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DE_testing.R
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DE_testing.R
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#' ---
#' title: "Differential abundance testing for Hu et. al."
#' author: "F.M. Kerckhof"
#' date: "`r format(Sys.time(), '%F')`"
#' ---
#+ setup, include = FALSE
#### load required packages ####
library(readxl)
library(dplyr)
library(DESeq2)
library(knitr)
library(ggplot2)
library(gtools)
library(scales)
library(ggthemes)
#font_import()
loadfonts(device = "win")
#### User defined functions ####
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
#source("Highqualgraphr.R")
source("DataLoader.R")
absconc <- read.csv("Source_Data/Xiaona_absolute_counts.csv")
absconc.relab <- read.csv("Source_Data/Xiaona_absolute_counts.csv")
#+ spacer
#' # Loading & preformatting the data
#' First, all data was loaded and preformated/filtered in different ways for
#' comparative purposes.
#+ dataloader, fig.width=6, fig.height=7, cache=T
#### load the data ####
meta <- metadata.no.inoculum
shared <- shared.no.inoculum
#### preformat the data ###
sh.x <- shared
# rownames(sh.x) <- shared$OTU
#remove absoute singletons
sh.x.ns <- sh.x[which(rowSums(sh.x)!=1),]
meta <- data.frame(meta,Factor3=factor(paste(meta$Factor1,meta$Factor2,sep="_"))) # to have a one-factor DESeq design
#### prevalence filtering ####
## prevalence = (fraction of samples in which an OTU is observed minimum 1 time)
minobs=1
prevalence <- apply(as.matrix(sh.x.ns),1,function(x,minobs){sum(x>=minobs)},minobs)/ncol(sh.x.ns)
prevalencefilter <- prevalence>0.05
sh.x.prevfilt <- sh.x.ns[prevalencefilter,]
##Read counts should exceed 0.5 times the number of samples
sh.x.prevfilt <- sh.x.prevfilt[rowSums(sh.x.prevfilt)>0.5*ncol(sh.x.prevfilt),]
sh.x.prevfilt.prop <- sweep(sh.x.prevfilt,2,colSums(sh.x.prevfilt),'/')
# deseq normalise ==> very dependent upon design
#+ spacer
#' # Estimating overdispersion and variance stabilisation
#' Next, the prevalence-filtered data was normalized and variance-stabilized.
#+ deseqformatting
deseqdata <- as.matrix(sh.x.prevfilt +1)
deseqdata <- DESeqDataSetFromMatrix(deseqdata,colData=meta,design= ~ Factor3)
deseqdata <- estimateSizeFactors(deseqdata)
sharedfiltereddeseq <- counts(deseqdata,normalized=TRUE)
deseqdata <- estimateDispersions(deseqdata,fitType="local")
sharedfiltereddeseqvartransf <- varianceStabilizingTransformation(deseqdata, blind = FALSE)
sharedfiltereddeseqvartransfmatrix <- assay(sharedfiltereddeseqvartransf)
wnwndeseqvartransf <- getVarianceStabilizedData(deseqdata)
#jaccard_deseq <- vegdist(t(sharedfiltereddeseq),method="jaccard",binary="FALSE")
#+ spacer3
#' # Differential abundance testing
#' Ultimately, the transformed data was used to test for differential abundance.
#' Multiple testing correction controls the false discovery rate (FDR) using the
#' method proposed by Benjamini & Yekutieli (2001)
#+ DEtestdeseq, cache=TRUE
testdiff <- DESeq(deseqdata,test = "Wald",fitType = "local")
testdiff.res <- results(testdiff,pAdjustMethod = "BY")
testdiff.res.ordered <- testdiff.res[order(testdiff.res$padj),]
summary(testdiff.res)
signotu <- testdiff.res$padj<0.05
otunames <- row.names(sh.x.prevfilt)[signotu==TRUE]
resdf <- data.frame(sh.x.prevfilt,sigdif=signotu)
write.csv2(resdf,"differentialabundancexiaona.csv")
# with comments from ?results
testdiff.res5 <- results(testdiff,alpha=0.05,pAdjustMethod = "BY")
testdiff.res.ordered5 <- testdiff.res5[order(testdiff.res5$padj),]
summary(testdiff.res5)
signotu5 <- testdiff.res5$padj<0.05
resdf5 <- data.frame(sh.x.prevfilt,sigdif=signotu5,padjusted=testdiff.res5$padj)
write.csv(resdf5,"differentialxiaona5.csv")
#+ spacer 4
#' Hence it appears that `r length(otunames)` OTUs are significantly different among the different sources and nitrogen conditions among
#' the total of `r nrow(sh.x.prevfilt)` OTUs. A csv was created with this information included.
#+ spacer 4
#' # Visualisation all OTU DE
#' Only significant OTUs are plotted below
#'
pwvals <- results(testdiff,contrast = c("Factor3","BS_N2","BS_NH4"),pAdjustMethod = "BY")$padj
ggcols2 <- gg_color_hue(2)
saveotu.sig <- data.frame()
significantotus <- rownames(resdf5[!is.na(resdf5$sigdif)&resdf5$sigdif==TRUE,])
for(i in significantotus){
diffotu <- plotCounts(testdiff,gene=i,intgroup = "Factor3",returnData = TRUE,transform=FALSE)
diffotu <- cbind(diffotu,OTU=i)
saveotu.sig <- rbind(saveotu.sig,diffotu)
}
rankedsig <- saveotu.sig %>% group_by(OTU) %>% summarise(med=median(count)) %>% arrange(desc(med)) %>% select(OTU)
#### generate separate ggplot2 objects to be able to extract outliers ####
plotsig.vals <- ggplot(aes(y=count,x=Factor3,color=Factor3,shape=Factor3),
data=saveotu.sig) +
geom_boxplot(alpha=0.5,outlier.shape = 8,outlier.size = 3,
outlier.alpha = 1) +
geom_jitter(position=position_jitter(width=.3,
height=0),
size=4,alpha=1) +
scale_y_continuous(labels=scientific,trans='log10',
breaks = trans_breaks('log10',
function(x) 10^x)) +
ylab("Normalised log transformed counts")+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
text = element_text(size=18)) +
scale_x_discrete(breaks=c("BS_N2","HE_N2","SS_N2",
"BS_NH4","HE_NH4","SS_NH4"),
labels=c("BS_N2","HE_N2","SS_N2",
"BS_NH4","HE_NH4","SS_NH4")) +
facet_grid(~OTU,scales="free")
saveotu.sig$Factor3<- factor(saveotu.sig$Factor3,
levels=c("BS_N2","HE_N2","SS_N2",
"BS_NH4","HE_NH4","SS_NH4"))
saveotu.sig.XH <- saveotu.sig %>%
dplyr::filter(OTU %in% c("Otu00001","Otu00002","Otu00003"))
saveotu.sig.XH$OTUclass <- factor(saveotu.sig.XH$OTU)
levels(saveotu.sig.XH$OTUclass)[levels(saveotu.sig.XH$OTUclass)=="Otu00001"] <-
"Otu00001 (Azonexus)"
levels(saveotu.sig.XH$OTUclass)[levels(saveotu.sig.XH$OTUclass)=="Otu00002"] <-
"Otu00002 (Comamonadaceae)"
levels(saveotu.sig.XH$OTUclass)[levels(saveotu.sig.XH$OTUclass)=="Otu00003"] <-
"Otu00003 (Xanthobacter)"
plotsig.xh2 <- ggplot(aes(y=count,x=Factor3,color=Factor3,shape=Factor3),
data=saveotu.sig.XH) +
geom_boxplot(alpha=0.5,outlier.shape = 8,outlier.size = 3,
outlier.alpha = 1) +
geom_jitter(position=position_jitter(width=.3,
height=0),
size=2,alpha=1) +
scale_y_continuous(labels=scientific,trans='log10',
breaks = trans_breaks('log10',
function(x) 10^x)) +
ylab("Normalised log transformed counts")+
scale_color_discrete(name="Treatment",
breaks=c("BS_N2","HE_N2","SS_N2",
"BS_NH4","HE_NH4","SS_NH4"),
labels=expression(BS-N[2],HE-N[2],SS-N[2],
BS-NH[4]^"+",HE-NH[4]^"+",
SS-NH[4]^"+")) +
scale_shape_discrete(name="Treatment",
breaks=c("BS_N2","HE_N2","SS_N2",
"BS_NH4","HE_NH4","SS_NH4"),
labels=expression(BS-N[2],HE-N[2],SS-N[2],
BS-NH[4]^"+",HE-NH[4]^"+",
SS-NH[4]^"+")) +
theme_bw() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
text = element_text(size=10),
legend.title= element_blank(),
legend.text.align = 0,
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
facet_grid(~OTUclass,scales="free")
ggsave(filename="XiaonaDEsell.tiff",device="tiff",width=7,
height=4,units = "in",dpi = 300)
### Boxplot for the same with the counts from the data
absco.long <- absconc %>% group_by(X) %>%
tidyr::gather("Sample_name","Count",-X) %>%
filter(X %in% c("Otu00001","Otu00002","Otu00003"))
absco.long$Sample_type <- "BS N2"
absco.long$Sample_type[grep("HE[0-9].*",absco.long$Sample_name)]<-"HE N2"
absco.long$Sample_type[grep("SS[0-9].*",absco.long$Sample_name)]<-"SS N2"
absco.long$Sample_type[grep("BSN[0-9].*",absco.long$Sample_name)]<-"BS NH4"
absco.long$Sample_type[grep("HEN[0-9].*",absco.long$Sample_name)]<-"HE NH4"
absco.long$Sample_type[grep("SSN[0-9].*",absco.long$Sample_name)]<-"SS NH4"
absco.long$Sample_type <- factor(absco.long$Sample_type,
levels=c("BS N2","HE N2","SS N2",
"BS NH4","HE NH4","SS NH4"))
absco.long$OTUclass <- factor(absco.long$X)
levels(absco.long$OTUclass)[levels(absco.long$OTUclass)=="Otu00001"] <-
"Otu00001 (Azonexus)"
levels(absco.long$OTUclass)[levels(absco.long$OTUclass)=="Otu00002"] <-
"Otu00002 (Comamonadaceae)"
levels(absco.long$OTUclass)[levels(absco.long$OTUclass)=="Otu00003"] <-
"Otu00003 (Xanthobacter)"
absco.plot <- ggplot(aes(x=Sample_type,y=Count,shape=Sample_type,
color=Sample_type),
data=absco.long)+
geom_boxplot(alpha=0.5,outlier.shape = 8,outlier.size = 3,
outlier.alpha = 1) +
geom_jitter(position=position_jitter(width=.3,
height=0),
size=2,alpha=1) +
ylab("Absolute taxon abundance per mL")+
scale_color_discrete(name="Treatment",
breaks=c("BS N2","HE N2","SS N2",
"BS NH4","HE NH4","SS NH4"),
labels=expression(BS-N[2],HE-N[2],SS-N[2],
BS-NH[4]^"+",HE-NH[4]^"+",
SS-NH[4]^"+")) +
scale_shape_discrete(name="Treatment",
breaks=c("BS N2","HE N2","SS N2",
"BS NH4","HE NH4","SS NH4"),
labels=expression(BS-N[2],HE-N[2],SS-N[2],
BS-NH[4]^"+",HE-NH[4]^"+",
SS-NH[4]^"+")) +
theme_bw() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
text = element_text(size=10),
legend.title= element_blank(),
legend.text.align = 0,
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()) +
facet_grid(~OTUclass,scales="free")
ggsave(filename="Xiaonaabscosell.tiff",device="tiff",width=7,
height=4,units = "in",dpi = 300)
pdf("xiaonaDE.pdf")
for(i in significantotus){
otusel <- saveotu.sig[saveotu.sig$OTU==i,]
plotsig.vals <- ggplot(aes(y=count,x=Factor3,color=Factor3,shape=Factor3),
data=otusel) +
geom_boxplot(alpha=0.5,outlier.shape = 8,outlier.size = 3,
outlier.alpha = 1) +
geom_jitter(position=position_jitter(width=.3,
height=0),
size=4,alpha=1) +
scale_y_continuous(labels=scientific,trans='log10',
breaks = trans_breaks('log10',
function(x) 10^x)) +
ylab("Normalised log transformed counts")+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
text = element_text(size=18)) +
scale_x_discrete(breaks=c("BS_N2","HE_N2","SS_N2",
"BS_NH4","HE_NH4","SS_NH4"),
labels=c("BS_N2","HE_N2","SS_N2",
"BS_NH4","HE_NH4","SS_NH4")) +
facet_grid(~OTU,scales="free")
print(plotsig.vals)
}
dev.off()
#### do DE testing in subsampled datasets for each phase ####
#### 1. Phase 1 ####
shared.phase1 <- shared.t.ns %>%
dplyr::select(which(grepl(pattern = "[NES]1",colnames(shared.t.ns)))) %>%
dplyr::select(-contains("0"))
metadata.phase1 <- metadata[metadata$SampleName %in% colnames(shared.phase1),]
meta <- metadata.phase1
shared <- shared.phase1
sh.x <- shared
sh.x.ns <- sh.x[which(rowSums(sh.x)!=1),]
meta <- data.frame(meta,Factor3=factor(paste(meta$Factor1,meta$Factor2))) # to have a one-factor DESeq design
## prevalence = (fraction of samples in which an OTU is observed minimum 1 time)
minobs=1
prevalence <- apply(as.matrix(sh.x.ns),1,function(x,minobs){sum(x>=minobs)},minobs)/ncol(sh.x.ns)
prevalencefilter <- prevalence>0.05
sh.x.prevfilt <- sh.x.ns[prevalencefilter,]
##Read counts should exceed 0.5 times the number of samples
sh.x.prevfilt <- sh.x.prevfilt[rowSums(sh.x.prevfilt)>0.5*ncol(sh.x.prevfilt),]
sh.x.prevfilt.prop <- sweep(sh.x.prevfilt,2,colSums(sh.x.prevfilt),'/')
deseqdata <- as.matrix(sh.x.prevfilt +1)
deseqdata <- DESeqDataSetFromMatrix(deseqdata,colData=meta,design= ~ Factor3)
deseqdata <- estimateSizeFactors(deseqdata)
sharedfiltereddeseq <- counts(deseqdata,normalized=TRUE)
deseqdata <- estimateDispersions(deseqdata,fitType="local")
sharedfiltereddeseqvartransf <- varianceStabilizingTransformation(deseqdata, blind = FALSE)
sharedfiltereddeseqvartransfmatrix <- assay(sharedfiltereddeseqvartransf)
wnwndeseqvartransf <- getVarianceStabilizedData(deseqdata)
testdiff <- DESeq(deseqdata,test = "Wald",fitType = "local")
testdiff.res5 <- results(testdiff,alpha=0.05,pAdjustMethod = "BY")
testdiff.res.ordered5 <- testdiff.res5[order(testdiff.res5$padj),]
summary(testdiff.res5)
signotu5 <- testdiff.res5$padj<0.05
resdf5 <- data.frame(sh.x.prevfilt,sigdif=signotu5,padjusted=testdiff.res5$padj)
write.csv(resdf5,"differentialxiaona5_phase1.csv")
pwvals <- results(testdiff,contrast = c("Factor3","BS Nfree","BS Nitrogen"),pAdjustMethod = "BY")$padj
saveotu.sig <- data.frame()
significantotus <- rownames(resdf5[!is.na(resdf5$sigdif)&resdf5$sigdif==TRUE,])
for(i in significantotus){
diffotu <- plotCounts(testdiff,gene=i,intgroup = "Factor3",returnData = TRUE,transform=FALSE)
diffotu <- cbind(diffotu,OTU=i)
saveotu.sig <- rbind(saveotu.sig,diffotu)
}
saveotu.sig <- data.frame(saveotu.sig,Source=factor(substr(as.character(saveotu.sig$Factor3),0,2)))
rankedsig <- saveotu.sig %>% group_by(OTU) %>% summarise(med=median(count)) %>% arrange(desc(med)) %>% select(OTU)
plotsig.vals <- ggplot(aes(y=count,x=Factor3,color=Factor3,shape=Factor3),
data=saveotu.sig) +
geom_boxplot(alpha=0.5,outlier.shape = 8,outlier.size = 3,
outlier.alpha = 1) +
geom_jitter(position=position_jitter(width=.3,
height=0),
size=4,alpha=1) +
scale_y_continuous(labels=scientific,trans='log10',
breaks = trans_breaks('log10',
function(x) 10^x)) +
ylab("Normalised log transformed counts")+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
text = element_text(size=18)) +
scale_x_discrete(breaks=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen"),
labels=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen")) +
facet_grid(Source~OTU,scales="free")
saveotu.sig$Factor3<- factor(saveotu.sig$Factor3,levels=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen"))
pdf("xiaonaDEphase1.pdf")
for(i in significantotus){
otusel <- saveotu.sig[saveotu.sig$OTU==i,]
plotsig.vals <- ggplot(aes(y=count,x=Factor3,color=Factor3,shape=Factor3),
data=otusel) +
geom_boxplot(alpha=0.5,outlier.shape = 8,outlier.size = 3,
outlier.alpha = 1) +
geom_jitter(position=position_jitter(width=.3,
height=0),
size=4,alpha=1) +
scale_y_continuous(labels=scientific,trans='log10',
breaks = trans_breaks('log10',
function(x) 10^x)) +
ylab("Normalised log transformed counts")+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
text = element_text(size=18)) +
scale_x_discrete(breaks=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen"),
labels=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen")) +
facet_grid(~OTU,scales="free")
print(plotsig.vals)
}
dev.off()
#### 2. Phase 2 ####
meta <- metadata.endpoint
shared <- shared.endpoint
sh.x <- shared
sh.x.ns <- sh.x[which(rowSums(sh.x)!=1),]
meta <- data.frame(meta,Factor3=factor(paste(meta$Factor1,meta$Factor2))) # to have a one-factor DESeq design
## prevalence = (fraction of samples in which an OTU is observed minimum 1 time)
minobs=1
prevalence <- apply(as.matrix(sh.x.ns),1,function(x,minobs){sum(x>=minobs)},minobs)/ncol(sh.x.ns)
prevalencefilter <- prevalence>0.05
sh.x.prevfilt <- sh.x.ns[prevalencefilter,]
##Read counts should exceed 0.5 times the number of samples
sh.x.prevfilt <- sh.x.prevfilt[rowSums(sh.x.prevfilt)>0.5*ncol(sh.x.prevfilt),]
sh.x.prevfilt.prop <- sweep(sh.x.prevfilt,2,colSums(sh.x.prevfilt),'/')
deseqdata <- as.matrix(sh.x.prevfilt +1)
deseqdata <- DESeqDataSetFromMatrix(deseqdata,colData=meta,design= ~ Factor3)
deseqdata <- estimateSizeFactors(deseqdata)
sharedfiltereddeseq <- counts(deseqdata,normalized=TRUE)
deseqdata <- estimateDispersions(deseqdata,fitType="local")
sharedfiltereddeseqvartransf <- varianceStabilizingTransformation(deseqdata, blind = FALSE)
sharedfiltereddeseqvartransfmatrix <- assay(sharedfiltereddeseqvartransf)
wnwndeseqvartransf <- getVarianceStabilizedData(deseqdata)
testdiff <- DESeq(deseqdata,test = "Wald",fitType = "local")
testdiff.res5 <- results(testdiff,alpha=0.05,pAdjustMethod = "BY")
testdiff.res.ordered5 <- testdiff.res5[order(testdiff.res5$padj),]
summary(testdiff.res5)
signotu5 <- testdiff.res5$padj<0.05
resdf5 <- data.frame(sh.x.prevfilt,sigdif=signotu5,padjusted=testdiff.res5$padj)
write.csv(resdf5,"differentialxiaona5_endpoint.csv")
pwvals <- results(testdiff,contrast = c("Factor3","BS Nfree","BS Nitrogen"),pAdjustMethod = "BY")$padj
saveotu.sig <- data.frame()
significantotus <- rownames(resdf5[!is.na(resdf5$sigdif)&resdf5$sigdif==TRUE,])
for(i in significantotus){
diffotu <- plotCounts(testdiff,gene=i,intgroup = "Factor3",returnData = TRUE,transform=FALSE)
diffotu <- cbind(diffotu,OTU=i)
saveotu.sig <- rbind(saveotu.sig,diffotu)
}
saveotu.sig <- data.frame(saveotu.sig,Source=factor(substr(as.character(saveotu.sig$Factor3),0,2)))
rankedsig <- saveotu.sig %>% group_by(OTU) %>% summarise(med=median(count)) %>% arrange(desc(med)) %>% select(OTU)
plotsig.vals <- ggplot(aes(y=count,x=Factor3,color=Factor3,shape=Factor3),
data=saveotu.sig) +
geom_boxplot(alpha=0.5,outlier.shape = 8,outlier.size = 3,
outlier.alpha = 1) +
geom_jitter(position=position_jitter(width=.3,
height=0),
size=4,alpha=1) +
scale_y_continuous(labels=scientific,trans='log10',
breaks = trans_breaks('log10',
function(x) 10^x)) +
ylab("Normalised log transformed counts")+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
text = element_text(size=18)) +
scale_x_discrete(breaks=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen"),
labels=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen")) +
facet_grid(Source~OTU,scales="free")
saveotu.sig$Factor3<- factor(saveotu.sig$Factor3,levels=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen"))
pdf("xiaonaDEendpoint.pdf")
for(i in significantotus){
otusel <- saveotu.sig[saveotu.sig$OTU==i,]
plotsig.vals <- ggplot(aes(y=count,x=Factor3,color=Factor3,shape=Factor3),
data=otusel) +
geom_boxplot(alpha=0.5,outlier.shape = 8,outlier.size = 3,
outlier.alpha = 1) +
geom_jitter(position=position_jitter(width=.3,
height=0),
size=4,alpha=1) +
scale_y_continuous(labels=scientific,trans='log10',
breaks = trans_breaks('log10',
function(x) 10^x)) +
ylab("Normalised log transformed counts")+
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
text = element_text(size=18)) +
scale_x_discrete(breaks=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen"),
labels=c("BS Nfree","HE Nfree","SS Nfree",
"BS Nitrogen","HE Nitrogen","SS Nitrogen")) +
facet_grid(~OTU,scales="free")
print(plotsig.vals)
}
dev.off()