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AGR-RNA-Analysis-1.Rmd
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AGR-RNA-Analysis-1.Rmd
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---
title: "RNAseq analysis of wheat leaf asymptomatic green region during yellow spot infection"
author: "Paula Moolhuijzen"
date: '`r format(Sys.time(), "%d %B, %Y")`'
output:
html_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r path, echo=FALSE, message=F, warning=F}
setwd(getwd())
```
```{r load, echo=FALSE, message=FALSE, warning=FALSE}
library(DESeq2)
library(ggplot2)
library(genefilter)
library(gplots)
library(pheatmap)
library("dplyr")
library(pca3d)
library(ggfortify)
library("Hmisc")
library(corrplot)
library(reshape2)
library("ggVennDiagram")
library(topGO)
library("ggpubr")
library(stringr)
library(biomaRt)
library(RColorBrewer)
library(data.table)
library("VennDiagram")
library("tibble")
```
### Simple contrasts Yellow Spot infection of wheat (takes about 15 minutes)
For the contrasts genes are significant differential expressed (DE) at padj <= 0.005 & abs(log2FoldChange) >= 2
```{r norm, echo=FALSE, message=FALSE, warning=FALSE}
meta <- read.table("meta", sep="\t", header = TRUE)
mat <- read.table("matrix", sep="\t", header = TRUE, row.names=1)
meta$group <- factor(paste0(meta$cultivar, meta$inoculated, meta$treatment, meta$dpi))
data <- DESeqDataSetFromMatrix(countData=mat, colData=meta, design = ~group )
suppressMessages(
dds <- DESeq(data)
)
norm.counts <- counts(dds, normalized=TRUE)
norm.counts <- log2(norm.counts + 1)
```
Plots for mapped read count per sample
```{r readCount, fig.cap = "Sample read count", echo=FALSE}
sampColor <- c("Scout"="red", "Magenta"="blue")
## Add a column to the pheno type to specify the color of each sample according to its genotype
meta$color[meta$cultivar == "Scout"] <- sampColor["Scout"]
meta$color[meta$cultivar == "Magenta"] <- sampColor["Magenta"]
pdf(file="sample-reads-count.pdf")
bpt <- barplot(colSums(mat)/1000000,
main="Total no. reads per sample (million)",
col=meta$color,
las=1, horiz=TRUE,
ylab="Samples", cex.names=0.4,
xlab="Million counts")
dev.off()
```
```{r vst, echo=FALSE, message=FALSE, warning=FALSE}
vst <- vst(data, blind = FALSE)
mat.a <- assay(vst)
mat.a <- limma::removeBatchEffect(mat.a, vst$batch)
assay(vst) <- mat.a
```
### PCA plots
```{r elipPCA, fig.cap = "PCA plot", echo=FALSE, message=FALSE, warning=FALSE, fig.height=10, fig.width=12}
set <- colorRampPalette(brewer.pal(7, "Paired"))(14)
pdf("PCA-elip.pdf", height = 10, width=12)
pcaData <- plotPCA(vst, intgroup = c("cultivar","inoculated", "batch"), returnData = TRUE, ntop = 2000)
percentVar <- round(100 * attr(pcaData, "percentVar"))
p <- ggplot(pcaData, aes(x = PC1, y = PC2, color=group, label=name)) +
geom_point(size =3) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
ggtitle("PCA, variance stabilised") +
theme(panel.grid = element_blank(), panel.border = element_rect(fill= "transparent"),
axis.text=element_text(size=16), #text=element_text(family="Calibri"),
axis.title=element_text(size=18, face="bold"),
legend.text=element_text(size=18), legend.title=element_text(size=18, face="bold"),
strip.text = element_text(size = 16, face = "bold")) +
scale_fill_manual(values = set)
p + stat_ellipse(geom="polygon", aes(fill = group), alpha = 0.2, show.legend = FALSE, level = 0.95)
dev.off()
```
```{r threeDimPCA, echo=FALSE, fig.width=12, fig.height=10}
rv <- rowVars(assay(vst))
select <- head(order(rv, decreasing=TRUE), 5000)
pca <- prcomp(t(assay(vst)[select,]))
pca3d(pca, components = 1:3, group=meta$group, show.ellipses=TRUE, ellipse.ci=0.70, show.plane=FALSE, legend="bottomright", show.scale=FALSE, show.centroids=TRUE,show.group.labels=FALSE)
snapshotPCA3d("Treatment.png")
```
```{r twoDimPCA, echo=FALSE, warning=FALSE}
pdf("PCA-2d.pdf", height = 10, width=16)
pca2d(pca, group=meta$group, legend="bottomright", show.centroids = TRUE, show.group.labels=FALSE)
dev.off()
```
```{r culttreatPCA, fig.cap = "PCA plot", echo=FALSE, warning=FALSE}
set <- colorRampPalette(brewer.pal(12, "Paired"))(23)
pdf("PCA-cultivar-treatment.pdf", width = 12, height = 10)
pcaData <- plotPCA(vst, intgroup = c("cultivar","inoculated"), returnData = TRUE, ntop = 2000)
percentVar <- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(x = PC1, y = PC2, color = group, fill=group, label=name)) +
geom_point(size =3 ) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
ggtitle("PCA, variance stabilised") +
scale_fill_manual(values = set) +
guides(fill=guide_legend(title="Cultivar:Ptr inoculated"))
dev.off()
```
```{r , fig.cap = "PCA plot", echo=FALSE, warning=FALSE}
set <- colorRampPalette(brewer.pal(12, "Paired"))(12)
pdf("PCA.pdf", width = 10, height = 8)
pcaData <- plotPCA(vst, intgroup = c("cultivar","inoculated"), returnData = TRUE, ntop = 2000)
percentVar <- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(x = PC1, y = PC2, color = group)) +
geom_point(size=4) +
theme(axis.text=element_text(size=18), text=element_text(size = 18),
axis.title=element_text(size=20, face="bold"),
legend.text=element_text(size=16), legend.title=element_text(size=18),
strip.text = element_text(size = 16, face = "bold"),
strip.background=element_rect(fill="white"), panel.background = element_blank(),
panel.grid = element_line(color="grey90"),
legend.position = "right", panel.border = element_rect(fill = NA)) +
scale_color_discrete(name="Group") +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
ggtitle("PCA, variance stabilised") +
scale_fill_manual(values = set)
dev.off()
```
### Contrast analysis
1. Contrast Scout control versus Inoculated 3 dpi No fungicide - Group I
```{r groupI,echo=FALSE, message=FALSE}
res <- results(dds, contrast=c("group", "ScoutInoculatedNo_fungicide3" , "ScoutControlNo_fungicide3"))
sin3.scn3 <- subset(res, (padj <= 0.005 & !is.na(pvalue))& abs(log2FoldChange) >= 2 )
I <- data.frame(sin3.scn3)
I$Group <- "I"
I$Gene <- row.names(I)
```
2. Contrast Scout control versus inoculated 6 dpi No fungicide - Group II
```{r groupII, echo=FALSE, message=FALSE}
res <- results(dds, contrast=c("group", "ScoutInoculatedNo_fungicide6" , "ScoutControlNo_fungicide6"))
sin6.scn6 <- subset(res, (padj <= 0.005 & !is.na(pvalue))& abs(log2FoldChange) >= 2 )
II <- data.frame(sin6.scn6)
II$Group <- "II"
II$Gene <- row.names(II)
```
3. Contrast Scout control versus Inoculated 8 dpi No fungicide - Group III
```{r groupIII, echo=FALSE, message=FALSE}
res <- results(dds, contrast=c("group", "ScoutInoculatedNo_fungicide8" , "ScoutControlNo_fungicide8"))
sin8.scn8 <- subset(res, (padj <= 0.005 & !is.na(pvalue))& abs(log2FoldChange) >= 2 )
III <- data.frame(sin8.scn8)
III$Group <- "III"
III$Gene <- row.names(III)
```
4. Contrast Scout control fungicide versus Inoculated fungicide 6 dpi Group IV
```{r groupIV, echo=FALSE}
x <- results(dds, contrast=c("group", "ScoutInoculatedFungicide6" , "ScoutControlFungicide6"))
sif6.scf6 <- subset(x, (padj <= 0.005 & !is.na(pvalue))& abs(log2FoldChange) > 2 )
IV <- data.frame(sif6.scf6)
IV$Group <- "IV"
IV$Gene <- row.names(IV)
```
5. Contrast Scout control fungicide versus Inoculated fungicide 8 dpi Group V
```{r groupV, echo=FALSE}
x <- results(dds, contrast=c("group", "ScoutInoculatedFungicide8" , "ScoutControlFungicide8"))
sif8.scf8 <- subset(x, (padj <= 0.005 & !is.na(pvalue))& abs(log2FoldChange) > 2 )
V <- data.frame(sif8.scf8)
V$Group <- "V"
V$Gene <- row.names(V)
```
6. Contrast Magneta Control versus Inoculated 3 dpi No fungicide - Group VI
```{r groupVI, echo=FALSE, message=FALSE}
n <- list()
res <- results(dds, contrast=c("group", "MagentaInoculatedNo_fungicide3" , "MagentaControlNo_fungicide3"))
min3.mcn3 <- subset(res, (padj <= 0.005 & !is.na(pvalue))& abs(log2FoldChange) >= 2 )
VI <- data.frame(min3.mcn3)
VI$Group <- "VI"
VI$Gene <- row.names(VI)
```
7. Contrast Magneta Control versus Inoculated 8 dpi No fungicide - Group VII
```{r groupVII, echo=FALSE, message=FALSE}
res <- results(dds, contrast=c("group", "MagentaInoculatedNo_fungicide8" , "MagentaControlNo_fungicide8"))
min8.mcn8 <- subset(res, (padj <= 0.005 & !is.na(pvalue))& abs(log2FoldChange) >= 2 )
VII <- data.frame(min8.mcn8)
VII$Group <- "VII"
VII$Gene <- row.names(VII)
```
```{r join, echo=FALSE}
tlist <- list(
"I"=I,
"II"=II,
"III"=III,
"IV"=IV,
"V"=V,
"VI"=VI,
"VII"=VII)
tab <- rbindlist(tlist,use.names=TRUE)
```
### Table of regulation
```{r tableReg, fig.height=6, fig.width=10, echo=FALSE}
dat <- tab[log2FoldChange>0, .N, by = .(Group)][tab[log2FoldChange<0, .N, by =.(Group)] , on = c("Group"), nomatch=0]
colnames(dat)[2] <- "Induced"
colnames(dat)[3] <- "Suppressed"
dat2 <- as.matrix(dat[,-1])
rownames(dat2) <- dat$Group
longData<- reshape2::melt(dat2) #melt(dat2)
h<- ggplot(longData, aes(y = Var2, x = Var1)) +
geom_raster(aes(fill=value)) +
scale_fill_gradient(low="grey90", high="red", guide = "none") +
geom_text(aes(label = round(value, 1)), size=10) +
labs(y="SDEG Regulation", x="Group") +
theme(axis.text=element_text(size=24), text=element_text(family="Calibri", size = 20),
axis.title=element_text(size=28, face="bold"),
strip.text = element_text(size = 20, face = "bold"),
strip.background=element_rect(fill="white"), panel.background = element_blank(),
panel.grid = element_line(color="grey90"))
ggsave(h, file="Figure3C-SDEG.png", width=10, height=6)
```
### Venn diagrams
Induced Venn
```{r inducedVenn, echo=FALSE}
I <- subset(tab, (Group == "I") & (log2FoldChange) >= 1)
I <- I$Gene
II <- subset(tab, (Group == "II") & (log2FoldChange) >= 1)
II <- II$Gene
III <- subset(tab, (Group == "III") & (log2FoldChange) >= 1)
III <- III$Gene
IV <- subset(tab, (Group == "IV") & (log2FoldChange) >= 1)
IV <- IV$Gene
V <- subset(tab, (Group == "V") & (log2FoldChange) >= 1)
V <- V$Gene
VI <- subset(tab, (Group == "VI") & (log2FoldChange) >= 1)
VI <- VI$Gene
VII <- subset(tab, (Group == "VII") & (log2FoldChange) >= 1)
VII <- VII$Gene
Group_I_II_III = c(I, II, III)
Group_IV_V = c(IV, V)
Group_VI_VII = c(VI, VII)
```
```{r inducedGroup, fig.height=10, fig.width=10, echo=FALSE}
vdeu <- list(
"I_II_III"= Group_I_II_III,
"IV_V"= Group_IV_V,
"VI_VII" = Group_VI_VII
)
pdf(file="venn-diagram-up-bulk-groups.pdf", width=10, height=10)
ggVennDiagram(vdeu, label="both",lwd = 0.3, lty = 1, color = "grey", label_alpha = 0, edge_size = 0.3, label_size = 12, set_size = 12) +
scale_fill_gradient(low = "white", high = "red") +
theme(legend.position = "none", title=element_text(size=28), plot.title = element_text(hjust = 0.5)) +
labs(title="Induced SDEGs")
dev.off()
```
Suppressed Venn
```{r supVenn, fig.height=10, fig.width=10, echo=FALSE}
I <- subset(tab, (Group == "I") & (log2FoldChange) < 1)
I <- I$Gene
II <- subset(tab, (Group == "II") & (log2FoldChange) < 1)
II <- II$Gene
III <- subset(tab, (Group == "III") & (log2FoldChange) < 1)
III <- III$Gene
IV <- subset(tab, (Group == "IV") & (log2FoldChange) < 1)
IV <- IV$Gene
V <- subset(tab, (Group == "V") & (log2FoldChange) < 1)
V <- V$Gene
VI <- subset(tab, (Group == "VI") & (log2FoldChange) < 1)
VI <- VI$Gene
VII <- subset(tab, (Group == "VII") & (log2FoldChange) < 1)
VII <- VII$Gene
Group_I_II_III = c(I, II, III)
Group_IV_V = c(IV, V)
Group_VI_VII = c(VI, VII)
```
```{r groupVenn, fig.height=10, fig.width=10, echo=FALSE}
vded <- list(
"I_II_III"= Group_I_II_III,
"IV_V"= Group_IV_V,
"VI_VII" = Group_VI_VII
)
pdf(file="venn-diagram-down-bulk-groups.pdf", width=10, height=10)
ggVennDiagram(vded, label="both",lwd = 0.3, lty = 1, color = "grey", label_alpha = 0, edge_size = 0.3, label_size = 12, set_size = 12) +
scale_fill_gradient(low = "white", high = "red") +
theme(legend.position = "none", title=element_text(size=28), plot.title = element_text(hjust = 0.5)) +
labs(title="Suppressed SDEGs")
dev.off()
```
### GO biomaRt
```{r loadBiomart}
library(biomaRt)
library(stringr)
```
```{r, warning=FALSE}
mart <- useMart("plants_mart", host = "plants.ensembl.org")
```
```{r}
listEnsembl(mart)
```
BioMart and Paths
```{r}
tmart=useDataset("taestivum_eg_gene", mart = mart)
```
Run to download all gene GO ids - (converted to geneid2go.map.txt format with slurp-topgo.pl)
```{r downloadGO, echo=FALSE}
#Get all gene ids and go ids
#geneid2go.map <- getBM(attributes = c("ensembl_gene_id","go_id"), mart = tmart)
#write.table(geneid2go.map, file="geneid2go.txt", sep="\t", col.names=FALSE, row.names=FALSE)
#geneid2go.map <- setNames(split(geneid2go.map$go_id, seq(nrow(geneid2go.map))), geneid2go.map$ensembl_gene_id)
```
### GO analyses for SDEGs
GO term enrichment for significantly DE genes are significant at p-value < 0.01
Reading in data for all wheat gene ids mapped to Gene Ontologies (GO)
```{r readGO, echo=FALSE, message=FALSE}
geneid2go <- readMappings(file = "geneid2go.map.txt", sep="\t")
geneNames <- names(geneid2go)
```
https://support.bioconductor.org/p/29775/
The SDEG lists for all contrasts - TopGo analysis - run time is around 10 mins
```{r topGO, echo=FALSE, warning=FALSE, message=FALSE}
dflist <- list("GroupI"=sin3.scn3,
"GroupII"=sin6.scn6,
"GroupIII"=sin8.scn8,
"GroupIV"=sif6.scf6,
"GroupV"=sif8.scf8,
"GroupVI"=min3.mcn3,
"GroupVII"=min8.mcn8)
glist <-c("MF","BP","CC")
cnt=0
b=0
l <- list()
for(e in dflist){
cnt=cnt+1
nm<- names(dflist[cnt])
deup <- factor(as.vector(row.names(subset(e, log2FoldChange >= 1))))
ded <- factor(as.vector(row.names(subset(e, log2FoldChange <= -1))))
geneListUP <- factor(as.integer(geneNames %in% deup))
names(geneListUP) <- geneNames
geneListD <- factor(as.integer(geneNames %in% ded))
names(geneListD) <- geneNames
for(go in glist){
b=b+1
# GO analysis for up-regulated SDEGs
suppressMessages(
sampleGOup <- new("topGOdata", ontology = go, allGenes = geneListUP,
geneSel = deup, nodeSize = 5,
annot = annFUN.gene2GO, gene2GO = geneid2go)
)
allGO.up = genesInTerm(sampleGOup)
resultFisher <- runTest(sampleGOup, algorithm = "weight01", statistic = "fisher")
induced <- GenTable(sampleGOup, weightFisher = resultFisher, orderBy='weightFisher',
topNodes = length(resultFisher@score), numChar = 120)
induced$weightFisher <- gsub( "< ", "", as.character(induced$weightFisher))
induced$weightFisher <- as.numeric(induced$weightFisher)
induced.p= induced[which(induced$weightFisher<=0.01),]
induced.p$reg[induced.p$weightFisher <=0.01] <- "Induced"
# get the gene lists
AnnotatedGenes.u = lapply(induced.p$GO.ID, function(x) as.character(unlist(genesInTerm(object = sampleGOup, whichGO = x))))
# GO analysis for the down-regulated SDEGs
suppressMessages(
sampleGOdwn <- new("topGOdata", ontology = go, allGenes = geneListD,
geneSel = ded,
annot = annFUN.gene2GO, gene2GO = geneid2go)
)
allGO.dwn = genesInTerm(sampleGOdwn)
resultFisher <- runTest(sampleGOdwn, algorithm = "weight01", statistic = "fisher")
supress <- GenTable(sampleGOdwn, weightFisher = resultFisher, orderBy='weightFisher',
topNodes = length(resultFisher@score), numChar = 150)
supress$weightFisher <- gsub( "< ", "", as.character(supress$weightFisher))
supress$weightFisher <- as.numeric(supress$weightFisher)
supress.p= supress[which(supress$weightFisher<=0.01),]
supress.p$reg[supress.p$weightFisher <=0.01] <- "Supressed"
new <- rbind(supress.p, induced.p)
# get the gene lists
AnnotatedGenes.d = lapply(supress.p$GO.ID, function(x) as.character(unlist(genesInTerm(object = sampleGOdwn, whichGO = x))))
new$reg <- factor(new$reg, levels = c("Induced", "Supressed"),
labels = c("Induced", "Supressed"))
new$godomain <- go
new$godomain <- factor(new$godomain, levels = c(go),
labels = c(go))
new$genotype <- nm
new$Term = str_wrap(new$Term, width = 50)
l[[b]] <-new
}
}
```
Bind all GO for SDEGs
```{r bindGO, echo=FALSE, message=F, warning=F, fig.width=10, fig.height=12}
go <- rbindlist(l,use.names=TRUE)
colnames(go)[7] <- "Regulation"
colnames(go)[9] <- "Group"
go$Group[go$Group == "GroupI"] <- "I"
go$Group[go$Group == "GroupII"] <- "II"
go$Group[go$Group == "GroupIII"] <- "III"
go$Group[go$Group == "GroupIV"] <- "IV"
go$Group[go$Group == "GroupV"] <- "V"
go$Group[go$Group == "GroupVI"] <- "VI"
go$Group[go$Group == "GroupVII"] <- "VII"
write.table(go, file="group-go-results.txt", sep="\t", col.names=TRUE, row.names=TRUE)
```
Biological processes Figure 3D groups I-VII
```{r bp, echo=FALSE, message=FALSE, warning=FALSE, fig.width=8, fig.height=14}
bp <- subset(go, go$godomain == "BP")
bp <- subset(bp, bp$Term != "metabolic process")
bp.bar <- list("photosynthesis",
"calcium ion binding",
"chitin catabolic process",
"ethylene-activated signaling pathway",
"fatty acid biosynthetic process",
"L-phenylalanine catabolic process",
"lipid transport",
"microtubule-based movement",
"oligopeptide transport",
"protein metabolic process",
"recognition of pollen",
"regulation of systemic acquired resistance",
"response to hormone",
"response to stress",
"translation",
"zinc ion transmembrane transport")
bp <- subset(bp, bp$weightFisher < 1e-04)
bp <- subset(bp, bp$Term %in% bp.bar)
#meta$ratio <- meta$Significant/meta$Annotated
len <- length(unique(bp$Term))
bp$Term = str_wrap(bp$Term,20)
set <- colorRampPalette(brewer.pal(12, "Paired"))(len)
r <- ggplot(bp, aes(Group)) +
theme(axis.text=element_text(size=26), text=element_text(family="Calibri"),
axis.title=element_text(size=28, face="bold"),
legend.text=element_text(size=26), legend.title=element_text(size=26, face="bold"),
strip.text = element_text(size = 2, face = "bold"),
strip.background=element_rect(fill="white"), panel.background = element_blank(),
panel.grid = element_line(color="grey90"), legend.position = "right", panel.border = element_rect(fill = NA)) +
geom_bar(position="stack", data = subset(bp, Regulation == "Induced"),
aes(y = Significant, fill = Term), stat = "identity") +
geom_bar(position="stack", data = subset(bp, Regulation == "Supressed"),
aes(y = -Significant, fill = Term), stat = "identity") + xlab("Group") + ylab("Number of Significant DE genes") +
guides(fill=guide_legend(nrow=len, title="GO biological process")) + geom_hline(yintercept = 0,colour = "grey90") + scale_fill_manual(values = set)
r
ggsave(r, file="figure3D-manuscript-BP-go.png", width=8, height=14)
```
Cellular components Figure 3F groups I-VII
```{r cc, echo=FALSE, message=F, warning=F, fig.width=8, fig.height=14}
cc <- subset(go, go$godomain == "CC")
#cc.bar <- list( "")
cc <- subset(cc, cc$weightFisher < 1e-04)
len=12
cc$Term = str_wrap(cc$Term,20)
set <- colorRampPalette(brewer.pal(12, "Paired"))(len)
s <- ggplot(cc, aes(Group)) +
theme(axis.text=element_text(size=26), text=element_text(family="Calibri"),
axis.title=element_text(size=28, face="bold"),
legend.text=element_text(size=26), legend.title=element_text(size=26, face="bold"),
strip.text = element_text(size = 26, face = "bold"),
strip.background=element_rect(fill="white"), panel.background = element_blank(),
panel.grid = element_line(color="grey90"), legend.position = "right", panel.border = element_rect(fill = NA)) +
geom_bar(position="stack", data = subset(cc, Regulation == "Induced"),
aes(y = Significant, fill = Term), stat = "identity") +
geom_bar(position="stack", data = subset(cc, Regulation == "Supressed"),
aes(y = -Significant, fill = Term), stat = "identity") + xlab("Group") + ylab("Number of Significant DE genes") +
guides(fill=guide_legend(nrow=len, title="GO cellular component")) + geom_hline(yintercept = 0,colour = "grey90") + scale_fill_manual(values = set)
s
ggsave(s, file="figure3E-manuscript-CC-go.png", width=8, height=14)
```
Molecular functions Figure 3F groups I-VII
```{r mf, echo=FALSE, message=F, warning=F, fig.width=8, fig.height=14}
mf <- subset(go, go$godomain == "MF")
#mf <- subset(mf, mf$Term != "metabolic process")
mf.bar <- list(
"calcium ion binding",
"DNA-binding transcription factor activity",
"iron ion binding",
"oxidoreductase activity",
"peroxidase activity",
"polysaccharide binding",
"protein serine/threonine kinase activity",
"sequence-specific DNA binding",
"terpene synthase activity",
"transporter activity",
"zinc ion transmembrane transporter activity"
)
mf <- subset(mf, mf$weightFisher < 1e-04)
mf <- subset(mf, mf$Term %in% mf.bar)
#meta$ratio <- meta$Significant/meta$Annotated
#len <- length(unique(mf$Term))
len=12
mf$Term = str_wrap(mf$Term,20)
set <- colorRampPalette(brewer.pal(12, "Paired"))(len)
t <- ggplot(mf, aes(Group)) +
theme(axis.text=element_text(size=26), text=element_text(family="Calibri"),
axis.title=element_text(size=28, face="bold"),
legend.text=element_text(size=26), legend.title=element_text(size=26, face="bold"),
strip.text = element_text(size = 26, face = "bold"),
strip.background=element_rect(fill="white"), panel.background = element_blank(),
panel.grid = element_line(color="grey90"), legend.position = "right", panel.border = element_rect(fill = NA)) +
geom_bar(position="stack", data = subset(mf, Regulation == "Induced"),
aes(y = Significant, fill = Term), stat = "identity") +
geom_bar(position="stack", data = subset(mf, Regulation == "Supressed"),
aes(y = -Significant, fill = Term), stat = "identity") + xlab("Group") + ylab("Number of Significant DE genes") +
guides(fill=guide_legend(nrow=len, title="GO moleuclar function")) + geom_hline(yintercept = 0,colour = "grey90") + scale_fill_manual(values = set)
t
ggsave(t, file="figure3F-manuscript-MF-go.png", width=8, height=14)
```
### Heatmaps for selected defence related SDEGs in manuscript table
Interpro information
```{r interproSub, echo=FALSE, message=FALSE}
dflist <- list("GroupI"=sin3.scn3,
"GroupII"=sin6.scn6,
"GroupIII"=sin8.scn8,
"GroupIV"=sif6.scf6,
"GroupV"=sif8.scf8,
"GroupVI"=min3.mcn3,
"GroupVII"=min8.mcn8
)
cnt=0
b=0
l <- list()
for(e in dflist){
cnt=cnt+1
nm<- names(dflist[cnt])
e$Group <- nm
g <- as.vector(row.names(e))
ga <- getBM(attributes = c("ensembl_gene_id", "description","interpro", "interpro_description"),
filters = "ensembl_gene_id", values = g, mart = tmart)
e<-as.data.frame(e)
ega <- merge(x=e, y=ga, by.x=0, by.y="ensembl_gene_id", all=TRUE)
l[[cnt]] <-ega
}
```
write interpro annotation for SDEGs
```{r interproTable, echo=FALSE, message=FALSE}
ipro <- rbindlist(l,use.names=TRUE)
write.table(ipro, file="significant-genes-IPR.txt", sep="\t", col.names=TRUE, row.names=TRUE)
```
```{r interproSubset, echo=FALSE, message=FALSE}
i <- list("NB.ARC"="IPR002182", "Leucine.rich.repeat"="IPR001611", "Mlo"="IPR004326",
"ABC.transporter"="IPR003439", "Sugar.Transporter"="IPR005828",
"Glucasyltransferase"= "IPR002213", "Heme.peroxidase"="IPR002016",
"Eythlene.responsive.tf"="IPR044808","AIB.MYC.like.tf"="IPR045084",
"WRKY.domain"="IPR003657","AP2.ERF.tf"="IPR017392",
"bHLH.tf"="IPR031066","MYC.MYB.N.terminal.tf"="IPR025610",
"Glutathione.S.transferase.N"="IPR004045","PR.14.IPR"="IPR000528",
"PR.16.Germin"="IPR001929", "PR.17.IPR"="IPR007541")
iprof <- ipro[interpro %chin% i]
iprof$FC[iprof$log2FoldChange >= 1] <- "Induced"
iprof$FC[iprof$log2FoldChange < 0] <- "Suppressed"
```
write SDEGs interpro annotation for subset of defence related terms
```{r, echo=FALSE}
# Adds readable IPR group label
cnt = 0
for(e in i){
cnt=cnt+1
iprof[interpro %in% e] ~ names(i[cnt])
iprof$IPR[iprof$interpro %in% e] <- names(i[cnt])
iprof$Id[iprof$interpro %in% e] <- i[cnt]
}
tmp <- as.data.frame(iprof)
tmp <- subset(tmp, select = -c(Id))
write.table(tmp, file="significant-genes-subset-IPR.txt", sep="\t", col.names=TRUE, row.names=TRUE)
```
GO information
```{r GO, echo=FALSE, message=FALSE}
dflist <- list("GroupI"=sin3.scn3,
"GroupII"=sin6.scn6,
"GroupIII"=sin8.scn8,
"GroupIV"=sif6.scf6,
"GroupV"=sif8.scf8,
"GroupVI"=min3.mcn3,
"GroupVII"=min8.mcn8
)
cnt=0
b=0
l <- list()
for(e in dflist){
cnt=cnt+1
nm<- names(dflist[cnt])
e$Group <- nm
g <- as.vector(row.names(e))
ga <- getBM(attributes = c("ensembl_gene_id", "description","go_id", "name_1006", "namespace_1003"),
filters = "ensembl_gene_id", values = g, mart = tmart)
e<-as.data.frame(e)
ega <- merge(x=e, y=ga, by.x=0, by.y="ensembl_gene_id", all=TRUE)
l[[cnt]] <-ega
}
```
write GO annotation for SDEGs
```{r tableGO, echo=FALSE, message=FALSE}
gos <- rbindlist(l,use.names=TRUE)
gos$FC[gos$log2FoldChange >= 1] <- "Induced"
gos$FC[gos$log2FoldChange < 0] <- "Suppressed"
write.table(gos, file="significant-genes-GO.txt", sep="\t", col.names=TRUE, row.names=TRUE)
```
```{r subsetGO, echo=FALSE, message=FALSE}
gi <- list("Trancription.factors"="GO:0006355", "Protien.kinase"="GO:0004672",
"Defence"="GO:0006952",
"Transporter"="GO:0055085",
"SA.bp"="GO:0009697", # Salicylic acid (SA) biosynthetic processes
"JA.bp"="GO:0009695", # jasmonic acid (JA) biosynthetic processe
"ET.asp"="GO:0009873", # ethylene (ET) activated signalling pathway
"JA.msp"="GO:0009867", # JA mediated signalling pathway
"JA.ET.dsr"="GO:0009861", # JA and ET-dependent systemic resistance
"SAR.SA.msp"="GO:0009862") # systemic acquired resistance involving SA-mediated signalling pathways
gosf <- gos[go_id %chin% gi ]
gosf$FC[gosf$log2FoldChange >= 1] <- "Induced"
gosf$FC[gosf$log2FoldChange < 0] <- "Suppressed"
write.table(gosf, file="significant-genes-subset-GO.txt", sep="\t", col.names=TRUE, row.names=TRUE)
```
write SDEG GO annotation for subset of defence terms
```{r, echo=FALSE}
# Adds readable GO label
cnt = 0
for(e in gi){
cnt=cnt+1
gosf[go_id %in% e] ~ names(gi[cnt])
gosf$GO[gosf$go_id %in% e] <- names(gi[cnt])
gosf$ID[gosf$go_id %in% e] <- gi[cnt]
}
tmp <- as.data.frame(gosf)
tmp <- subset(tmp, select = -c(ID))
write.table(tmp, file="significant-genes-subset-GO-label.txt", sep="\t", col.names=TRUE, row.names=TRUE)
```
heatmap filtered GO
```{r , fig.cap="Effect of treatment in Susceptible ", echo=FALSE, fig.width=10, fig.height=12 }
go <- norm.counts[ gosf$Row.names, ]
go.sum <- t(apply(go, 1, function(row) c(mean(row[c(1,12,23,42,43,44)]), #SCN3 GroupI
mean(row[c(34,37,38,45,46,47)]), #SIN3 GroupI
mean(row[c(11,13,27,60,61,74)]), #SCN8 GroupIII
mean(row[c(15,16,17,63,64,65)]), #SIN8 GroupIII
mean(row[c(24,25,26,71,72,73)]), #MCN3 GroupVI
mean(row[c(14,28,29,62,75,76)]), #MIN3 GroupVI
mean(row[c(30,31,32,77,78,79)]), #MCN8 GroupVII
mean(row[c(33,35,36,80,81,82)]) #MIN8 GroupVII
)))
go.sum <- go.sum - rowMeans(go.sum)
colnames(go.sum) <- c("SCN3", "SIN3","SCN8", "SIN8", "MCN3",
"MIN3", "MCN8", "MIN8")
anno <- as.data.frame(colData(dds)[, c( "cultivar", "inoculated", "treatment", "dpi")])
x <- c("Scout", "Scout", "Scout", "Scout", "Magenta",
"Magenta","Magenta", "Magenta" )
y <- colnames(go.sum)
z <- c("Control","Inoc.", "Control",
"Inoc.","Control","Inoc.","Control", "Inoc.")
anno <- data.frame("cultivar" = x, "ptr"= z, row.names=y)
anno$dpi <- c("3d","3d","8d","8d","3d","3d","8d","8d" )
gosf <- gosf %>% dplyr::rename(Gene = 1)
annor <- as.data.frame(gosf[,c( "Gene", "GO")])
#Remove duplicates
clean_data <- annor %>%
dplyr::group_by(Gene) %>%
dplyr::summarise(GO = paste0(unique(GO), collapse = "_"))
clean_data <- as.data.frame(clean_data$GO, row.names=clean_data$Gene)
clean_data <- clean_data %>% dplyr::rename(GO = 1)
clean_data$GO[clean_data$GO == "Protien.kinase_Defence"] <- "Defence_Protien.kinase"
#
go.sum.u <- unique(go.sum)
# set colors for row
len <- length(unique(clean_data$GO))
set <- colorRampPalette(brewer.pal(5, "Set1"))(len)
names <- sort(unique(clean_data$GO))
names(set) <- names
# Specify colors
ann_colors <- list(
cultivar = c(Scout="firebrick",Magenta="blue"),
ptr = c(Control = "#1B9E77", Inoc. = "#D95F02"),
dpi = c("3d" = "#7570B3", "8d" = "#E7298A"),
GO = set )
pheatmap(go.sum.u, annotation_col = anno, annotation_row = clean_data, fontsize_col = 12, fontsize_row = 12, show_rownames=F, show_colnames=TRUE, cluster_rows = TRUE, cluster_cols = FALSE, cutree_rows = 10, annotation_colors = ann_colors)
```
```{r heatmapGO, fig.cap="Effect of treatment in Susceptible ", echo=FALSE, fig.width=10, fig.height=10}
pdf("heatmap-defence-GO.pdf", width=10, height=10)
pheatmap(go.sum.u, annotation_col = anno, annotation_row = clean_data, fontsize_col = 12, fontsize_row = 12, show_rownames=F, show_colnames=TRUE, cluster_rows = TRUE, cluster_cols = FALSE, cutree_rows = 10, annotation_colors = ann_colors)
dev.off()
```
Pfam information
```{r pfam, echo=FALSE, message=FALSE}
dflist <- list("GroupI"=sin3.scn3,
"GroupII"=sin6.scn6,
"GroupIII"=sin8.scn8,
"GroupIV"=sif6.scf6,
"GroupV"=sif8.scf8,
"GroupVI"=min3.mcn3,
"GroupVII"=min8.mcn8
)
p <- list("PR.1.cysteine.rich"="PF00188","PR.2.glucanse"="PF00332",
"PR.3.chitinase"="PF00182","PR.4.barwin"="PF00967",
"PR.5.thaumatin"="PF00314","PR.6.serpin"="PF00079",
"PR.6.bowman.birk"="PF00228","PR.6.inhibitor"="PF00280",
"PR.8.subtilase"="PF00704","PR.9.peroxidase"="PF00141",
"PR.10.betv1"="PF00407","PR.12.gama.thionin"="PF00304",
"PR.13.thionine"="PF00321","PR.14.nsLTP"="PF00234",
"PR.15.cupin"="PF00190","PR.17.BSP"="PF04450",
"Chalcone.synthase-N"="PF00195","Chalcone.synthase-C"="PF02797",
"MatE"="PF01554", "Cytochrome.P450"="PF00067")
cnt=0
b=0
l <- list()
for(e in dflist){