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Barley-DH-POW-RNA.Rmd
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---
title: "Powdery Mildew RNA analysis bulk lines"
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$set(echo = TRUE)
#https://rstudio-pubs-static.s3.amazonaws.com/329027_593046fb6d7a427da6b2c538caf601e1.html
```
```{r path, echo=FALSE, message=F, warning=F}
getwd()
setwd(getwd())
print(getwd())
```
```{r load, echo=FALSE, message=F, warning=F}
library(DESeq2)
library(ggplot2)
library(genefilter)
library(gplots)
library(pheatmap)
library("dplyr")
library(pca3d)
library(ggfortify)
library("Hmisc")
library(corrplot)
require(scatterplot3d)
library(reshape2)
library(ggpubr)
library("ggbeeswarm")
library(topGO)
#library(Rgraphviz)
library("ggpubr")
library(stringr)
library("ggVennDiagram")
library(data.table)
library(biomaRt)
#library(yarn)
#library("GSEABase")
library(dplyr)
library(RColorBrewer)
```
## Read in data for expression analysis
```{r, echo=FALSE}
meta <- read.table("meta.txt", sep="\t", header = TRUE)
mat <- read.table("matrix.txt", sep="\t", header = TRUE, row.names=1)
meta$group <- factor(paste0(meta$Genotype, meta$Treatment))
data <- DESeqDataSetFromMatrix(countData=mat, colData=meta, design = ~group )
data$group <- relevel(data$group, ref="SControl")
dds <- DESeq(data)
norm.counts <- counts(dds, normalized=TRUE)
norm.counts <- log2(norm.counts + 1)
```
Plot raw read count per sample
```{r Sample Read Count, fig.cap = "Sample read count", echo=FALSE}
sampColor <- c("S"="red", "APR"="blue")
## Add a column to the pheno type to specify the color of each sample according to its genotype
meta$color[meta$Genotype == "S"] <- sampColor["S"]
meta$color[meta$Genotype == "APR"] <- sampColor["APR"]
#pdf("Supplemental-Figure-S1.pdf")
bpt <- barplot(colSums(mat)/1000000,
#main="Total no. reads per sample (million)",
col=meta$color,
las=1, horiz=TRUE,
ylab="Samples", cex.names=0.5,
xlab="Read count (million)")
#dev.off()
```
PCA sample variance stabilised expression
```{r vst, echo=FALSE, message=FALSE, warning=FALSE}
vst <- vst(data, blind = FALSE)
```
```{r vst, echo=FALSE, message=FALSE, warning=FALSE}
pdf("Supplemental-Figure-S2.pdf")
pcaData <- plotPCA(vst, intgroup = c("Genotype","Treatment"), returnData = TRUE, ntop = 2000)
percentVar <- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(x = PC1, y = PC2, color = group)) +
labs(color='Key:') +
#geom_point(size =3, aes(fill=group )) +
geom_point(size =3) +
scale_shape_manual(values=c(21,22)) +
scale_alpha_manual(values=c("S"=0, "APR"=1)) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
theme(legend.position="top",
legend.text=element_text(size=13),
legend.title=element_text(size=15),
axis.text=element_text(size=14),
axis.title=element_text(size=16))
#+ ggtitle("PCA of all genes, no covariate adjusted")
dev.off()
```
## 1. Contrast Susceptible Inoculated versus Control
```{r S}
res <- results(dds, contrast=c("group", "SInoculated" , "SControl"))
sic <- subset(res, (padj <= 0.05 & !is.na(padj)) & abs(log2FoldChange) >= 1)
#write.table(sic, file="sus-inoc-control-p0.05-fc2.txt", sep="\t", col.names=TRUE, row.names=TRUE)
a <- rownames(sic)
```
# 2. Contrast APR Inoculated versus Control
```{r S}
res <- results(dds, contrast=c("group", "APRInoculated" , "APRControl"))
aic <- subset(res, (padj <= 0.05) & abs(log2FoldChange) >= 1)
#write.table(aic, file="apr-inoc-control-p0.05-fc2.txt", sep="\t", col.names=TRUE, row.names=TRUE)
b <- rownames(aic)
```
# 3. Contrast Inoculated APR versus S
```{r S}
res <- results(dds, contrast=c("group", "APRInoculated", "SInoculated"))
ias <- subset(res, (padj <= 0.05) & abs(log2FoldChange) >= 1)
#write.table(ias, file="inoc-apr-sus-p0.05-fc2.txt", sep="\t", col.names=TRUE, row.names=TRUE)
c <- rownames(ias)
#c <- head(rownames(ias[order(ias$padj),]), 40) # sort
```
Write annotation to text file
```{r}
g <- as.vector(rownames(ias))
mart <- useMart("plants_mart", host = "plants.ensembl.org")
hmart=useDataset("hvulgare_eg_gene", mart = mart)
ga <- getBM(attributes = c("ensembl_gene_id", "description","interpro", "interpro_description"),
filters = "ensembl_gene_id", values = g, mart = hmart)
iast <-as.data.frame(ias)
ega <- merge(x=iast, y=ga, by.x=0, by.y="ensembl_gene_id", all=TRUE)
#write.table(ega, file="ias-ipr-annotated.txt", sep="\t", col.names=TRUE, row.names=TRUE)
```
Heatmap of ais SDEGs
```{r}
anot <- data.frame(c("APR","APR","S","S"),c("Inoculated","Control","Inoculated","Control"))
rownames(anot) <- c("APR.I","APR.C","S.I","S.C")
colnames(anot) <- c("Genotype", "Treatment")
```
Supplementary figure heatmap of expression
```{r, fig.width=10, fig.height=12}
clt <- norm.counts[c,]
#clt <- t(apply(clt, 1, function(row) c(mean(row[0:15]), mean(row[16:30]), mean(row[31:46]), mean(row[47:62]))))
#clt <- clt - rowMeans(clt)
#colnames(clt) <- c("APR.I","APR.C","S.I","S.C")
pdf(file="Supplementary-top40.pdf", width = 10, height = 12)
anno <- as.data.frame(colData(dds)[, c( "Barley_line", "Genotype", "Treatment" )])
pheatmap(clt, annotation_col = anno, fontsize_col = 8, fontsize_row = 10, show_rownames=T, fontsize = 12, cluster_rows=TRUE, cluster_cols=TRUE, show_colnames=FALSE)
#annotation_col = anot,
dev.off()
```
A Venn plot contrast groups
```{r}
vde <- list(
II = a,
I = b,
III = c
)
#str(vde)
#ggVennDiagram(vde, label_alpha = 0.5)
pdf(file="venn-diagram-groups.pdf", width=14, height=14)
ggVennDiagram(vde, lwd = 0.5, lty = 1, color = "grey", label_alpha = 0, edge_size = 2, label_size = 14, set_size = 24) +
scale_fill_gradient(low = "white", high = "red") +
theme(legend.position = "none")
#scale_fill_distiller(palette = "Reds", direction = 1)
dev.off()
```
ais SDEGs
```{r APR-S-heat, fig.cap="Contrast different phenotypes", echo=FALSE }
sig <- ias[order(ias$padj),] # sort
ix = which.min(sig$padj) # most significant
#sig[ix,]
sig.r <- rownames(sig)
```
PCA for ias SDEGs
```{r vst, echo=FALSE, message=FALSE, warning=FALSE}
pdf("Supplemental-Figure-S3.pdf")
pcaData <- plotPCA(vst[sig.r,], intgroup = c("Genotype","Treatment"), returnData = TRUE, ntop = 2000)
percentVar <- round(100 * attr(pcaData, "percentVar"))
ggplot(pcaData, aes(x = PC1, y = PC2, color = group)) +
labs(color='Key:') +
# geom_point(size =3, aes(fill=group )) +
geom_point(size =3) +
scale_shape_manual(values=c(21,22)) +
scale_alpha_manual(values=c("S"=0, "APR"=1)) +
xlab(paste0("PC1: ", percentVar[1], "% variance")) +
ylab(paste0("PC2: ", percentVar[2], "% variance")) +
theme(legend.position="top",
legend.text=element_text(size=13),
legend.title=element_text(size=15),
axis.text=element_text(size=16),
axis.title=element_text(size=18))
dev.off()
```
### Reading in data for all wheat gene ids mapped to Gene Ontologies (GO)
```{r read, echo=FALSE, message=FALSE}
geneID2GO <- readMappings(file = "geneid2go.map", sep="\t")
GO2geneID <- inverseList(geneID2GO)
geneNames <- names(geneID2GO)
```
```{r, echo=FALSE, message=FALSE}
dflist <- list("II"=sic,"I"=aic,"III"=ias)
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
sampleGOup <- new("topGOdata", ontology = go, allGenes = geneListUP,
geneSel = deup,
annot = annFUN.gene2GO, gene2GO = geneID2GO)
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$weightFisher <=0.01] <- "Induced"
sampleGOdwn <- new("topGOdata", ontology = go, allGenes = geneListD,
geneSel = ded,
annot = annFUN.gene2GO, gene2GO = geneID2GO)
resultFisher <- runTest(sampleGOdwn, algorithm = "weight01", statistic = "fisher")
supress <- GenTable(sampleGOdwn, weightFisher = resultFisher, orderBy='weightFisher',
topNodes = length(resultFisher@score), numChar = 150)
supress$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)
#new <- new[new$Significant/new$Annotated>0.12,]
new$reg <- factor(new$reg, levels = c("Induced", "Supressed"),
labels = c("Induced", "Supressed"))
new$godomain <- go
new$godomain <- factor(new$godomain, levels = c(go),
labels = c(go))
new$Group <- nm
ngo <- paste(nm,"_result_",go,sep="")
write.table(new, file=paste(ngo, "-go.txt", sep=""), sep="\t", col.names=TRUE, row.names=TRUE)
new$Term = str_wrap(new$Term, width = 50)
l[[b]] <-new
}
}
```
## DE Susceptible and APR during inoculation
Collect all GO analysis
```{r }
go <- rbindlist(l,use.names=TRUE)
colnames(go)[7] <- "Regulation"
colnames(go)[9] <- "Group"
```
Molecular functions Figure 3F groups I-III
```{r, fig.height=12, fig.width=10 }
mf <- subset(go, go$godomain == "MF")
mf$Ratio <- mf$Significant/mf$Annotated
#mf <- subset(mf, (mf$weightFisher < 1e-04 | mf$Ratio > 0.9))
mf.dif <- list("GO:0030145","GO:0005385","GO:0071577","GO:0008519","GO:0000162","GO:0005544","GO:0005544","GO:0009734",
"GO:0045548","GO:0009800","GO:0055073","GO:0009695","GO:0042546","GO:2000652","GO:0043531","GO:0000162",
"GO:0010155","GO:0009607","GO:0004674","GO:0007188","GO:0010952","GO:0004474","GO:0001664","GO:0008725")
mf <- subset(mf, mf$GO.ID %in% mf.dif)
len <- length(unique(mf$Term))
mf$Term = str_wrap(mf$Term,30)
set <- colorRampPalette(brewer.pal(10, "Paired"))(len)
t <- ggplot(mf, aes(Group)) +
theme(axis.text=element_text(size=28), text=element_text(family="Calibri"),
axis.title=element_text(size=32,face="bold"), legend.text=element_text(size=28),
legend.title=element_text(size=32, face="bold"), #strip.text = element_text(size = 18),
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(data = subset(mf, Regulation == "Induced"),
aes(y = Ratio, fill = Term), stat = "identity", position="stack") +
geom_bar(data = subset(mf, Regulation == "Supressed"),
aes(y = -Ratio, fill = Term), stat = "identity", position="stack") + xlab("Group") +
xlab("Groups") + ylab("Ratio of significant DE / Annotated genes") +
guides(fill=guide_legend(nrow=len, title="GO molecular function")) + geom_hline(yintercept = 0,colour = "grey90") +
scale_fill_manual(values = set)
#facet_wrap(~ genotype ) +
t
ggsave(t, file="Figure2C-manuscript-MF-go.png", width=10, height=12)
```
Biological processes
```{r, echo=FALSE, message=FALSE, fig.width=10, fig.height=12}
bp <- subset(go, go$godomain == "BP")
bp$Ratio <- bp$Significant/bp$Annotated
#bp <- subset(bp, (bp$weightFisher < 1e-04 | bp$Ratio > 0.9))
bp.dif <- list("GO:0030145","GO:0005385","GO:0071577","GO:0008519","GO:0000162","GO:0005544","GO:0005544","GO:0009734",
"GO:0045548","GO:0009800","GO:0055073","GO:0009695","GO:0042546","GO:2000652","GO:0043531","GO:0000162",
"GO:0010155","GO:0009607","GO:0004674","GO:0007188","GO:0010952","GO:0004474","GO:0001664","GO:0008725",
"GO:0008725")
bp <- subset(bp, bp$GO.ID %in% bp.dif)
# Filter
bp <- subset(bp, bp$Term != "regulation of blood pressure")
bp <- subset(bp, bp$Term != "vasoconstriction")
bp <- subset(bp, bp$Term != "enteric nervous system development")
len <- length(unique(bp$Term))
bp$Term = str_wrap(bp$Term,30)
set <- colorRampPalette(brewer.pal(12, "Paired"))(len)
s <- ggplot(bp, aes(Group)) +
theme(axis.text=element_text(size=28), text=element_text(family="Calibri"),
axis.title=element_text(size=32,face="bold"), legend.text=element_text(size=28),
legend.title=element_text(size=32, face="bold"), #strip.text = element_text(size = 18),
strip.background=element_rect(fill="white"), panel.background = element_blank(),
panel.grid = element_line(color="grey90"), legend.position = "right", panel.border = element_rect(fill = NA)) +
geom_bar(data = subset(bp, Regulation == "Induced"),
aes(y = Ratio, fill = Term), stat = "identity", position="stack") +
geom_bar(data = subset(bp, Regulation == "Supressed"),
aes(y = -Ratio, fill = Term), stat = "identity", position="stack") + xlab("Group") +
xlab("Groups") + ylab("Ratio of significant DE / Annotated genes") +
guides(fill=guide_legend(nrow=len, title="GO biological process")) + geom_hline(yintercept = 0,colour = "grey90") +
scale_fill_manual(values = set)
s
ggsave(s, file="Figure2C-manuscript-BP-go.png", width=10, height=12)
```
Cellular component Figure 2C groups I-III
```{r cc, echo=FALSE, message=F, warning=F, fig.width=10, fig.height=12}
cc <- subset(go, go$godomain == "CC")
#cc <- subset(cc, cc$weightFisher < 1e-04)
cc$Ratio <- cc$Significant/cc$Annotated
len <- length(unique(cc$Term))
cc$Term = str_wrap(cc$Term,30)
set <- colorRampPalette(brewer.pal(12, "Paired"))(len)
r <- ggplot(cc, aes(Group)) +
theme(axis.text=element_text(size=28), text=element_text(family="Calibri"),
axis.title=element_text(size=32,face="bold"), legend.text=element_text(size=28),
legend.title=element_text(size=32, face="bold"), #strip.text = element_text(size = 18),
strip.background=element_rect(fill="white"), panel.background = element_blank(),
panel.grid = element_line(color="grey90"), legend.position = "right", panel.border = element_rect(fill = NA)) +
geom_bar(data = subset(cc, Regulation == "Induced"),
aes(y = Ratio, fill = Term), stat = "identity", position="stack") +
geom_bar(data = subset(cc, Regulation == "Supressed"),
aes(y = -Ratio, fill = Term), stat = "identity", position="stack") + xlab("Group") +
xlab("Groups") + ylab("Ratio of significant DE / Annotated genes") +
guides(fill=guide_legend(nrow=len, title="GO cellular components")) + geom_hline(yintercept = 0,colour = "grey90") +
scale_fill_manual(values = set)
r
ggsave(r, file="Figure2C-manuscript-CC-go.png", width=10, height=12)
```
## GO biomaRt
```{r}
library(biomaRt)
library(stringr)
```
```{r}
mart <- useMart("plants_mart", host = "plants.ensembl.org")
listEnsembl(mart)
```
```{r}
tail(listDatasets(useMart("plants_mart", host = "plants.ensembl.org")), 10)
```
```{r}
searchDatasets(mart = mart, pattern = "Hordeum")
```
```{r }
head(listAttributes(useMart("plants_mart", dataset="hvulgare_eg_gene", host = "plants.ensembl.org")), 40)
hmart=useDataset("hvulgare_eg_gene", mart = mart)
```
```{r}
#ensembl_gene_id
head(searchAttributes(mart=mart, pattern="go"))
```
```{r}
# For normal go analysis
#Get all gene ids and go ids
geneid2go.map <- getBM(attributes = c("ensembl_gene_id","go_id"),
mart = hmart)
#write.table(geneid2go.map,file = "geneid2go.map", sep = "\t")
length(geneid2go.map[["go_id"]])
head(geneid2go.map)
```
```{r, echo=FALSE}
# For normal go analysis
#Remove blank entries
geneid2go.map <- geneid2go.map[geneid2go.map$go_id != '',]
# convert from table format to list format
geneID2GO <- by(geneid2go.map$go_id,
geneid2go.map$ensembl_gene_id,
function(x) as.character(x))
```
```{r, echo=FALSE}
# For normal go analysis
GO2geneID <- inverseList(geneID2GO)
geneNames <- names(geneID2GO)
#geneListUP <- factor(as.integer(geneNames %in% gup))
#names(geneListUP) <- geneNames
#geneListD <- factor(as.integer(geneNames %in% gdown))
#names(geneListD) <- geneNames
#geneList <- factor(as.integer(geneNames %in% siggene))
#names(geneList) <- geneNames
```
## GO Slim
```{r}
fl <- system.file("extdata", "goslim_plant.obo", package="GSEABase")
slim <- getOBOCollection(fl)
```
```{r}
#dflist <- list("sic"=sic,"aic"=aic,"ias"=ias,"cas"=cas)
glist <- c("MF","BP", "CC")
cnt=0
b=0
ll <- list()
for(e in dflist){
cnt=cnt+1
up <- rownames(subset(e, e$log2FoldChange >= 1))
dn <- rownames(subset(e, e$log2FoldChange <= -1))
nm<- names(dflist[cnt])
#reg <- unique(e$reg)
#gl <-rownames(e)
tmp.up <- getBM(attributes = c("ensembl_gene_id","go_id"),
filters="ensembl_gene_id", values=up, mart = hmart)
tmp.dn <- getBM(attributes = c("ensembl_gene_id","go_id"),
filters="ensembl_gene_id", values=dn, mart = hmart)
#Remove blank entries
#gup.go <- gup.go[gup.go$go_id != '',]
tmpgo.up <- tmp.up$go_id
tmpgo.dn <- tmp.dn$go_id
myCollection.up <- GOCollection(tmpgo.up)
myCollection.dn <- GOCollection(tmpgo.dn)
for(f in glist){
b=b+1
tmp.slim.up <- goSlim(myCollection.up, slim, f)
tmp.slim.dn <- goSlim(myCollection.dn, slim, f)
tmp.slim.up <- subset(tmp.slim.up, Count > 0)
tmp.slim.dn <- subset(tmp.slim.dn, Count > 0)
tmp.slim.up$regulation <- "induced"
tmp.slim.dn$regulation <- "supressed"
tmp.slim <- rbind(tmp.slim.up, tmp.slim.dn)
tmp.slim$genotype <- nm
tmp.slim$godomain <- f
ll[[b]] <-tmp.slim
}
}
```
### Go Slims All MF BP CC
```{r, echo=FALSE, fig.width=14, fig.height=14}
ctrl <-rbind(ll[[1]], ll[[2]], ll[[3]], ll[[4]], ll[[5]], ll[[6]])
ctrl$godomain <- factor(ctrl$godomain, levels = c("BP","MF","CC"),
labels = c("Biological process","Molecular function", "Cellular component"))
ctrl$genotype <- factor(ctrl$genotype, levels = c("sic","aic"),
labels = c("Susceptible","Resistant"))
ctrl$regulation <- factor(ctrl$regulation, levels = c("induced","supressed"))
ctrl <- subset(ctrl, Term != "molecular_function")
ctrl <- subset(ctrl, Term != "biological_process")
#ggplot(ctrl, aes(x = Count, y = Term, label="Gene Ontology")) +
# geom_bar(stat="identity")
newplot <-ggplot(data=ctrl, aes(y=Count, x=Term, color=regulation, fill=regulation)) +
geom_bar(stat="identity", position="dodge") +
geom_text(aes(label=as.character(Count)), size = 1.8, hjust=0, angle=0, color="black") +
theme(legend.position="right", axis.text.x = element_text(angle = 0, hjust = 0),
axis.text.y = element_text(angle = 0, size=8),
axis.text=element_text(size=6)) +
xlab("Gene ontology name") + ylab("GO slim term count") +
scale_x_discrete(labels = function(x) stringr::str_wrap(x, width = 30)) +
facet_wrap(ctrl$godomain~ctrl$genotype, labeller = label_wrap_gen(multi_line=FALSE),
scales="fixed", nrow=1) + coord_flip()
newplot
```
```{r,echo=FALSE}
pdf(file="goslim-all.pdf", width=14, height=12)
newplot
dev.off()
```
# Mart and Paths
```{r }
listAttributes(useMart("plants_mart", dataset="hvulgare_eg_gene", host = "plants.ensembl.org"))
```
```{r}
unipathway.map <- getBM(attributes = c("ensembl_gene_id","unipathway"),
mart = hmart)
kegg.map <- getBM(attributes = c("ensembl_gene_id","kegg_enzyme"),
mart = hmart)
sic.de.up <- rownames(subset(sic, sic$log2FoldChange >= 1))
sic.de.dn <- rownames(subset(sic, sic$log2FoldChange <= -1))
de.kegg.up <- getBM(attributes = c("ensembl_gene_id","kegg_enzyme"),
filters = "ensembl_gene_id", values = sic.de.up, mart = hmart)
de.kegg.dn <- getBM(attributes = c("ensembl_gene_id","kegg_enzyme"),
filters = "ensembl_gene_id", values = sic.de.dn, mart = hmart)
de.kegg.up$color <- "red"
de.kegg.dn$color <- "blue"
de.kegg <- rbind(de.kegg.up, de.kegg.dn)
#Remove blank entries
de.kegg <- de.kegg[de.kegg$kegg_enzyme != '',]
tmp <- de.kegg[,c(2, 3)]
write.table(tmp, file="susceptible-kegg.txt", sep="\t")
#head(searchAttributes(mart=mart, pattern="kegg"))
```
```{r}
aic.de.up <- rownames(subset(aic, sic$log2FoldChange >= 1))
aic.de.dn <- rownames(subset(aic, sic$log2FoldChange <= -1))
de.kegg.up <- getBM(attributes = c("ensembl_gene_id","kegg_enzyme"),
filters = "ensembl_gene_id", values = aic.de.up, mart = hmart)
de.kegg.dn <- getBM(attributes = c("ensembl_gene_id","kegg_enzyme"),
filters = "ensembl_gene_id", values = aic.de.dn, mart = hmart)
de.kegg.up$color <- "red"
de.kegg.dn$color <- "blue"
de.kegg <- rbind(de.kegg.up, de.kegg.dn)
#Remove blank entries
de.kegg <- de.kegg[de.kegg$kegg_enzyme != '',]
tmp <- de.kegg[,c(2, 3)]
write.table(tmp, file="adultplant-resistance-kegg.txt", sep="\t")
#head(searchAttributes(mart=mart, pattern="kegg"))
```
```{r}
library(pathfindR)
get_pin_file(source = "BioGRID", org = "Arabidopsis_thaliana_Columbia", path2pin="/Users/266063e/Work/r_notebook", release = "4.3.196")
gsets_list <- get_gene_sets_list(source = "KEGG",
org_code = "ath")
```
```{r}
ath_kegg_genes <- gsets_list$gene_sets
ath_kegg_descriptions <- gsets_list$descriptions
## Save both as RDS files for later use
saveRDS(ath_kegg_genes, "ath_kegg_genes.RDS")
saveRDS(ath_kegg_descriptions, "ath_kegg_descriptions.RDS")
```
```{r}
## Downloading the STRING PIN file to tempdir
url <- "https://stringdb-static.org/download/protein.links.v11.0/3702.protein.links.v11.0.txt.gz"
path2file <- file.path("/Users/266063e/Work", "STRING.txt.gz")
download.file(url, path2file)
## read STRING pin file
ath_string_df <- read.table(path2file, header = TRUE)
#head(gma_string_df)
## filter using combined_score cut-off value of 800
ath_string_df <- ath_string_df[ath_string_df$combined_score >= 800, ]
## fix ids
#ath_string_pin <- data.frame(Interactor_A = sub("^3702\\.", "", ath_string_df$protein1),
# Interactor_B = sub("^3702\\.", "", ath_string_df$protein2))
ath_string_pin <- data.frame(Interactor_A = ath_string_df$protein1,
Interactor_B = ath_string_df$protein2)
tail(ath_string_pin, 2)
```
```{r}
mart <- useMart("plants_mart", host = "plants.ensembl.org")
#gma_ensembl <- useMart(mart, dataset = "gmax_gene_ensembl")
searchDatasets(mart, pattern = "halian")
ath_ensembl <- useMart("plants_mart", dataset="athaliana_eg_gene", host = "plants.ensembl.org")
searchAttributes(mart=ath_ensembl, pattern="gene")
searchAttributes(mart=ath_ensembl)
converted <- getBM(attributes = c("string", "tair_symbol"),
filters = "string",
values = unique(unlist(ath_string_pin)),
mart = ath_ensembl)
ath_string_pin$Interactor_B <- converted$tair_symbol[match(ath_string_pin$Interactor_B, converted$string)]
ath_string_pin <- ath_string_pin[!is.na(ath_string_pin$Interactor_A) & !is.na(ath_string_pin$Interactor_B), ]
ath_string_pin <- ath_string_pin[ath_string_pin$Interactor_A != "" & ath_string_pin$Interactor_B != "", ]
head(ath_string_pin, 2)
```
```{r}
# remove self interactions
self_intr_cond <- ath_string_pin$Interactor_A == ath_string_pin$Interactor_B
ath_string_pin <- ath_string_pin[!self_intr_cond, ]
# remove duplicated interactions (including symmetric ones)
ath_string_pin <- unique(t(apply(ath_string_pin, 1, sort))) # this will return a matrix object
ath_string_pin <- data.frame(A = ath_string_pin[, 1],
pp = "pp",
B = ath_string_pin[, 2])
```
```{r}
path2SIF <- file.path(tempdir(), "athalianaPIN.sif")
write.table(ath_string_pin,
file = path2SIF,
col.names = FALSE,
row.names = FALSE,
sep = "\t",
quote = FALSE)
path2SIF <- normalizePath(path2SIF)
```
```{r}
##Expression data
#athaliana_eg_homolog_ensembl_gene
sus_hor <- getBM(attributes = c("ensembl_gene_id", "athaliana_eg_homolog_ensembl_gene"),
filters = "ensembl_gene_id",
values = unique(rownames(sic)),
mart = hmart)
sus_hor <- sus_hor[sus_hor$athaliana_eg_homolog_ensembl_gene != '',]
sus_hor$logFC <- sic$log2FoldChange[match(sus_hor$ensembl_gene_id, rownames(sic))]
sus_hor$FDR <- sic$padj[match(sus_hor$ensembl_gene_id, rownames(sic))]
sus_ath <- getBM(attributes = c("ensembl_gene_id", "tair_symbol"),
filters = "ensembl_gene_id",
values = unique(sus_hor$athaliana_eg_homolog_ensembl_gene),
mart = ath_ensembl)
sus_ath <- sus_ath[sus_ath$tair_symbol != '',]
colnames(sus_ath)[2] <- "Gene_Symbol"
sus_ath$HOR <- sus_hor$ensembl_gene_id[match(sus_ath$ensembl_gene_id, sus_hor$athaliana_eg_homolog_ensembl_gene)]
sus_ath$logFC <- sus_hor$logFC[match(sus_ath$ensembl_gene_id, sus_hor$athaliana_eg_homolog_ensembl_gene)]
sus_ath$FDR <- sus_hor$FDR[match(sus_ath$ensembl_gene_id, sus_hor$athaliana_eg_homolog_ensembl_gene)]
sus_ath1 <- sus_ath[,c(2,4,5)]
#colnames(sus_ath1)[1] <- "Gene_Symbol"
```
```{r, include=FALSE, echo=FALSE, message=FALSE, warning=FALSE}
#pdf(file="susceptible-rpath.pdf")
sus_output <- run_pathfindR(input = sus_ath1,
convert2alias = FALSE,
gene_sets = "Custom",
custom_genes = ath_kegg_genes,
custom_descriptions = ath_kegg_descriptions,
pin_name_path = path2SIF)
#dev.off()
```
```{r}
#knitr::kable(sus_output)
write.table(sus_output, file="susceptible-rpath.txt", sep="\t")
```
```{r}
sus_clustered <- cluster_enriched_terms(sus_output)
sus_clustered_chart <- enrichment_chart(sus_clustered, plot_by_cluster = TRUE)
pdf(file="susceptible-rpath-clus.pdf")
sus_clustered_chart
dev.off()
```
```{r}
sus_clusteredf <- cluster_enriched_terms(sus_output, method="fuzzy")
sus_clustered_fchart <- enrichment_chart(sus_clustered, plot_by_cluster = TRUE)
#pdf(file="susceptible-rpath-clusf.pdf")
sus_clustered_fchart
#dev.off()
```
```{r, fig.width=18, fig.height=10}
pdf(file="sus-term-gene-graph.pdf", width = 22, height = 10)
term_gene_graph(result_df = sus_output, use_description = TRUE)
dev.off()
```
Working on this chunk - need to change for 4 lines
```{r}
#https://www.biostars.org/p/322415/#google_vignette
m <- as.matrix(t(apply(log2(norm.counts +1), 1, function(row) c(mean(row[0:16]), mean(row[17:30]), mean(row[31:46]), mean(row[47:62])))), rownames.force = TRUE)
colnames(m) <- c("APR.Inoc","APR.Ctrl","S.Inoc","S.Ctrl")
sus_assay <- as.matrix(m[ rownames(m) %in% sus_hor$ensembl_gene_id, ], rownames.force = TRUE)
tmp <- as.data.frame(cbind(sus_assay, sus_ath[, "Gene_Symbol"][match(rownames(sus_assay), sus_ath$HOR)]))
colnames(tmp)[5] <- "Gene_Symbol"
df <- subset(tmp, !is.na(tmp[,"Gene_Symbol"]))
df <- df[!duplicated(df$Gene_Symbol),]
#write.table(df, file="df.txt", sep="\t", row.names = TRUE, col.names = TRUE,)
rownames(df) <- df[,5]
df <- df[,1:4]
df <- data.frame(df, stringsAsFactors=FALSE)
df <- mutate_all(df, function(x) as.numeric(as.character(x)))
sus_matrix <- data.matrix(df, rownames.force = TRUE)
#write.table(sus_matrix, file="sus_matrix", sep="\t", row.names = TRUE, col.names = TRUE,)
#test <- data.matrix(read.table(file="sus_matrix", header= TRUE, sep="\t", row.names = 1, stringsAsFactors = FALSE), rownames.force = TRUE)
#cases <- c("APR.Inoc", "APR.Ctrl", "S.Inoc", "S.Ctrl")
score_matrix <- score_terms(enrichment_table = sus_clustered[sus_clustered$Status == "Representative", ],
exp_mat = sus_matrix,
cases = NULL,
use_description = TRUE, # default FALSE
label_samples = TRUE) # default = TRUE
```
```{r}
pdf(file="susceptible-score-matrix.pdf", width = 10, height = 10)
score_matrix <- score_terms(enrichment_table = sus_clustered[sus_clustered$Status == "Representative", ],
exp_mat = sus_matrix,
cases = NULL,
use_description = TRUE, # default FALSE
label_samples = TRUE) # default = TRUE
dev.off()
```
```{r}
pdf(file="susceptible-all-score-matrix.pdf", width = 10, height = 10)
score_matrix <- score_terms(enrichment_table = sus_clustered,
exp_mat = sus_matrix,
cases = NULL,
use_description = TRUE, # default FALSE
label_samples = TRUE) # default = TRUE
dev.off()