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Appearance_Functions.R
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Appearance_Functions.R
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#Appearance Main
#Input: Data
#Ouput: A dataframe in which for each finding and RO and rodents/non-rodents: It will have 0 if the finding is not existing in the "specific"
#duration study and 1 if it exists.
#Read the data
MainData_file="Data/S3.txt"
PathologyData_file="Data/S4.txt"
Data=ReadingData(MainData_file,PathologyData_file)
rm(MainData_file,PathologyData_file)
# Change the Data
Data=ChangeData(Data)
Appear_Species=function(animal, Dataf_find, MainData_drug,findingD) {
# Check whether you have studies for the specific animal and study
Existence_short=ifelse(any(MainData_drug$species==animal) & any(MainData_drug$group=="short"),TRUE,NA)
Existence_middle=ifelse(any(MainData_drug$species==animal) & any(MainData_drug$group=="middle"),TRUE,NA)
Existence_long=ifelse(any(MainData_drug$species==animal) & any(MainData_drug$group=="long"),TRUE,NA)
# Dataset for the specific drug, finding and species
Dataf_find=Dataf_find %>% filter(species==animal)
#Specify whether the finding exists for the specific species and the specific drug in short, middle and long duration (if they have been conducted)
row=data.frame(drug=unique(MainData_drug$identifier),
modality=unique(MainData_drug$type),
species=animal,
finding=findingD,
short=any(Dataf_find$group=="short")*Existence_short,
middle=any(Dataf_find$group=="middle")*Existence_middle,
long=any(Dataf_find$group=="long")*Existence_long, stringsAsFactors = FALSE)
return(row)
}
Appear_Find=function(findingD, Dataf_drug, MainData_drug) {
# Iterate through the different species
App_Find=map_dfr(.x =c("rodent","non-rodent"),.f = Appear_Species, Dataf_drug %>% filter(description==findingD), MainData_drug,findingD)
return(App_Find)
}
Appear_Drug=function(Specdrug,Dataf) {
App_Drug=data.frame()
for (key in names(Dataf)[-1]) {
#Iterate for each finding
findings=unique(Dataf[[key]]$description)
App_Drug=rbind(App_Drug,map_dfr(.x =findings,.f = Appear_Find, Dataf[[key]] %>% filter(identifier==Specdrug),
Dataf$MainData %>% filter(identifier==Specdrug)))
}
return(App_Drug)
}
Cond.Appearance.fn=function(Appearance) {
#Define Systems
Systems=c("Weight changes", "Neurological clinical signs", "Gastrointestinal clinical signs", "Other clinical signs",
"In life cardiovascular effects","Whole body",
"liver", "Lymphoid Tissues", "Endocrine System", "Reproductive System",
"Nervous System", "Urinary System", "Respiratory System","Exocrine System",
"Cutaneous", "MuscularSkeletal System", "GI tract", "Eye/conjuctiva", "Cardiovascular System", "whole body" )
#Search the category for a specific finding
search.Categ= function(finding, Systems) {
return(Systems[which(sapply(Systems,grepl,finding))])
}
Appearance =Appearance %>% mutate(Category=sapply(finding,search.Categ,Systems))
Appearance$finding=Appearance$Category
Appearance=Appearance %>% select(-Category)
#Transform it to TRUE/FALSE
Cond.Appearance= Appearance %>% group_by(drug,modality,species,finding) %>%
summarise(short.cond=ifelse(all(is.na(short)) , NA ,any(short==1,na.rm = TRUE)),
middle.cond=ifelse(all(is.na(middle)) , NA ,any(middle==1,na.rm = TRUE)),
long.cond=ifelse(all(is.na(long)) , NA ,any(long==1,na.rm = TRUE))) %>% ungroup()
#Create the short_middle.cond_Column
Cond.Appearance=Cond.Appearance %>% group_by(drug,modality,species,finding) %>%
mutate(short_middle.cond= ifelse((is.na(short.cond) & is.na(middle.cond)), NA , any(short.cond==TRUE | middle.cond==TRUE,na.rm = TRUE))) %>% ungroup()
return(Cond.Appearance)
}
Cond.Appearance.ext.fn=function(Appearance) {
#Transform it to TRUE/FALSE
Cond.Appearance= Appearance %>% group_by(drug,modality,species,finding) %>%
summarise(short.cond=ifelse(all(is.na(short)) , NA ,any(short==1,na.rm = TRUE)),
middle.cond=ifelse(all(is.na(middle)) , NA ,any(middle==1,na.rm = TRUE)),
long.cond=ifelse(all(is.na(long)) , NA ,any(long==1,na.rm = TRUE))) %>% ungroup()
#Create the short_middle.cond_Column
Cond.Appearance=Cond.Appearance %>% group_by(drug,modality,species,finding) %>%
mutate(short_middle.cond= ifelse((is.na(short.cond) & is.na(middle.cond)), NA , any(short.cond==TRUE | middle.cond==TRUE,na.rm = TRUE))) %>% ungroup()
return(Cond.Appearance)
}
#Distribution of FP, FN
Appear_plot=function(dataf,species,legend_pos) {
#Number of molecules
No_molecules=dataf$TP[1]+dataf$TN[1]+dataf$FN[1]+dataf$FP[1]
#Keep only FP and FN and create the frequencies and transform the dataset in long format
dataf=dataf %>% select(c(finding, FN, FP)) %>%
mutate(FP= round((FP/No_molecules*100),2), FN=round((FN/No_molecules*100),2)) %>%
pivot_longer(!finding, names_to = "type", values_to = "value")
#Create the Categories
in_life=c("Weight changes", "Neurological clinical signs", "Gastrointestinal clinical signs", "Other clinical signs",
"In life cardiovascular effects")
dataf= dataf %>% mutate(Category=ifelse(finding %in% in_life,"in life observations", "necropsy observations"))
# For better plotting
dataf$finding[dataf$finding=="Gastrointestinal clinical signs"]="Gastrointestinal \nclinical signs"
dataf$finding[dataf$finding=="In life cardiovascular effects"]="In life cardiovascular\n effects"
#Create the plot
Nplot=ggplot(dataf, aes(x= finding, y=value, fill=factor(type))) +
geom_bar(stat = "identity", width = 0.7,position = position_dodge(width = 0.75),colour="black") +
#geom_text(aes(label = value), vjust = -0.3, hjust=0.5, position=position_dodge(0.75),size=2.5)+
facet_grid(. ~Category,switch = "x", scales = "free", space = "free")+
scale_fill_manual("Contingency table \nvalues",
values = c("FP" = "skyblue3", "FN" = "tomato3"))
Nplot= Nplot+
theme_bw()+
labs(y="Frequency (%)",x= "", title=paste("Frequency of FP and FN across the different Categories in",species)) +
scale_y_continuous(limits = c(0,100),breaks = c(0,10,20,30,40,50,60,70,80,90,100))+
theme(#plot.background = element_rect(fill= NA, colour = "black", size = 1),
# plot.margin = margin(10,10,10,10),
plot.title = element_text(size=14, face="bold", hjust =0.5,vjust=0.4 ),
plot.subtitle = element_text( hjust =0.5,vjust=0.5),
panel.grid.major = element_line(colour = "grey50", size = 0.2, linetype = "dotted"),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.spacing = unit(2, "lines"),
strip.background = element_blank(),
strip.placement = "outside",
strip.text.x = element_text(size=12,color="black",face = "bold.italic"),
axis.ticks.y = element_line(colour = "black", size = 0.2),
axis.ticks.x = element_line(colour = "black", size = 0.2),
axis.text.x = element_text(size=10, colour="black", angle = 55, hjust=1),
axis.text.y = element_text(size=11, colour="black"),
# axis.title.y = element_text(size = 11,colour = "black", margin=margin(r=10)),
# axis.title.x = element_text(size = 11,colour = "black",face="bold", margin=margin(t=10)),
legend.background = element_blank(),
legend.position = legend_pos,
legend.justification = c("left", "top"),
legend.title = element_text(colour = "black", size = 11, hjust = 0.5),
legend.text = element_text(colour = "black", size = 10, hjust = 0.5))
return(Nplot)
}
Adverse_finding=function(adverse_dataf,dataf, type) {
# Filter the studies for a specific modality and that have long and short/middle study
dataf=dataf %>% filter(modality==type)
dataf= dataf %>% filter(!is.na(long.cond) & !is.na(short_middle.cond))
adverse_dataf=adverse_dataf %>% filter(modality==type)
adverse_dataf= adverse_dataf %>% filter(!is.na(long.cond) & !is.na(short_middle.cond))
# Some findings are not included in the adverse_dataf because they have never observed as adverse. You want to exclude those
# from the dataf before the comparison
findings_vector=unique(dataf$finding)
Not_keep= findings_vector[which(!findings_vector %in% adverse_dataf$finding)]
dataf=dataf %>% filter(!finding %in% Not_keep)
#Create the two columns
find_dataf=dataf[,c(1:4)]
# Was existing as adverse in the short or middle study too
ExistingAdverse=adverse_dataf$long.cond==TRUE & adverse_dataf$short_middle.cond==TRUE
# Was existing in the short or middle study too
ExistingNot_Adverse=adverse_dataf$long.cond==TRUE & dataf$short_middle.cond==TRUE & adverse_dataf$short_middle.cond==FALSE
# You see it in the long study firt time
NotExisting=adverse_dataf$long.cond==TRUE & dataf$short_middle.cond==FALSE & adverse_dataf$short_middle.cond==FALSE
# add them to the dataset
find_dataf= find_dataf %>% mutate(ExistingAdverse,ExistingNot_Adverse,NotExisting)
# Group for each finding
find_dataf=find_dataf %>% group_by(finding) %>% summarise(ExistingAdverse=sum(ExistingAdverse),
ExistingNot_Adverse=sum(ExistingNot_Adverse),
NotExisting=sum(NotExisting))
return(find_dataf)
}
Adversity.Summary.fn=function(adverse_dataf, type) {
#Filter only the specific modality
adverse_dataf=adverse_dataf %>% filter(modality==type)
#Answer the question: Did you have any adversity seen in the long or short/middle duration for a drug?
adverse_dataf= adverse_dataf %>% group_by(drug) %>%
summarise(short_middle=ifelse(all(is.na(short_middle.cond)) , NA ,any(short_middle.cond,na.rm = TRUE)),
long=ifelse(all(is.na(long.cond)) , NA ,any(long.cond,na.rm = TRUE))) %>% filter(!is.na(long) & !is.na(short_middle))
# Order it
adverse_dataf = adverse_dataf %>% arrange(short_middle,long)
return(adverse_dataf)
}