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graphs for PS lineage dist for all and dr muts

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Tanu 2020-01-22 10:12:09 +00:00 • committed by Tanushree Tunstall
parent ec37e3c1f6
commit 4f06e42ee4
4 changed files with 93 additions and 567 deletions
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509
mcsm_analysis/pyrazinamide/scripts/.Rhistory
View file

@ -1,512 +1,7 @@
###########################
# 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$Position)
write.csv(combined_df, outFile
, row.names = F)
getwd() getwd()
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting") setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
getwd() getwd()
########################################################################
# Installing and loading required packages #
########################################################################
source("../Header_TT.R") source("../Header_TT.R")
#source("barplot_colour_function.R")
require(data.table)
require(dplyr)
########################################################################
# Read file: call script for combining df for PS #
########################################################################
source("../combining_two_df.R") source("../combining_two_df.R")
########################### source("../combining_two_df.R")
# This will return: source("../combining_two_df.R")
# df with NA:
# merged_df2
# merged_df3
# df without NA:
# merged_df2_comp
# merged_df3_comp
###########################
#---------------------- PAY ATTENTION
# the above changes the working dir
#[1] "git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts"
#---------------------- PAY ATTENTION
###########################
# you need merged_df3
# or
# merged_df3_comp
# since these have unique SNPs
# I prefer to use the merged_df3
# because using the _comp dataset means
# we lose some muts and at this level, we should use
# as much info as available
###########################
# uncomment as necessary
#%%%%%%%%%%%%%%%%%%%%%%%%
# REASSIGNMENT
my_df = merged_df3
#my_df = merged_df3_comp
#%%%%%%%%%%%%%%%%%%%%%%%%
# delete variables not required
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
# quick checks
colnames(my_df)
str(my_df)
###########################
# Data for bfactor figure
# PS average
# Lig average
###########################
head(my_df$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)

31
mcsm_analysis/pyrazinamide/scripts/Header_TT.R
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@ -1,25 +1,31 @@
######################################################### #########################################################
### A) Installing and loading required packages ### A) Installing and loading required packages
######################################################### #########################################################
#lib_loc = "/usr/local/lib/R/site-library")
#if (!require("gplots")) { #if (!require("gplots")) {
# install.packages("gplots", dependencies = TRUE) # install.packages("gplots", dependencies = TRUE)
# library(gplots) # library(gplots)
#} #}
if (!require("tidyverse")) { #if (!require("tidyverse")) {
install.packages("tidyverse", dependencies = TRUE) # install.packages("tidyverse", dependencies = TRUE)
library(tidyverse) # library(tidyverse)
} #}
if (!require("ggplot2")) { if (!require("ggplot2")) {
install.packages("ggplot2", dependencies = TRUE) install.packages("ggplot2", dependencies = TRUE)
library(ggplot2) library(ggplot2)
} }
if (!require("plotly")) {
install.packages("plotly", dependencies = TRUE)
library(plotly)
}
if (!require("cowplot")) { if (!require("cowplot")) {
install.packages("copwplot", dependencies = TRUE) install.packages("copwplot", dependencies = TRUE)
library(ggplot2) library(cowplot)
} }
if (!require("ggcorrplot")) { if (!require("ggcorrplot")) {
@ -43,37 +49,33 @@ if (!require ("GOplot")) {
} }
if(!require("VennDiagram")) { if(!require("VennDiagram")) {
install.packages("VennDiagram", dependencies = T) install.packages("VennDiagram", dependencies = T)
library(VennDiagram) library(VennDiagram)
} }
if(!require("scales")) { if(!require("scales")) {
install.packages("scales", dependencies = T) install.packages("scales", dependencies = T)
library(scales) library(scales)
} }
if(!require("plotrix")) { if(!require("plotrix")) {
install.packages("plotrix", dependencies = T) install.packages("plotrix", dependencies = T)
library(plotrix) library(plotrix)
} }
if(!require("stats")) { if(!require("stats")) {
install.packages("stats", dependencies = T) install.packages("stats", dependencies = T)
library(stats) library(stats)
} }
if(!require("stats4")) { if(!require("stats4")) {
install.packages("stats4", dependencies = T) install.packages("stats4", dependencies = T)
library(stats4) library(stats4)
} }
if(!require("data.table")) { if(!require("data.table")) {
library(stats4) install.packages("data.table")
library(data.table)
} }
if (!require("PerformanceAnalytics")){ if (!require("PerformanceAnalytics")){
@ -98,18 +100,17 @@ if (!require ("psych")){
if (!require ("dplyr")){ if (!require ("dplyr")){
install.packages("dplyr") install.packages("dplyr")
library(psych) library(dplyr)
} }
if (!require ("compare")){ if (!require ("compare")){
install.packages("compare") install.packages("compare")
library(psych) library(compare)
} }
if (!require ("arsenal")){ if (!require ("arsenal")){
install.packages("arsenal") install.packages("arsenal")
library(psych) library(arsenal)
} }

4
mcsm_analysis/pyrazinamide/scripts/combining_two_df.R
View file

@ -11,7 +11,7 @@ getwd()
# Installing and loading required packages # # Installing and loading required packages #
######################################################################## ########################################################################
source("Header_TT.R") #source("Header_TT.R")
#require(data.table) #require(data.table)
#require(arsenal) #require(arsenal)
#require(compare) #require(compare)
@ -286,7 +286,7 @@ outDir = "~/git/Data/pyrazinamide/output/"
getwd() getwd()
outFile1 = paste0(outDir, "merged_df3.csv"); outFile1 outFile1 = paste0(outDir, "merged_df3.csv"); outFile1
write.csv(merged_df3, outFile1) #write.csv(merged_df3, outFile1)
#outFile2 = paste0(outDir, "merged_df3_comp.csv"); outFile2 #outFile2 = paste0(outDir, "merged_df3_comp.csv"); outFile2
#write.csv(merged_df3_comp, outFile2) #write.csv(merged_df3_comp, outFile2)

100
mcsm_analysis/pyrazinamide/scripts/plotting/lineage_dist_PS.R
View file

@ -1,5 +1,5 @@
getwd() getwd()
setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting") # thinkpad setwd("~/git/LSHTM_analysis/mcsm_analysis/pyrazinamide/scripts/plotting")
getwd() getwd()
######################################################################## ########################################################################
@ -24,11 +24,11 @@ source("../combining_two_df.R")
#========================== #==========================
# This will return: # This will return:
# df with NA: # df with NA for pyrazinamide:
# merged_df2 # merged_df2
# merged_df3 # merged_df3
# df without NA: # df without NA for pyrazinamide:
# merged_df2_comp # merged_df2_comp
# merged_df3_comp # merged_df3_comp
#=========================== #===========================
@ -38,14 +38,17 @@ source("../combining_two_df.R")
# you need merged_df2 or merged_df2_comp # you need merged_df2 or merged_df2_comp
# since this is one-many relationship # since this is one-many relationship
# i.e the same SNP can belong to multiple lineages # i.e the same SNP can belong to multiple lineages
# using the _comp dataset means
# we lose some muts and at this level, we should use
# as much info as available, hence use df with NA
########################### ###########################
# uncomment as necessary # uncomment as necessary
#<<<<<<<<<<<<<<<<<<<<<<<<< #!!!!!!!!!!!!!!!!!!!!!!!
# REASSIGNMENT # REASSIGNMENT
my_df = merged_df2 my_df = merged_df2
#my_df = merged_df2_comp #my_df = merged_df2_comp
#<<<<<<<<<<<<<<<<<<<<<<<<< #!!!!!!!!!!!!!!!!!!!!!!!
# delete variables not required # delete variables not required
rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp) rm(merged_df2, merged_df2_comp, merged_df3, merged_df3_comp)
@ -59,12 +62,39 @@ is.factor(my_df$lineage)
my_df$lineage = as.factor(my_df$lineage) my_df$lineage = as.factor(my_df$lineage)
is.factor(my_df$lineage) is.factor(my_df$lineage)
table(my_df$mutation_info) table(my_df$mutation_info); str(my_df$mutation_info)
# subset df with dr muts only
my_df_dr = subset(my_df, mutation_info == "dr_mutations_pyrazinamide")
######################################################################## ########################################################################
# end of data extraction and cleaning for plots # # end of data extraction and cleaning for plots #
######################################################################## ########################################################################
#==========================
# Data for plot: assign as
# necessary
#===========================
# uncomment as necessary
#!!!!!!!!!!!!!!!!!!!!!!!
# REASSIGNMENT
#==================
# data for ALL muts
#==================
plot_df = my_df
my_plot_name = 'lineage_dist_PS.svg'
#my_plot_name = 'lineage_dist_PS_comp.svg'
#=======================
# data for dr_muts ONLY
#=======================
#plot_df = my_df_dr
#my_plot_name = 'lineage_dist_dr_PS.svg'
#my_plot_name = 'lineage_dist_dr_PS_comp.svg'
#!!!!!!!!!!!!!!!!!!!!!!!
#========================== #==========================
# Plot: Lineage Distribution # Plot: Lineage Distribution
# x = mcsm_values, y = dist # x = mcsm_values, y = dist
@ -74,6 +104,7 @@ table(my_df$mutation_info)
#=================== #===================
# Data for plots # Data for plots
#=================== #===================
table(plot_df$lineage); str(plot_df$lineage)
# subset only lineages1-4 # subset only lineages1-4
sel_lineages = c("lineage1" sel_lineages = c("lineage1"
@ -82,34 +113,29 @@ sel_lineages = c("lineage1"
, "lineage4") , "lineage4")
# uncomment as necessary # uncomment as necessary
df_lin = subset(my_df, subset = lineage %in% sel_lineages ) df_lin = subset(plot_df, subset = lineage %in% sel_lineages )
# refactor # refactor
df_lin$lineage = factor(df_lin$lineage) df_lin$lineage = factor(df_lin$lineage)
table(df_lin$lineage) #{RESULT: No of samples within lineage} table(df_lin$lineage) #{RESULT: No of samples within lineage}
#lineage1 lineage2 lineage3 lineage4 #lineage1 lineage2 lineage3 lineage4
#104 1293 264 1311
# when merged_df2_comp is used
#lineage1 lineage2 lineage3 lineage4
#99 1275 263 1255
length(unique(df_lin$Mutationinformation)) length(unique(df_lin$Mutationinformation))
#{Result: No. of unique mutations the 4 lineages contribute to} #{Result: No. of unique mutations the 4 lineages contribute to}
# sanity checks # sanity checks
r1 = 2:5 # when merged_df2 used: because there is missing lineages r1 = 2:5 # when merged_df2 used: because there is missing lineages
if(sum(table(my_df$lineage)[r1]) == nrow(df_lin)) { if(sum(table(plot_df$lineage)[r1]) == nrow(df_lin)) {
print ("sanity check passed: numbers match") print ("sanity check passed: numbers match")
} else{ } else{
print("Error!: check your numbers") print("Error!: check your numbers")
} }
#<<<<<<<<<<<<<<<<<<<<<<<<< #!!!!!!!!!!!!!!!!!!!!!!!!!
# REASSIGNMENT # REASSIGNMENT
df <- df_lin df <- df_lin
#<<<<<<<<<<<<<<<<<<<<<<<<< #!!!!!!!!!!!!!!!!!!!!!!!!!
rm(df_lin) rm(df_lin)
@ -117,8 +143,8 @@ rm(df_lin)
# generate distribution plot of lineages # generate distribution plot of lineages
#****************** #******************
# basic: could improve this! # basic: could improve this!
library(plotly) #library(plotly)
library(ggridges) #library(ggridges)
g <- ggplot(df, aes(x = ratioDUET)) + g <- ggplot(df, aes(x = ratioDUET)) +
geom_density(aes(fill = DUET_outcome) geom_density(aes(fill = DUET_outcome)
@ -129,42 +155,44 @@ g <- ggplot(df, aes(x = ratioDUET)) +
ggplotly(g) ggplotly(g)
# 2 : ggridges (good!) # 2 : ggridges (good!)
my_ats = 15 # axis text size my_ats = 15 # axis text size
my_als = 20 # axis label size my_als = 20 # axis label size
fooNames=c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4') my_labels = c('Lineage 1', 'Lineage 2', 'Lineage 3', 'Lineage 4')
names(fooNames)=c('lineage1', 'lineage2', 'lineage3', 'lineage4') names(my_labels) = c('lineage1', 'lineage2', 'lineage3', 'lineage4')
# set output dir for plots # set output dir for plots
getwd() getwd()
setwd("~/git/Data/pyrazinamide/output/plots") setwd("~/git/Data/pyrazinamide/output/plots")
getwd() getwd()
svg('lineage_dist_PS.svg') # check plot name
my_plot_name
printFile = ggplot( df, aes(x = ratioDUET # output svg
, y = DUET_outcome) )+ svg(my_plot_name)
printFile = ggplot(df, aes(x = ratioDUET
, y = DUET_outcome))+
#printFile=geom_density_ridges_gradient( #printFile=geom_density_ridges_gradient(
geom_density_ridges_gradient( aes(fill = ..x..) geom_density_ridges_gradient(aes(fill = ..x..)
, scale = 3 , scale = 3
, size = 0.3 ) + , size = 0.3 ) +
facet_wrap( ~lineage facet_wrap( ~lineage
, scales = "free" , scales = "free"
# , switch = 'x' # , switch = 'x'
, labeller = labeller(lineage = fooNames) ) + , labeller = labeller(lineage = my_labels) ) +
coord_cartesian( xlim = c(-1, 1) coord_cartesian( xlim = c(-1, 1)
# , ylim = c(0, 6) # , ylim = c(0, 6)
# , clip = "off" # , clip = "off"
) + ) +
scale_fill_gradientn( colours = c("#f8766d", "white", "#00bfc4") scale_fill_gradientn(colours = c("#f8766d", "white", "#00bfc4")
, name = "DUET" ) + , name = "DUET" ) +
theme( axis.text.x = element_text( size = my_ats theme(axis.text.x = element_text(size = my_ats
, angle = 90 , angle = 90
, hjust = 1 , hjust = 1
, vjust = 0.4) , vjust = 0.4)
# , axis.text.y = element_text( size = my_ats # , axis.text.y = element_text(size = my_ats
# , angle = 0 # , angle = 0
# , hjust = 1 # , hjust = 1
# , vjust = 0) # , vjust = 0)
@ -173,9 +201,9 @@ printFile = ggplot( df, aes(x = ratioDUET
, axis.title.y = element_blank() , axis.title.y = element_blank()
, axis.ticks.y = element_blank() , axis.ticks.y = element_blank()
, plot.title = element_blank() , plot.title = element_blank()
, strip.text = element_text(size=my_als) , strip.text = element_text(size = my_als)
, legend.text = element_text(size=10) , legend.text = element_text(size = 10)
, legend.title = element_text(size=my_als) , legend.title = element_text(size = my_als)
# , legend.position = c(0.3, 0.8) # , legend.position = c(0.3, 0.8)
# , legend.key.height = unit(1, 'mm') # , legend.key.height = unit(1, 'mm')
) )
@ -183,10 +211,12 @@ printFile = ggplot( df, aes(x = ratioDUET
print(printFile) print(printFile)
dev.off() dev.off()
#=!=!=!=!=!=! #=!=!=!=!=!=!=!
# COMMENT: When you look at all mutations, the lineage differences disappear... # COMMENT: Not much differences in the distributions
# when using merged_df2 or merged_df2_comp.
# Also, the lineage differences disappear when looking at all muts
# The pattern we are interested in is possibly only for dr_mutations # The pattern we are interested in is possibly only for dr_mutations
#=!=!=!=!=!=! #=!=!=!=!=!=!=!
#=================================================== #===================================================
# COMPARING DISTRIBUTIONS # COMPARING DISTRIBUTIONS