkdir -p $DATA_DIR/$DRUG/input/original
|
||||||
|
mkdir -p $DATA_DIR/$DRUG/input/processed
|
||||||
|
mkdir -p $DATA_DIR/$DRUG/input/structure
|
||||||
|
mkdir -p $DATA_DIR/$DRUG/output/plots
|
||||||
|
mkdir -p $DATA_DIR/$DRUG/output/structure
|
||||||
|
|
||||||
|
else
|
||||||
|
echo "Error: $DATA_DIR does not exist. Did you check it out of git?"
|
||||||
|
exit
|
||||||
|
fi
|
||||||
|
|
||||||
|
fi
|
||||||
|
|
Loading…
Add table
Add a link
Reference in a new issue