library(tidyr) library(dplyr) library(multcomp) getwd() ## Set working directory ## Old working directory #setwd("C:/Users/Public/Documents/My Documents/CD1 PFOA GenX/R CSV files") setwd("C:/Users/blakebe/Documents/CD1 PFOA GenX/R CSV files") ## Read in data file containing animal IDs, treatment groups, etc x<-read.csv("Combined necropsy data E11.5 and E17.5_updated.csv") ## Set working directory ## Old working directory #setwd("C:/Users/Public/Documents/My Documents/CD1 PFOA GenX/HPLC sample lists") setwd("C:/Users/blakebe/Documents/CD1 PFOA GenX/HPLC sample lists") ## Read in data file containing amniotic fluid and maternal serum HPLC values y<-read.csv("UPLC QQQ CD-1 PFOA GenX serum AF gestational exposure study data BEB 05242019.csv") ## Check structure of data str(x) str(y) names(y) test<-spread(y, sample, ng.ml) names(test) af<-na.omit(test[c(1:5)]) ser<-na.omit(test[c(1:4,6)]) names(af)[names(af)=="Amniotic fluid"]<-"ng.ml.af" names(ser)[names(ser)=="Serum"]<-"ng.ml.ser" summary(glht(aov(ng.ml.af~group, data=af),linfct=mcp(group="Tukey"))) ser.11<-subset(ser, timepoint=="E11.5") ser.17<-subset(ser, timepoint=="E17.5") summary(glht(aov(ng.ml.ser~group, data=ser.11),linfct=mcp(group="Tukey"))) summary(glht(aov(ng.ml.ser~group, data=ser.17),linfct=mcp(group="Tukey"))) test.2<-left_join(ser, af, by=c("dam.id","timepoint","group")) test.2$ser.af<-test.2$ng.ml.ser/test.2$ng.ml.af summary(glht(aov(ser.af~group, data=test.2),linfct=mcp(group="Tukey"))) #write.csv(test.2, "Serum and AF dosimetry.csv", row.names = F) names(test.2) summary.stats<-test.2%>% dplyr::group_by(timepoint, group) %>% dplyr::summarise(#nobs.ser = nobs(Serum, na.rm = TRUE), #nobs.af = nobs(AF, na.rm = TRUE), mean.af = mean(ng.ml.af, na.rm = TRUE), sd.af = sd(ng.ml.af, na.rm = TRUE), mean.ser = mean(ng.ml.ser, na.rm = TRUE), sd.ser = sd(ng.ml.ser, na.rm = TRUE), mean.ser.af = mean(ng.ml.ser/ng.ml.af, na.rm = TRUE), sd.ser.af = sd(ng.ml.ser/ng.ml.af, na.rm = TRUE), mean.af.ser = mean(ng.ml.af/ng.ml.ser, na.rm = TRUE), sd.af.ser = sd(ng.ml.af/ng.ml.ser, na.rm = TRUE)) summary.stats y1<-merge(y, x, by=c("dam.id","timepoint")) names(y1)[names(y1)=="group.y"]<-"color.group" names(y1)[names(y1)=="group.x"]<-"group" names(y1) ser<-subset(y1, sample="Serum") af<-subset(y1, sample="Amniotic fluid") names(ser)[names(ser) == "ng.ml"] <- "ng.ml.ser" names(af)[names(af) == "ng.ml"] <- "ng.ml.af" af2 <- af[c(1,6)] ser2 <- ser[c(1,6)] ## Read in data file containing maternal liver and whole embryo HPLC data z<-read.csv("UPLC QQQ CD-1 PFOA GenX Liver Embryo gestational exposure study data BEB 05282019.csv") names(z) summary.stats2<-z%>% dplyr::group_by(group,timepoint,type) %>% dplyr::summarise(mean.ng.g=mean(ng.g), sd.ng.g=sd(ng.g)) summary.stats2 e11.5<-subset(z, timepoint=="E11.5") e17.5<-subset(z, timepoint=="E17.5") e11.5.liv<-subset(e11.5, type=="Dam liver") e11.5.emb<-subset(e11.5, type=="Embryo") names(e11.5.liv)[names(e11.5.liv) == "ng.g"] <- "ng.g.liv" names(e11.5.emb)[names(e11.5.emb) == "ng.g"] <- "ng.g.emb" summary(glht(aov(ng.g.liv~group, data=e11.5.liv),linfct=mcp(group="Tukey"))) summary(glht(aov(ng.g.emb~group, data=e11.5.emb),linfct=mcp(group="Tukey"))) e17.5.liv<-subset(e17.5, type=="Dam liver") e17.5.emb<-subset(e17.5, type=="F Fetus") e17.5.liv<-subset(e17.5, type=="Dam liver") e17.5.embf<-subset(e17.5, type=="F Fetus") e17.5.embm<-subset(e17.5, type=="M Fetus") names(e17.5.liv)[names(e17.5.liv) == "ng.g"] <- "ng.g.liv" names(e17.5.embf)[names(e17.5.embf) == "ng.g"] <- "ng.g.embf" names(e17.5.embm)[names(e17.5.embm) == "ng.g"] <- "ng.g.embm" e17.5.embf2<-subset(e17.5, type=="F Fetus") e17.5.embm2<-subset(e17.5, type=="M Fetus") e17.5emb<-rbind(e17.5.embf2,e17.5.embm2) summary(glht(aov(ng.g.liv~group, data=e17.5.liv),linfct=mcp(group="Tukey"))) summary(glht(aov(ng.g~group, data=e17.5emb),linfct=mcp(group="Tukey"))) summary(glht(aov(ng.g.embm~group, data=e17.5.embm),linfct=mcp(group="Tukey"))) summary(glht(aov(ng.g.embf~group, data=e17.5.embf),linfct=mcp(group="Tukey"))) ##################################################################### names(e11.5.liv)[names(e11.5.liv) == "ng.g"] <- "ng.g.liv" names(e11.5.emb)[names(e11.5.emb) == "ng.g"] <- "ng.g.emb" liver2 <- e11.5.liv[c(1,3,15)] embryo2 <- e11.5.emb[c(1,3,15)] e11.5_spread<-merge(liver2, embryo2, by=c("group","dam.id")) e11.5_spread$emb.liv.rat<-e11.5_spread$ng.g.emb/e11.5_spread$ng.g.liv e11.5_spread$liv.emb.rat<-e11.5_spread$ng.g.liv/e11.5_spread$ng.g.emb summary(glht(aov(liv.emb.rat~group, data=e11.5_spread),linfct=mcp(group="Tukey"))) #write.csv(e11.5_spread, "Embryo Liver internal dosimetry E11.5.csv", row.names=F) summary.stats3<-e11.5_spread%>% dplyr::group_by(group) %>% dplyr::summarise(mean.liv.emb.ratio=mean(liv.emb.rat), sd.liv.emb.ratio=sd(liv.emb.rat), mean.emb.liv.ratio=mean(emb.liv.rat), sd.emb.liv.ratio=sd(emb.liv.rat)) summary.stats3 e17.5.liv<-subset(e17.5, type=="Dam liver") e17.5.embf<-subset(e17.5, type=="F Fetus") e17.5.embm<-subset(e17.5, type=="M Fetus") names(e17.5.liv)[names(e17.5.liv) == "ng.g"] <- "ng.g.liv" names(e17.5.embf)[names(e17.5.embf) == "ng.g"] <- "ng.g.embf" names(e17.5.embm)[names(e17.5.embm) == "ng.g"] <- "ng.g.embm" liver2 <- e17.5.liv[c(1,3,15)] embryoF2 <- e17.5.embf[c(1,3,15)] embryoM2 <- e17.5.embm[c(1,3,15)] e17.5_embryo<-merge(embryoF2, embryoM2, by=c("group","dam.id"), all.x = T) e17.5_spread<-merge(liver2, e17.5_embryo, by=c("group","dam.id")) #write.csv(e17.5_spread, "Embryo Liver internal dosimetry E17.5.csv", row.names=F) e17.5_spread$liv.embf.rat<-e17.5_spread$ng.g.embf/e17.5_spread$ng.g.liv e17.5_spread$embf.liv.rat<-e17.5_spread$ng.g.liv/e17.5_spread$ng.g.embf e17.5_spread$liv.embm.rat<-e17.5_spread$ng.g.embm/e17.5_spread$ng.g.liv e17.5_spread$embm.liv.rat<-e17.5_spread$ng.g.liv/e17.5_spread$ng.g.embm summary.stats5<-e17.5_spread%>% dplyr::group_by(group) %>% dplyr::summarise(mean.liv.embf.ratio=mean(liv.embf.rat, na.rm = TRUE), sd.liv.embf.ratio=sd(liv.embf.rat, na.rm = TRUE), mean.embf.liv.ratio=mean(embf.liv.rat, na.rm = TRUE), sd.embf.liv.ratio=sd(embf.liv.rat, na.rm = TRUE), mean.liv.embm.ratio=mean(liv.embm.rat, na.rm = TRUE), sd.liv.embm.ratio=sd(liv.embm.rat, na.rm = TRUE), mean.embm.liv.ratio=mean(embm.liv.rat, na.rm = TRUE), sd.embm.liv.ratio=sd(embm.liv.rat, na.rm = TRUE)) summary.stats5 e17.5<-subset(z, timepoint=="E17.5") e17.5.test<-e17.5%>% dplyr::filter(!type=="Dam liver")%>% dplyr::group_by(group, dam.id) %>% dplyr::summarise(ng.g.emb.mean=mean(ng.g)) e17.5.test e17.5.liv<-subset(e17.5, type=="Dam liver") names(e17.5.liv)[names(e17.5.liv) == "ng.g"] <- "ng.g.liv" e17.5_liv.emb<-merge(e17.5.liv, e17.5.test, by=c("group","dam.id")) e17.5_liv.emb$liv.emb.rat<-e17.5_liv.emb$ng.g.liv/e17.5_liv.emb$ng.g.emb.mean e17.5_liv.emb$emb.liv.rat<-e17.5_liv.emb$ng.g.emb.mean/e17.5_liv.emb$ng.g.liv summary(glht(aov(liv.emb.rat~group, data=e17.5_liv.emb),linfct=mcp(group="Tukey"))) summary.stats6<-e17.5_liv.emb%>% dplyr::group_by(group) %>% dplyr::summarise(mean.liv.emb.ratio=mean(liv.emb.rat, na.rm = TRUE), sd.liv.emb.ratio=sd(liv.emb.rat, na.rm = TRUE), mean.emb.liv.ratio=mean(emb.liv.rat, na.rm = TRUE), sd.emb.liv.ratio=sd(emb.liv.rat, na.rm = TRUE), mean.emb.ng.g=mean(ng.g.emb.mean, na.rm = TRUE), sd.emb.ng.g=sd(ng.g.emb.mean, na.rm = TRUE)) summary.stats6