library(ggplot2)
library(multcomp)
library(survminer)
library(coxme)
library(tidyverse)
library(wrappedtools)
library(growthcurver)
library(rstatix)
library(glmmADMB)

FoodChoice <- read.delim("C:/") #set to file location
attach(FoodChoice)

#Testing for normal distribution and calculating medians
as_tibble(FoodChoice)
rawdata <- FoodChoice%>% 
  mutate(
    Cohort=factor(Cohort),
    Day=factor(Day),
    Treatment=factor(Treatment,
                     levels=c('U','W','L','H'),
                     labels=c('Control','Sham',
                              'Low','High'))) %>% 
  map_at(.at = vars(Ratio:EatenTotal),
         .f = as.numeric)%>% 
  as_tibble()
rawdata_day0<-filter(rawdata,Day=='0')
ksnormal(rawdata_day0$Ratio)
median(rawdata_day0$Ratio)
mean(rawdata_day0$Ratio)

rawdata_day1_C <- filter(rawdata, Day=='1' & Treatment=='Control')
rawdata_day1_S <- filter(rawdata, Day=='1' & Treatment=='Sham')
rawdata_day1_H <- filter(rawdata, Day=='1' & Treatment=='High')
rawdata_day1_L <- filter(rawdata, Day=='1' & Treatment=='Low')

ksnormal(rawdata_day1_C$Ratio)
median(rawdata_day1_C$Ratio)
mean(rawdata_day1_C$Ratio)

ksnormal(rawdata_day1_S$Ratio)
median(rawdata_day1_S$Ratio)
mean(rawdata_day1_S$Ratio)

ksnormal(rawdata_day1_L$Ratio)
median(rawdata_day1_L$Ratio)
mean(rawdata_day1_L$Ratio)

ksnormal(rawdata_day1_H$Ratio)
median(rawdata_day1_H$Ratio)
mean(rawdata_day1_H$Ratio)


ksnormal(rawdata_day0$Pproportion)
median(rawdata_day0$Pproportion)
mean(rawdata_day0$Pproportion)

ksnormal(rawdata_day1_C$Pproportion)
median(rawdata_day1_C$Pproportion)
mean(rawdata_day1_C$Pproportion)

ksnormal(rawdata_day1_S$Pproportion)
median(rawdata_day1_S$Pproportion)
mean(rawdata_day1_S$Pproportion)

ksnormal(rawdata_day1_L$Pproportion)
median(rawdata_day1_L$Pproportion)
mean(rawdata_day1_L$Pproportion)

ksnormal(rawdata_day1_H$Pproportion)
median(rawdata_day1_H$Pproportion)
mean(rawdata_day1_H$Pproportion)


ksnormal(rawdata_day0$Cproportion)
median(rawdata_day0$Cproportion)
mean(rawdata_day0$Cproportion)

ksnormal(rawdata_day1_C$Cproportion)
median(rawdata_day1_C$Cproportion)
mean(rawdata_day1_C$Cproportion)

ksnormal(rawdata_day1_S$Cproportion)
median(rawdata_day1_S$Cproportion)
mean(rawdata_day1_S$Cproportion)

ksnormal(rawdata_day1_L$Cproportion)
median(rawdata_day1_L$Cproportion)
mean(rawdata_day1_L$Cproportion)

ksnormal(rawdata_day1_H$Cproportion)
median(rawdata_day1_H$Cproportion)
mean(rawdata_day1_H$Cproportion)


ksnormal(rawdata_day0$CarbohydrateEaten) 
median(rawdata_day0$CarbohydrateEaten)
mean(rawdata_day0$CarbohydrateEaten)

ksnormal(rawdata_day1_C$CarbohydrateEaten)
median(rawdata_day1_C$CarbohydrateEaten)
mean(rawdata_day1_C$CarbohydrateEaten)

ksnormal(rawdata_day1_S$CarbohydrateEaten)
median(rawdata_day1_S$CarbohydrateEaten)
mean(rawdata_day1_S$CarbohydrateEaten)

ksnormal(rawdata_day1_L$CarbohydrateEaten)
median(rawdata_day1_L$CarbohydrateEaten)
mean(rawdata_day1_L$CarbohydrateEaten)

ksnormal(rawdata_day1_H$CarbohydrateEaten)
median(rawdata_day1_H$CarbohydrateEaten)
mean(rawdata_day1_H$CarbohydrateEaten)


ksnormal(rawdata_day0$ProteinEaten) 
median(rawdata_day0$ProteinEaten)
mean(rawdata_day0$ProteinEaten)

ksnormal(rawdata_day1_C$ProteinEaten)
median(rawdata_day1_C$ProteinEaten)
mean(rawdata_day1_C$ProteinEaten)

ksnormal(rawdata_day1_S$ProteinEaten)
median(rawdata_day1_S$ProteinEaten)
mean(rawdata_day1_S$ProteinEaten)

ksnormal(rawdata_day1_L$ProteinEaten)
median(rawdata_day1_L$ProteinEaten)
mean(rawdata_day1_L$ProteinEaten)

ksnormal(rawdata_day1_H$ProteinEaten)
median(rawdata_day1_H$ProteinEaten)
mean(rawdata_day1_H$ProteinEaten)

 
ksnormal(rawdata_day0$EatenTotal) 
median(rawdata_day0$EatenTotal)
mean(rawdata_day0$EatenTotal)

ksnormal(rawdata_day1_C$EatenTotal)
median(rawdata_day1_C$EatenTotal)
mean(rawdata_day1_C$EatenTotal)

ksnormal(rawdata_day1_S$EatenTotal)
median(rawdata_day1_S$EatenTotal)
mean(rawdata_day1_S$EatenTotal)

ksnormal(rawdata_day1_L$EatenTotal)
median(rawdata_day1_L$EatenTotal)
mean(rawdata_day1_L$EatenTotal)

ksnormal(rawdata_day1_H$EatenTotal)
median(rawdata_day1_H$EatenTotal)
mean(rawdata_day1_H$EatenTotal)




#Wilcox rank sum test on P:C ratios for the first day post-infection:
FoodChoice <- read.delim("C:/") #set to file location
attach(FoodChoice)
day1<-subset(FoodChoice, Day==1)

pairwise.wilcox.test(day1$Ratio,day1$Treatment, p.adj = "bonf")
pairwise.wilcox.test(day1$ProteinEaten,day1$Treatment, p.adj = "bonf")
pairwise.wilcox.test(day1$CarbohydrateEaten,day1$Treatment, p.adj = "bonf")
pairwise.wilcox.test(day1$EatenTotal,day1$Treatment, p.adj = "bonf")

#Calculation of W values of the Wilcox rank sum tests:

high_low<-subset(day1, Treatment == "H" | Treatment =="L")
wilcox.test(high_low$EatenTotal~high_low$Treatment)
wilcox.test(high_low$ProteinEaten~high_low$Treatment)
wilcox.test(high_low$CarbohydrateEaten~high_low$Treatment)
wilcox.test(high_low$Ratio~high_low$Treatment)

high_wounded<-subset(day1, Treatment == "H" | Treatment =="W")
wilcox.test(high_wounded$EatenTotal~high_wounded$Treatment)
wilcox.test(high_wounded$ProteinEaten~high_wounded$Treatment)
wilcox.test(high_wounded$CarbohydrateEaten~high_wounded$Treatment)
wilcox.test(high_wounded$Ratio~high_wounded$Treatment)

high_unmanipulated<-subset(day1, Treatment == "H" | Treatment =="U")
wilcox.test(high_unmanipulated$EatenTotal~high_unmanipulated$Treatment)
wilcox.test(high_unmanipulated$ProteinEaten~high_unmanipulated$Treatment)
wilcox.test(high_unmanipulated$CarbohydrateEaten~high_unmanipulated$Treatment)
wilcox.test(high_unmanipulated$Ratio~high_unmanipulated$Treatment)

low_wounded<-subset(day1, Treatment == "L" | Treatment =="W")
wilcox.test(low_wounded$EatenTotal~low_wounded$Treatment)
wilcox.test(low_wounded$ProteinEaten~low_wounded$Treatment)
wilcox.test(low_wounded$CarbohydrateEaten~low_wounded$Treatment)
wilcox.test(low_wounded$Ratio~low_wounded$Treatment)

low_unmanipulated<-subset(day1, Treatment == "L" | Treatment =="U")
wilcox.test(low_unmanipulated$EatenTotal~low_unmanipulated$Treatment)
wilcox.test(low_unmanipulated$ProteinEaten~low_unmanipulated$Treatment)
wilcox.test(low_unmanipulated$CarbohydrateEaten~low_unmanipulated$Treatment)
wilcox.test(low_unmanipulated$Ratio~low_unmanipulated$Treatment)

wounded_unmanipulated<-subset(day1, Treatment == "W" | Treatment =="U")
wilcox.test(wounded_unmanipulated$EatenTotal~wounded_unmanipulated$Treatment)
wilcox.test(wounded_unmanipulated$ProteinEaten~wounded_unmanipulated$Treatment)
wilcox.test(wounded_unmanipulated$CarbohydrateEaten~wounded_unmanipulated$Treatment)
wilcox.test(wounded_unmanipulated$Ratio~wounded_unmanipulated$Treatment)

detach(FoodChoice)
#GLMMs examine whether a response variable consisting of proportion of P consumed (amount of P eaten divided by the amount of diet available) or proportion of C (amount of C eaten divided by the amount of diet available) was influenced by treatment (high infection; low infection; wounded; and unmanipulated) and day post infection, as well as an interaction between treatment and day.
FoodChoice <- read.delim("C:/") #set to file location


FoodChoice$Cohort<-as.factor(FoodChoice$Cohort)
FoodChoice$Individual<-as.factor(FoodChoice$Individual)
FoodChoice$Day<-as.factor(FoodChoice$Day)
FoodChoice$Treatment<-as.factor(FoodChoice$Treatment)
attach(FoodChoice)

choice.Ppro<-glmmadmb(Pproportion~Treatment+Day+(1|Individual)+(1|Cohort), family = "beta")
ChoiceTukeyPpro<-glht(choice.Ppro, linfct = mcp(Treatment = "Tukey"))
summary(ChoiceTukeyPpro, adjusted(type="bonferroni"))

choice.Cpro<-glmmadmb(Cproportion~Treatment+Day+(1|Individual)+(1|Cohort), family = "beta")
ChoiceTukeyCpro<-glht(choice.Cpro, linfct = mcp(Treatment = "Tukey"))
summary(ChoiceTukeyCpro, adjusted(type="bonferroni"))

detach(FoodChoice)

#Survival analysis on enforced diet
Survival_EnforcedDiet <- read.delim("C:/") #set to file location
attach(Survival_EnforcedDiet)

surv<-coxph(formula=Surv(Time, Status==1)~Treatment, data = Survival_EnforcedDiet)
summary(surv)
detach (Survival_EnforcedDiet)

#Survival analysis on enforced diet followed by infection
Survival_DeathsAdded <- read.delim("C:/") #set to file location
attach(Survival_DeathsAdded)


#Survival model 

surv<-coxph(formula=Surv(Time, Death==1)~Treatment, data = Survival_DeathsAdded)

#Hazard ratio calculations in a for loop so that each treatment is compared to each other one
#To achiev this a "1" is added infront of the treatment which should be compared to all the others 
treatment_list<-c("BC","WC","UC","BE","WE","UE","BP","WP","WU")
for(i in treatment_list){
  new_surv<-Survival_DeathsAdded
  for(y in 1:nrow(new_surv)){
    
    if(new_surv$Treatment[y]==i){
      new_surv$Treatment[y]<-paste0("1",i)
    }
  }
  print(coxph(formula=Surv(Time, Status==1)~Treatment, data = new_surv))
}

#Multiple testing

survMult.glht <- glht(surv,mcp(Treatment="Tukey"))

#Correction for multiple testing

summary(survMult.glht, test = adjusted(type = "bonferroni")) 
summary(survMult.glht, test = adjusted(type = "fdr"))

detach(Survival_DeathsAdded)

#Making a survival plot
Survival_original <- read.delim("C:/") #set to file location
attach(Survival_original)
survival<-survfit(Surv(Time,Death==1)~Treatment, data = Survival_original)

ggsurvplot(survival,censor=TRUE,legend = "bottom",xlab="Time [h]", legend.title = "Treatment:",ylim = c(0, 1.02),xlim = c(0,63),axes.offset = FALSE,ggtheme = theme_bw())

detach(Survival_original)

#Bacterial growth inhibition data analysis using R package growthcruver with default settings
d <- read.delim("path to file") # add patch to file


gc_out <- SummarizeGrowthByPlate(d, plot_fit = TRUE,plot_file="plots.pdf")
#save results in a new txt file and add treatment in a column

#Stats on growthcurver results
Results_growthcurver <- read.delim("path to file") # add patch to file
attach(Results_growthcurver)
ksnormal(Results_growthcurver$auc_e)
tt_fdr<-pairwise_t_test(Results_growthcurver,auc_e~Treatment,pool.sd = FALSE,p.adjust.method = "fdr")
tt_bonferroni<-pairwise_t_test(Results_growthcurver,auc_e~Treatment,pool.sd = FALSE,p.adjust.method = "bonferroni")
detach(Results_growthcurver)

#For inclusion of the supplementary inhibition data
d <- read.delim("path to file") # add patch to file
gc_out <- SummarizeGrowthByPlate(d, plot_fit = TRUE,plot_file="plots_combined.pdf")
#save results in a new txt file and add treatment in a column
growthcruver_results_combined <- read.delim("path to file") # add patch to file
attach(growthcurver_results_combined)
ksnormal(growthcurver_results_combined$auc_e)
#Stats on growthcurver results
anova<-aov(auc_e ~ Treatment * Cohort, data = growthcurver_results_combined)
summary(anova)