Exometabolite_analysisNonPolarPos

#####This is analysis is determine the number of released exometabolites in each isolate#####
#####and, determine the identity of a subset of these exometabolites#####
#####This is for Polar analysis in positive ionization mode#####

library(dplyr)

#Read in MZmine final feature table for MS analysis
#####################MAKE SURE YOU CHANGE THIS ON GITHUB FOR FINAL CODE SUBMISSION#####################
filt <- read.csv("/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/initialFiles/MZmineNonPolarPoswExConForQC.csv",check.names=F)

#Remove outlier-1mem_Bt_45hr-4
filt_f <- filt[,-c(115)]

#Create group/sample identifiers
#library(reshape)
#filtMelt <- melt(filt_f,id.vars=c("ID","RT","MZ"))

#Split column name string so we get 1mem, 2mem, 3mem and ExCon identifiers
#msD <- data.frame(do.call('rbind', strsplit(as.character(filtMelt$variable),'_',fixed=TRUE)))
#Place this into melted object
#filtMelt$group <- msD$X1

#Melt dataframe
library(reshape2)
#Melt dataframe
filtM <- melt(filt_f,id.vars=c("ID","RT","MZ"))

#Change NA values and 0s to low value
min(filtM[filtM$value>0,5],na.rm=TRUE)
filtM[,5][filtM[,5]==0] <-1/1000*min(filtM[filtM$value>0,5],na.rm=TRUE)

###################################Filter 1###################################

#Retain features where the maximum value is found in a consortia
library(dplyr)

#Create groups for averaging
msD <- data.frame(do.call('rbind', strsplit(as.character(filtM$variable),'-',fixed=TRUE)))
msD_2 <- data.frame(do.call('rbind', strsplit(as.character(msD$X1),'_',fixed=TRUE)))

#Place this into melted object
filtM$group <- msD_2$X2

#Obtain the group with the maximum peak area value
FeatureMax <- filtM %>%
             group_by(ID) %>%
             filter(value == max(value,na.rm=T))

#Obtain feature IDs where the max value was found in 3mem, BtCv, BtPs, CvPs

MaxIn3mem <- FeatureMax[FeatureMax$group=="Full",]
MaxInBtCv <- FeatureMax[FeatureMax$group=="BtCv",]
MaxInBtPs <- FeatureMax[FeatureMax$group=="BtPs",]
MaxInCvPs <- FeatureMax[FeatureMax$group=="CvPs",]

#Sort in ascending order by ID
MaxIn3mem <- MaxIn3mem[order(MaxIn3mem$ID),]
MaxInBtCv <- MaxInBtCv[order(MaxInBtCv$ID),]
MaxInBtPs <- MaxInBtPs[order(MaxInBtPs$ID),]
MaxInCvPs <- MaxInCvPs[order(MaxInCvPs$ID),]

#Add groupTime column
gtFull <- data.frame(do.call('rbind', strsplit(as.character(MaxIn3mem$variable),'-',fixed=TRUE)))
gtBtCv <- data.frame(do.call('rbind', strsplit(as.character(MaxInBtCv$variable),'-',fixed=TRUE)))
gtBtPs <- data.frame(do.call('rbind', strsplit(as.character(MaxInBtPs$variable),'-',fixed=TRUE)))
gtCvPs <- data.frame(do.call('rbind', strsplit(as.character(MaxInCvPs$variable),'-',fixed=TRUE)))

#add column to original
MaxIn3mem$groupTime <- gtFull$X1
MaxInBtCv$groupTime <- gtBtCv$X1
MaxInBtPs$groupTime <- gtBtPs$X1
MaxInCvPs$groupTime <- gtCvPs$X1

#Keep these features
filtS1_3mem <- filt_f[c(which(filt_f$ID %in% MaxIn3mem$ID)),]
filtS1_BtCv <- filt_f[c(which(filt_f$ID %in% MaxInBtCv$ID)),]
filtS1_BtPs <- filt_f[c(which(filt_f$ID %in% MaxInBtPs$ID)),]
filtS1_CvPs <- filt_f[c(which(filt_f$ID %in% MaxInCvPs$ID)),]

###################################Filter 2###################################

#Noise filter: The min value in a condition should be 3x the max value found in the ExCon (Filter suggested by JGI)

library(reshape)
filt3mem <- melt(filtS1_3mem,id.vars=c("ID","RT","MZ"))
filtBtCv <- melt(filtS1_BtCv,id.vars=c("ID","RT","MZ"))
filtBtPs <- melt(filtS1_BtPs,id.vars=c("ID","RT","MZ"))
filtCvPs <- melt(filtS1_CvPs,id.vars=c("ID","RT","MZ"))

#Change NA values and 0s to low value
min(filt3mem[filt3mem$value>0,5],na.rm=TRUE)
min(filtBtCv[filtBtCv$value>0,5],na.rm=TRUE)
min(filtBtPs[filtBtPs$value>0,5],na.rm=TRUE)
min(filtCvPs[filtCvPs$value>0,5],na.rm=TRUE)

#filtM[,5][is.zero(filtM[,5])] <-1/1000*min(filtM[filtM$value>0,5],na.rm=TRUE)
filt3mem[,5][filt3mem[,5]==0] <-1/1000*min(filt3mem[filt3mem$value>0,5],na.rm=TRUE)
filtBtCv[,5][filtBtCv[,5]==0] <-1/1000*min(filtBtCv[filtBtCv$value>0,5],na.rm=TRUE)
filtBtPs[,5][filtBtPs[,5]==0] <-1/1000*min(filtBtPs[filtBtPs$value>0,5],na.rm=TRUE)
filtCvPs[,5][filtCvPs[,5]==0] <-1/1000*min(filtCvPs[filtCvPs$value>0,5],na.rm=TRUE)

#Create groups
msFull <- data.frame(do.call('rbind', strsplit(as.character(filt3mem$variable),'-',fixed=TRUE)))
msFull_2 <- data.frame(do.call('rbind', strsplit(as.character(msFull$X1),'_',fixed=TRUE)))

msBtCv <- data.frame(do.call('rbind', strsplit(as.character(filtBtCv$variable),'-',fixed=TRUE)))
msBtCv_2 <- data.frame(do.call('rbind', strsplit(as.character(msBtCv$X1),'_',fixed=TRUE)))

msBtPs <- data.frame(do.call('rbind', strsplit(as.character(filtBtPs$variable),'-',fixed=TRUE)))
msBtPs_2 <- data.frame(do.call('rbind', strsplit(as.character(msBtPs$X1),'_',fixed=TRUE)))

msCvPs <- data.frame(do.call('rbind', strsplit(as.character(filtCvPs$variable),'-',fixed=TRUE)))
msCvPs_2 <- data.frame(do.call('rbind', strsplit(as.character(msCvPs$X1),'_',fixed=TRUE)))

#Place this into melted object
filt3mem$MaxMatch <- msFull$X1
filt3mem$group <- msFull_2$X2

filtBtCv$MaxMatch <- msBtCv$X1
filtBtCv$group <- msBtCv_2$X2

filtBtPs$MaxMatch <- msBtPs$X1
filtBtPs$group <- msBtPs_2$X2

filtCvPs$MaxMatch <- msCvPs$X1
filtCvPs$group <- msCvPs_2$X2

#Filter out consortia conditions and ExCon
#Detach plyr if not working
detach("package:plyr", unload=TRUE)
require(dplyr)
Fullsamps <- c("Full", "ExControl")
BtCvsamps <- c("BtCv", "ExControl")
BtPssamps <- c("BtPs", "ExControl")
CvPssamps <- c("CvPs", "ExControl")

filtFull_ExCon <- filter(filt3mem, group %in% Fullsamps)
filtBtCv_ExCon <- filter(filtBtCv, group %in% BtCvsamps)
filtBtPs_ExCon <- filter(filtBtPs, group %in% BtPssamps)
filtCvPs_ExCon <- filter(filtCvPs, group %in% CvPssamps)

#Add condition and time where the max value was found
filtFull_ExCon$groupTime <- rep(MaxIn3mem$groupTime,23)
filtBtCv_ExCon$groupTime <- rep(MaxInBtCv$groupTime,25)
filtBtPs_ExCon$groupTime <- rep(MaxInBtPs$groupTime,24)
filtCvPs_ExCon$groupTime <- rep(MaxInCvPs$groupTime,24)

#Obtain max values for each ID for
require(dplyr)
filtFullConMax <- filtFull_ExCon %>% group_by(ID,group) %>% summarise(value = max(value))
filtBtCvConMax <- filtBtCv_ExCon %>% group_by(ID,group) %>% summarise(value = max(value))
filtBtPsConMax <- filtBtPs_ExCon %>% group_by(ID,group) %>% summarise(value = max(value))
filtCvPsConMax <- filtCvPs_ExCon %>% group_by(ID,group) %>% summarise(value = max(value))

#Match the number of factor levels
filtFull_ExCon$MaxMatch <- factor(filtFull_ExCon$MaxMatch, levels=levels(filtFull_ExCon$groupTime))
filtBtCv_ExCon$MaxMatch <- factor(filtBtCv_ExCon$MaxMatch, levels=levels(filtBtCv_ExCon$groupTime))
filtBtPs_ExCon$MaxMatch <- factor(filtBtPs_ExCon$MaxMatch, levels=levels(filtBtPs_ExCon$groupTime))
filtCvPs_ExCon$MaxMatch <- factor(filtCvPs_ExCon$MaxMatch, levels=levels(filtCvPs_ExCon$groupTime))

#From each ID, extract all replicates from the condition (group + time) that the max value was found
filtFull_ExConMaxReps <- filtFull_ExCon[filtFull_ExCon$MaxMatch==filtFull_ExCon$groupTime,]
filtBtCv_ExConMaxReps <- filtBtCv_ExCon[filtBtCv_ExCon$MaxMatch==filtBtCv_ExCon$groupTime,]
filtBtPs_ExConMaxReps <- filtBtPs_ExCon[filtBtPs_ExCon$MaxMatch==filtBtPs_ExCon$groupTime,]
filtCvPs_ExConMaxReps <- filtCvPs_ExCon[filtCvPs_ExCon$MaxMatch==filtCvPs_ExCon$groupTime,]


#Obtain min values for each ID for
#Note, this minimum value will be the smallest value obtained within the replicates where the max value was found
#Meaning, filter step 1 obtained condition and times where the max value was found (e.g. Full_45hr). This group contained ~4 replicates
#Within the replicates where the max value was found, which in the smallest value from this group?
#This is the data we want. Otherwise, if we just obtain the smallest value across the whole condition (e.g. Full)
#We're excluding the possibility that this exometabolite could appear at later time timepoints

filtFullConMin <- filtFull_ExConMaxReps %>% group_by(ID,groupTime) %>% summarise(value = min(value))
filtBtCvConMin <- filtBtCv_ExConMaxReps %>% group_by(ID,groupTime) %>% summarise(value = min(value))
filtBtPsConMin <- filtBtPs_ExConMaxReps %>% group_by(ID,groupTime) %>% summarise(value = min(value))
filtCvPsConMin <- filtCvPs_ExConMaxReps %>% group_by(ID,groupTime) %>% summarise(value = min(value))

#Obtain and combine min from conditions and max from excontrol
filtFullConMaxMin <- rbind(filtFullConMin,filtFullConMax[filtFullConMax$group=="ExControl",])
filtBtCvConMaxMin <- rbind(filtBtCvConMin,filtBtCvConMax[filtBtCvConMax$group=="ExControl",])
filtBtPsConMaxMin <- rbind(filtBtPsConMin,filtBtPsConMax[filtBtPsConMax$group=="ExControl",])
filtCvPsConMaxMin <- rbind(filtCvPsConMin,filtCvPsConMax[filtCvPsConMax$group=="ExControl",])

#Obtain fold change of max values between 45 TP and ExCon
FullFC <- filtFullConMaxMin %>%
             group_by(ID) %>%
             mutate(
             FC = lag(value)/value
             )
table(FullFC$FC>3)

BtCvFC <- filtBtCvConMaxMin %>%
             group_by(ID) %>%
             mutate(
             FC = lag(value)/value
             )
table(BtCvFC$FC>3)

BtPsFC <- filtBtPsConMaxMin %>%
             group_by(ID) %>%
             mutate(
             FC = lag(value)/value
             )
table(BtPsFC$FC>3)

CvPsFC <- filtCvPsConMaxMin %>%
             group_by(ID) %>%
             mutate(
             FC = lag(value)/value
             )
table(CvPsFC$FC>3)

#Doublecheck that the FC calculations were accurate
FullFC[order(FullFC$ID),]
BtCvFC[order(BtCvFC$ID),]
BtPsFC[order(BtPsFC$ID),]
CvPsFC[order(CvPsFC$ID),]

FullMinMaxIDs <- unique(FullFC$ID[FullFC$FC>3]) #3135
BtCvMinMaxIDs <- unique(BtCvFC$ID[BtCvFC$FC>3]) #1949
BtPsMinMaxIDs <- unique(BtPsFC$ID[BtPsFC$FC>3]) #893
CvPsMinMaxIDs <- unique(CvPsFC$ID[CvPsFC$FC>3]) #1468

FullMinMaxFeatures <- filt_f[which(filt_f$ID %in% FullMinMaxIDs),]
BtCvMinMaxFeatures <- filt_f[which(filt_f$ID %in% BtCvMinMaxIDs),]
BtPsMinMaxFeatures <- filt_f[which(filt_f$ID %in% BtPsMinMaxIDs),]
CvPsMinMaxFeatures <- filt_f[which(filt_f$ID %in% CvPsMinMaxIDs),]

###################################Filter 3###################################

#RSD filter below 20%- All time points needs to be below 20%

filtMeltFull <- melt(FullMinMaxFeatures,id.vars=c("ID","RT","MZ"))
filtMeltBtCv <- melt(BtCvMinMaxFeatures,id.vars=c("ID","RT","MZ"))
filtMeltBtPs <- melt(BtPsMinMaxFeatures,id.vars=c("ID","RT","MZ"))
filtMeltCvPs <- melt(CvPsMinMaxFeatures,id.vars=c("ID","RT","MZ"))

#Change NA values and 0s to low value
filtMeltFull[,5][filtMeltFull[,5]<2] <- 2
filtMeltBtCv[,5][filtMeltBtCv[,5]<2] <- 2
filtMeltBtPs[,5][filtMeltBtPs[,5]<2] <- 2
filtMeltCvPs[,5][filtMeltCvPs[,5]<2] <- 2

#Perform RSD filter
rsdFull <- filtMeltFull
rsdBtCv <- filtMeltBtCv
rsdBtPs <- filtMeltBtPs
rsdCvPs <- filtMeltCvPs

#log transform values
rsdFull$value <- log2(rsdFull$value)
rsdBtCv$value <- log2(rsdBtCv$value)
rsdBtPs$value <- log2(rsdBtPs$value)
rsdCvPs$value <- log2(rsdCvPs$value)

#Create tibble
msDFull <- data.frame(do.call('rbind', strsplit(as.character(rsdFull$variable),'-',fixed=TRUE)))
msDBtCv <- data.frame(do.call('rbind', strsplit(as.character(rsdBtCv$variable),'-',fixed=TRUE)))
msDBtPs <- data.frame(do.call('rbind', strsplit(as.character(rsdBtPs$variable),'-',fixed=TRUE)))
msDCvPs <- data.frame(do.call('rbind', strsplit(as.character(rsdCvPs$variable),'-',fixed=TRUE)))

#Place this into melted object
rsdFull$groupTime <- msDFull$X1
rsdBtCv$groupTime <- msDBtCv$X1
rsdBtPs$groupTime <- msDBtPs$X1
rsdCvPs$groupTime <- msDCvPs$X1

#Add conditions to tibble
condFull <- data.frame(do.call('rbind', strsplit(as.character(rsdFull$variable),'_',fixed=TRUE)))
condBtCv <- data.frame(do.call('rbind', strsplit(as.character(rsdBtCv$variable),'_',fixed=TRUE)))
condBtPs <- data.frame(do.call('rbind', strsplit(as.character(rsdBtPs$variable),'_',fixed=TRUE)))
condCvPs <- data.frame(do.call('rbind', strsplit(as.character(rsdCvPs$variable),'_',fixed=TRUE)))

rsdFull$cond <- condFull$X2
rsdBtCv$cond <- condBtCv$X2
rsdBtPs$cond <- condBtPs$X2
rsdCvPs$cond <- condCvPs$X2

#Keep only conditions of interest
rsdFullOnly <- rsdFull[rsdFull$cond=="Full",]
rsdBtCvOnly <- rsdBtCv[rsdBtCv$cond=="BtCv",]
rsdBtPsOnly <- rsdBtPs[rsdBtPs$cond=="BtPs",]
rsdCvPsOnly <- rsdCvPs[rsdCvPs$cond=="CvPs",]

library(dplyr)
detach("package:plyr", unload=TRUE)
groupedRSDFull <- rsdFullOnly %>% group_by(ID,RT,MZ,groupTime)
groupedRSDBtCv <- rsdBtCvOnly %>% group_by(ID,RT,MZ,groupTime)
groupedRSDBtPs <- rsdBtPsOnly %>% group_by(ID,RT,MZ,groupTime)
groupedRSDCvPs <- rsdCvPsOnly %>% group_by(ID,RT,MZ,groupTime)

#Get mean and std
groupRSDSumFull <- summarise(groupedRSDFull, mean=mean(value), sd=sd(value))
groupRSDSumBtCv <- summarise(groupedRSDBtCv, mean=mean(value), sd=sd(value))
groupRSDSumBtPs <- summarise(groupedRSDBtPs, mean=mean(value), sd=sd(value))
groupRSDSumCvPs <- summarise(groupedRSDCvPs, mean=mean(value), sd=sd(value))

#Calculate RSD
groupRSDSumFull$RSD <-groupRSDSumFull$sd/groupRSDSumFull$mean
groupRSDSumBtCv$RSD <-groupRSDSumBtCv$sd/groupRSDSumBtCv$mean
groupRSDSumBtPs$RSD <-groupRSDSumBtPs$sd/groupRSDSumBtPs$mean
groupRSDSumCvPs$RSD <-groupRSDSumCvPs$sd/groupRSDSumCvPs$mean

#Determine which IDs have a RSD of 0.
groupRSDSumFull0s <- as.data.frame(groupRSDSumFull[groupRSDSumFull$RSD==0,])
groupRSDSumBtCv0s <- as.data.frame(groupRSDSumBtCv[groupRSDSumBtCv$RSD==0,])
groupRSDSumBtPs0s <- as.data.frame(groupRSDSumBtPs[groupRSDSumBtPs$RSD==0,])
groupRSDSumCvPs0s <- as.data.frame(groupRSDSumCvPs[groupRSDSumCvPs$RSD==0,])

#Most of these occur at the initial time point. This is okay. Just means that the exometabolite wasn't detected at the initial time point

#The plan: get rid of highly variable features. Features across all time point should have a RSD <20%. Standard in LCMS is 20%
#Find max RSD value
groupRSDSumFullMAX <- groupRSDSumFull %>%
             group_by(ID) %>%
             filter(RSD == max(RSD,na.rm=T))

groupRSDSumBtCvMAX <- groupRSDSumBtCv %>%
             group_by(ID) %>%
             filter(RSD == max(RSD,na.rm=T))

groupRSDSumBtPsMAX <- groupRSDSumBtPs %>%
             group_by(ID) %>%
             filter(RSD == max(RSD,na.rm=T))

groupRSDSumCvPsMAX <- groupRSDSumCvPs %>%
             group_by(ID) %>%
             filter(RSD == max(RSD,na.rm=T))


#Remove features whose minimum RSD value is >0.2. High variable features
FullFinalIDs <- groupRSDSumFullMAX$ID[groupRSDSumFullMAX$RSD<0.2] #2123
BtCvFinalIDs <- groupRSDSumBtCvMAX$ID[groupRSDSumBtCvMAX$RSD<0.2] #1514
BtPsFinalIDs <- groupRSDSumBtPsMAX$ID[groupRSDSumBtPsMAX$RSD<0.2] #533
CvPsFinalIDs <- groupRSDSumCvPsMAX$ID[groupRSDSumCvPsMAX$RSD<0.2] #581


#Save IDs for each isolate that have been determined to be released
saveRDS(FullFinalIDs, "/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/outputFiles/FullFinalIDsNonPolarPos.rds")
saveRDS(BtCvFinalIDs, "/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/outputFiles/BtCvFinalIDsNonPolarPos.rds")
saveRDS(BtPsFinalIDs, "/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/outputFiles/BtPsFinalIDsNonPolarPos.rds")
saveRDS(CvPsFinalIDs, "/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/outputFiles/CvPsFinalIDsNonPolarPos.rds")

##########End of filtering steps to determine released exometabolites##########

#Create CSV for these exometabolites

AllIDs <- c(FullFinalIDs,BtCvFinalIDs,BtPsFinalIDs,CvPsFinalIDs)

prepForMA <- filt_f[c(which(filt_f$ID %in% AllIDs)),]
#check all is well
table(AllIDs[order(AllIDs)]==prepForMA$ID)

#Add internal standard- 2­Amino­3­bromo­5­methylbenzoic acid, ID 33
prepForMA <- rbind(prepForMA,filt_f[filt_f$ID=="33",])

#Re-order columns
prepForMAo <- prepForMA[ , order(names(prepForMA))]

#Save for MetaboAnalyst

write.csv(prepForMAo,"/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/outputFiles/NonPolarPosFeatures_prepForMetaboAnalyst.csv")

#Manually edited file, put through Metabanalyst to normalize and cuberoot transform
#Continue from here

#PCA

#Read in normalized data from Metaboanalyst
dataNorm <- read.csv("/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/initialFiles/NonPolarPos_IndSamples_MetaboanalystNormalization_woMonos.csv",header=TRUE,row.names=1,stringsAsFactors=FALSE)

#Remove first row
dataNorm.edit <- dataNorm[-1,]

#Create metaData from column headers
mData.PP <- colnames(dataNorm.edit)

#Split names by "_" to get species
mDataSplit.PP <- data.frame(do.call('rbind', strsplit(as.character(mData.PP),'_',fixed=TRUE)))
#Split time remove replicate
mDataSplitTime.PP <- data.frame(do.call('rbind', strsplit(as.character(mDataSplit.PP$X3),'.',fixed=TRUE)))
#Bring metaData together
metaD.PP <- data.frame("Species"=mDataSplit.PP$X2, "Time"=mDataSplitTime.PP$X1)

#Prepare metadata table for varpart
metaDvP <- metaD.PP
#Add row names
row.names(metaDvP) <- mData.PP

metaD.PP <- data.frame("Group"=paste(mDataSplit.PP$X2,mDataSplitTime.PP$X1))

#Convert characters to numeric
dataNorm.edit <- as.data.frame(lapply(dataNorm.edit, as.numeric))

#Transpose
dataNormt <- t(dataNorm.edit)
colnames(dataNormt) <- rownames(dataNorm.edit) #newly added

#Load vegan
library(vegan)
dist.Metab <- vegdist(dataNormt, method="bray")

#Create groups
groups <- c(metaD.PP$Group)
#Calculate betadisper
mod <- betadisper(dist.Metab, groups)
#Calculates spatial median, not center of mass

#Variance explained should match individual plots from Metaboanalyst
PC1var <- mod$eig[1]/sum(mod$eig) #Checks out
PC2var <- mod$eig[2]/sum(mod$eig) #Checks out

#Extract scores
modScores <- scores(mod)

#Extract centroid
centroids <- as.data.frame(modScores$centroids)
#Extract species and time info
condI <- data.frame("Condition"=mDataSplit.PP$X2, "Time"=mDataSplitTime.PP$X1)
#Remove redundancies
condI <- condI[!duplicated(condI),]
#Add species
centroids$Label <- condI$Condition
#Add time
centroids$Time <- condI$Time
#Flip x-axis
#centroids$PCoA1 <- centroids$PCoA1*-1

#Extract axes vectors from individual samples
sites <- as.data.frame(modScores$sites)
#add groups
sites$groups <- mod$group

#Calculated PCoA axes SDs for each group
ag <- aggregate(.~groups,sites, function(x) sd=sd(x))
#Std axis 1
sd_axis1.PP <- ag$PCoA1
#Std axis 2
sd_axis2.PP <- ag$PCoA2

library(ggplot2)
#library(repr)
#options(repr.plot.width = 5, repr.plot.height = 3)
#Change time from factor to numeric
centroids$Time <- rep(c(12.5,25,30,35,40,45),4)

library(viridis)

PCA_NonPolarPos <- ggplot(centroids, aes(x=PCoA1, y=PCoA2))+
  geom_point(aes(size=factor(Time),fill=factor(Label)),colour="black",pch=21)+
  xlab(label = paste("PC1"," (", format(round(PC1var,digits = 3)*100,nsmall=1), "% var. explained)", sep = ""))+
  ylab(label = paste("PC2"," (", format(round(PC2var,digits = 3)*100,nsmall=1), "% var. explained)", sep = ""))+
  scale_x_continuous(breaks = pretty(centroids$PCoA1, n = 5)) +
  scale_y_continuous(breaks = pretty(centroids$PCoA2, n = 7)) +
  scale_fill_manual(values=c("#CC79A7", "#F0E442","#56B4E9","#000000")) +
  theme_bw()+
  theme(legend.position="none",axis.title = element_text(size = 10),axis.text = element_text(size = 8),
    legend.title=element_text(size=22))


PCA_NonPolarPosFinal <- PCA_NonPolarPos +
  geom_segment(data=centroids, aes(x=PCoA1,xend=PCoA1+sd_axis1.PP,y=PCoA2,yend=PCoA2),size=0.07,colour="black")+
  geom_segment(data=centroids, aes(x=PCoA1,xend=PCoA1-sd_axis1.PP,y=PCoA2,yend=PCoA2),size=0.07,colour="black")+
  geom_segment(data=centroids, aes(x=PCoA1,xend=PCoA1,y=PCoA2,yend=PCoA2+sd_axis2.PP),size=0.07,colour="black")+
  geom_segment(data=centroids, aes(x=PCoA1,xend=PCoA1,y=PCoA2,yend=PCoA2-sd_axis2.PP),size=0.07,colour="black")

#ggsave("/mnt/scratch/chodkows/PCA_NPP_Plot.eps",plot=PCA_NonPolarPosFinal,device="eps",width=12,height=12, units = "in", dpi=300)
ggsave("/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/PCA_NPP_Plot.eps",plot=PCA_NonPolarPosFinal,device="eps",width=4,height=4, units = "in", dpi=300)

#Run variation partitioning.
NPPvarPart <- varpart(dist.Metab,~Species,~Time,data=metaDvP)

#Run adonis- Ignore time for now.
condI <- data.frame("Condition"=mDataSplit.PP$X2, "Time"=mDataSplitTime.PP$X1)
rownames(condI) <- rownames(dataNormt)
adonis(dist.Metab ~ Condition, data=condI,permutations=999)
#Strain-specific exometabolite composition differs (even without taking into account time)

#Perform pairwise adonis post-hoc
library("RVAideMemoire")
#condI <- unite(condI, newcol, c(Condition, Time), remove=FALSE)
pairwise.perm.manova(dist.Metab,condI$Cond,nperm=999,p.method="fdr")

#####Perform Protest#####
library(vegan)
library(dplyr)

#Extract PC1&2 for bio reps
mod_sample.PCs <- as.data.frame(modScores$sites)
#Add strains
mod_sample.PCs$Strain <- mDataSplit.PP$X2
#Add bioreps
mod_sample.PCs$BR <- mDataSplitTime.PP$X2

#Extract B. thailandensis PCA PC1 & PC2 scores
BtPsR1 <- filter(mod_sample.PCs, Strain == "BtPs" & BR == "1")
BtPsR2 <- filter(mod_sample.PCs, Strain == "BtPs" & BR == "2")
BtPsR3 <- filter(mod_sample.PCs, Strain == "BtPs" & BR == "3")
BtPsR4 <- filter(mod_sample.PCs, Strain == "BtPs" & BR == "4")

#Extract B. thailandensis PCA PC1 & PC2 scores
BtCvR1 <- filter(mod_sample.PCs, Strain == "BtCv" & BR == "1")
BtCvR2 <- filter(mod_sample.PCs, Strain == "BtCv" & BR == "2")
BtCvR3 <- filter(mod_sample.PCs, Strain == "BtCv" & BR == "3")
BtCvR4 <- filter(mod_sample.PCs, Strain == "BtCv" & BR == "4")

#Extract C. violaceum PCA PC1 & PC2 scores
CvPsR1 <- filter(mod_sample.PCs, Strain == "CvPs" & BR == "1")
CvPsR2 <- filter(mod_sample.PCs, Strain == "CvPs" & BR == "2")
CvPsR3 <- filter(mod_sample.PCs, Strain == "CvPs" & BR == "3")
CvPsR4 <- filter(mod_sample.PCs, Strain == "CvPs" & BR == "4")

#Extract P. syringae PCA PC1 & PC2 scores
FullR1 <- filter(mod_sample.PCs, Strain == "Full" & BR == "1")
FullR2 <- filter(mod_sample.PCs, Strain == "Full" & BR == "2")
FullR3 <- filter(mod_sample.PCs, Strain == "Full" & BR == "3")
FullR4 <- filter(mod_sample.PCs, Strain == "Full" & BR == "4")

#Perform protest for BtCv
protest(X=BtCvR1[,1:2],Y=BtCvR2[1:4,1:2]) #missing data
protest(X=BtCvR1[,1:2],Y=BtCvR3[1:4,1:2]) #missing data
protest(X=BtCvR1[,1:2],Y=BtCvR4[1:4,1:2]) #missing data
protest(X=BtCvR2[,1:2],Y=BtCvR3[,1:2])
protest(X=BtCvR2[,1:2],Y=BtCvR4[,1:2])
protest(X=BtCvR3[,1:2],Y=BtCvR4[,1:2])

#Perform protest for BtPs
protest(X=BtPsR1[,1:2],Y=BtPsR2[2:4,1:2]) #missing data
protest(X=BtPsR1[,1:2],Y=BtPsR3[2:4,1:2]) #missing data
protest(X=BtPsR1[,1:2],Y=BtPsR4[2:4,1:2]) #missing data
protest(X=BtPsR2[,1:2],Y=BtPsR3[,1:2])
protest(X=BtPsR2[,1:2],Y=BtPsR4[,1:2])
protest(X=BtPsR3[,1:2],Y=BtPsR4[,1:2])

#Perform protest for CvPs
protest(X=CvPsR1[,1:2],Y=CvPsR2[c(3,4,6),1:2]) #missing data
protest(X=CvPsR1[,1:2],Y=CvPsR3[c(3,4,6),1:2]) #missing data
protest(X=CvPsR1[,1:2],Y=CvPsR4[c(3,4,6),1:2]) #missing data
protest(X=CvPsR2[,1:2],Y=CvPsR3[,1:2])
protest(X=CvPsR2[,1:2],Y=CvPsR4[,1:2])
protest(X=CvPsR3[,1:2],Y=CvPsR4[,1:2])

#Perform protest for Full
protest(X=FullR1[,1:2],Y=FullR2[1:2,1:2]) #missing data
protest(X=FullR1[,1:2],Y=FullR3[1:2,1:2]) #missing data
protest(X=FullR1[,1:2],Y=FullR4[1:2,1:2]) #missing data
protest(X=FullR2[,1:2],Y=FullR3[,1:2])
protest(X=FullR2[,1:2],Y=FullR4[,1:2])
protest(X=FullR3[,1:2],Y=FullR4[,1:2])

###############################Perform Protest on the filtered features for each member####################################
filt <- read.csv("/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/initialFiles/MZmineNonPolarPoswExConForQC.csv",check.names=F)

#Remove outlier-1mem_Bt_45hr-4
filt_f <- filt[,-c(115)]

#Load features
FullFinalIDs <- readRDS("/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/outputFiles/FullFinalIDsNonPolarPos.rds")
BtCvFinalIDs <- readRDS("/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/outputFiles/BtCvFinalIDsNonPolarPos.rds")
BtPsFinalIDs <- readRDS("/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/outputFiles/BtPsFinalIDsNonPolarPos.rds")
CvPsFinalIDs <- readRDS("/mnt/research/ShadeLab/Chodkowski/JGI_SynCom/SynCom_Paper_MassSpec/MS/outputFiles/CvPsFinalIDsNonPolarPos.rds")

FullFeatures <- filt_f[which(filt_f$ID %in% FullFinalIDs),]
BtCvFeatures <- filt_f[which(filt_f$ID %in% BtCvFinalIDs),]
BtPsFeatures <- filt_f[which(filt_f$ID %in% BtPsFinalIDs),]
CvPsFeatures <- filt_f[which(filt_f$ID %in% CvPsFinalIDs),]


library(tidyverse)
#Combine RT and MZ columns so that we can match to dataNorm file
FullFeaturesExt <- FullFeatures %>%
  unite("Feature", RT:MZ, sep= "/",
        remove = FALSE)

BtCvFeaturesExt <- BtCvFeatures %>%
  unite("Feature", RT:MZ, sep= "/",
        remove = FALSE)

BtPsFeaturesExt <- BtPsFeatures %>%
  unite("Feature", RT:MZ, sep= "/",
        remove = FALSE)

CvPsFeaturesExt <- CvPsFeatures %>%
  unite("Feature", RT:MZ, sep= "/",
        remove = FALSE)

#Extract features of interest from each coculture within the normalized file
dataNorm.Full <- dataNorm[match(FullFeaturesExt$Feature,rownames(dataNorm)),]
dataNorm.BtCv <- dataNorm[match(BtCvFeaturesExt$Feature,rownames(dataNorm)),]
dataNorm.BtPs <- dataNorm[match(BtPsFeaturesExt$Feature,rownames(dataNorm)),]
dataNorm.CvPs <- dataNorm[match(CvPsFeaturesExt$Feature,rownames(dataNorm)),]

#Convert characters to numeric
dataNorm.Full <- as.data.frame(lapply(dataNorm.Full, as.numeric))
dataNorm.BtCv <- as.data.frame(lapply(dataNorm.BtCv, as.numeric))
dataNorm.BtPs <- as.data.frame(lapply(dataNorm.BtPs, as.numeric))
dataNorm.CvPs <- as.data.frame(lapply(dataNorm.CvPs, as.numeric))

#Transpose
dataNormt.Full <- t(dataNorm.Full)
colnames(dataNormt.Full) <- rownames(dataNorm.Full) #newly added

dataNormt.BtCv <- t(dataNorm.BtCv)
colnames(dataNormt.BtCv) <- rownames(dataNorm.BtCv) #newly added

dataNormt.BtPs <- t(dataNorm.BtPs)
colnames(dataNormt.BtPs) <- rownames(dataNorm.BtPs) #newly added

dataNormt.CvPs <- t(dataNorm.CvPs)
colnames(dataNormt.CvPs) <- rownames(dataNorm.CvPs) #newly added

#Select rows containing only coculture of interest
dataNormt.Full.Ext <- dataNormt.Full[which(metaDvP$Species == "Full"),]
dataNormt.BtCv.Ext <- dataNormt.Full[which(metaDvP$Species == "BtCv"),]
dataNormt.BtPs.Ext <- dataNormt.Full[which(metaDvP$Species == "BtPs"),]
dataNormt.CvPs.Ext <- dataNormt.Full[which(metaDvP$Species == "CvPs"),]



#Load vegan
library(vegan)
dist.Metab.Full <- vegdist(dataNormt.Full.Ext, method="bray")
dist.Metab.BtCv <- vegdist(dataNormt.BtCv.Ext, method="bray")
dist.Metab.BtPs <- vegdist(dataNormt.BtPs.Ext, method="bray")
dist.Metab.CvPs <- vegdist(dataNormt.CvPs.Ext, method="bray")

#Extract groups from cocultures
library(data.table)
metaD.Full <- data.frame("Group"=metaD.PP[metaD.PP$Group %like% "Full", ])
metaD.BtCv <- data.frame("Group"=metaD.PP[metaD.PP$Group %like% "BtCv", ])
metaD.BtPs <- data.frame("Group"=metaD.PP[metaD.PP$Group %like% "BtPs", ])
metaD.CvPs <- data.frame("Group"=metaD.PP[metaD.PP$Group %like% "CvPs", ])

#Reform groups
groups.Full <- c(metaD.Full$Group)
groups.BtCv <- c(metaD.BtCv$Group)
groups.BtPs <- c(metaD.BtPs$Group)
groups.CvPs <- c(metaD.CvPs$Group)

#Calculate betadisper
mod.Full <- betadisper(dist.Metab.Full, groups.Full)
mod.BtCv <- betadisper(dist.Metab.BtCv, groups.BtCv)
mod.BtPs <- betadisper(dist.Metab.BtPs, groups.BtPs)
mod.CvPs <- betadisper(dist.Metab.CvPs, groups.CvPs)

#Note, groups was created before from the meta data table generated as preparation for the PCA

#Extract scores
modScores.Full <- scores(mod.Full)
modScores.BtCv <- scores(mod.BtCv)
modScores.BtPs <- scores(mod.BtPs)
modScores.CvPs <- scores(mod.CvPs)

#####Perform Protest#####
library(vegan)
library(dplyr)

#Extract PC1&2 for bio reps
mod_sample.PCs.Full <- as.data.frame(modScores.Full$sites)
mod_sample.PCs.BtCv <- as.data.frame(modScores.BtCv$sites)
mod_sample.PCs.BtPs <- as.data.frame(modScores.BtPs$sites)
mod_sample.PCs.CvPs <- as.data.frame(modScores.CvPs$sites)

#Add strains
mod_sample.PCs.Full$Strain <- mDataSplit.PP$X2[which(metaDvP$Species == "Full")]
mod_sample.PCs.BtCv$Strain <- mDataSplit.PP$X2[which(metaDvP$Species == "BtCv")]
mod_sample.PCs.BtPs$Strain <- mDataSplit.PP$X2[which(metaDvP$Species == "BtPs")]
mod_sample.PCs.CvPs$Strain <- mDataSplit.PP$X2[which(metaDvP$Species == "CvPs")]

#Add bioreps
mod_sample.PCs.Full$BR <- mDataSplitTime.PP$X2[which(metaDvP$Species == "Full")]
mod_sample.PCs.BtCv$BR <- mDataSplitTime.PP$X2[which(metaDvP$Species == "BtCv")]
mod_sample.PCs.BtPs$BR <- mDataSplitTime.PP$X2[which(metaDvP$Species == "BtPs")]
mod_sample.PCs.CvPs$BR <- mDataSplitTime.PP$X2[which(metaDvP$Species == "CvPs")]

#Extract B. thailandensis PCA PC1 & PC2 scores
BtPsR1 <- filter(mod_sample.PCs.BtPs, Strain == "BtPs" & BR == "1")
BtPsR2 <- filter(mod_sample.PCs.BtPs, Strain == "BtPs" & BR == "2")
BtPsR3 <- filter(mod_sample.PCs.BtPs, Strain == "BtPs" & BR == "3")
BtPsR4 <- filter(mod_sample.PCs.BtPs, Strain == "BtPs" & BR == "4")

#Extract B. thailandensis PCA PC1 & PC2 scores
BtCvR1 <- filter(mod_sample.PCs.BtCv, Strain == "BtCv" & BR == "1")
BtCvR2 <- filter(mod_sample.PCs.BtCv, Strain == "BtCv" & BR == "2")
BtCvR3 <- filter(mod_sample.PCs.BtCv, Strain == "BtCv" & BR == "3")
BtCvR4 <- filter(mod_sample.PCs.BtCv, Strain == "BtCv" & BR == "4")

#Extract C. violaceum PCA PC1 & PC2 scores
CvPsR1 <- filter(mod_sample.PCs.CvPs, Strain == "CvPs" & BR == "1")
CvPsR2 <- filter(mod_sample.PCs.CvPs, Strain == "CvPs" & BR == "2")
CvPsR3 <- filter(mod_sample.PCs.CvPs, Strain == "CvPs" & BR == "3")
CvPsR4 <- filter(mod_sample.PCs.CvPs, Strain == "CvPs" & BR == "4")

#Extract P. syringae PCA PC1 & PC2 scores
FullR1 <- filter(mod_sample.PCs.Full, Strain == "Full" & BR == "1")
FullR2 <- filter(mod_sample.PCs.Full, Strain == "Full" & BR == "2")
FullR3 <- filter(mod_sample.PCs.Full, Strain == "Full" & BR == "3")
FullR4 <- filter(mod_sample.PCs.Full, Strain == "Full" & BR == "4")

#Perform protest for BtCv
protest(X=BtCvR1[,1:2],Y=BtCvR2[1:4,1:2]) #missing data
protest(X=BtCvR1[,1:2],Y=BtCvR3[1:4,1:2]) #missing data
protest(X=BtCvR1[,1:2],Y=BtCvR4[1:4,1:2]) #missing data
protest(X=BtCvR2[,1:2],Y=BtCvR3[,1:2])
protest(X=BtCvR2[,1:2],Y=BtCvR4[,1:2])
protest(X=BtCvR3[,1:2],Y=BtCvR4[,1:2])

#Perform protest for BtPs
protest(X=BtPsR1[,1:2],Y=BtPsR2[2:4,1:2]) #missing data
protest(X=BtPsR1[,1:2],Y=BtPsR3[2:4,1:2]) #missing data
protest(X=BtPsR1[,1:2],Y=BtPsR4[2:4,1:2]) #missing data
protest(X=BtPsR2[,1:2],Y=BtPsR3[,1:2])
protest(X=BtPsR2[,1:2],Y=BtPsR4[,1:2])
protest(X=BtPsR3[,1:2],Y=BtPsR4[,1:2])

#Perform protest for CvPs
protest(X=CvPsR1[,1:2],Y=CvPsR2[c(3,4,6),1:2]) #missing data
protest(X=CvPsR1[,1:2],Y=CvPsR3[c(3,4,6),1:2]) #missing data
protest(X=CvPsR1[,1:2],Y=CvPsR4[c(3,4,6),1:2]) #missing data
protest(X=CvPsR2[,1:2],Y=CvPsR3[,1:2])
protest(X=CvPsR2[,1:2],Y=CvPsR4[,1:2])
protest(X=CvPsR3[,1:2],Y=CvPsR4[,1:2])

#Perform protest for Full
protest(X=FullR1[,1:2],Y=FullR2[1:2,1:2]) #missing data
protest(X=FullR1[,1:2],Y=FullR3[1:2,1:2]) #missing data
protest(X=FullR1[,1:2],Y=FullR4[1:2,1:2]) #missing data
protest(X=FullR2[,1:2],Y=FullR3[,1:2])
protest(X=FullR2[,1:2],Y=FullR4[,1:2])
protest(X=FullR3[,1:2],Y=FullR4[,1:2])