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AGR-RNA-Analysis-2.Rmd
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---
title: "Wheat AGR RNAseq cluster analysis"
author: "Paula Moolhuijzen"
date: '`r format(Sys.time(), "%d %B, %Y")`'
output:
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r path, echo=FALSE, message=F, warning=F}
setwd(getwd())
library(plotrix)
library("ggplot2")
library(ggrepel)
library(tidyverse)
library(ggparliament)
require(lattice)
library(GGally)
library(RColorBrewer)
library(data.table)
```
```{r,}
annot <- read.table("annotation.txt", sep="\t", header = FALSE, stringsAsFactors=FALSE)
len <- length(annot$V1)
color <- colorRampPalette(brewer.pal(12, "Paired"))(len)
annot$color <- color
f <- list.files(path="clusters")
for(x in f){
c <- paste("clusters/", x,"", sep="")
tbl <- read.table(c, sep="\t", header = FALSE, stringsAsFactors=FALSE)
cnt <- tbl$V2
pdb <- tbl$V3
df <- data.frame(value=cnt, group=pdb)
df$color <- annot$color[match(df$group, annot$V1)]
sum = sum(df$value)
df$percent = round(((df$value/sum)*100), digits=0)
ru_semicircle <- parliament_data(election_data = df,
type = "semicircle", # Parliament type
parl_rows = 10, # Number of rows of the parliament
party_seats = df$value) # Seats per party
eplot<- ggplot(ru_semicircle, aes(x = x, y = y, colour = group)) +
geom_parliament_seats() +
geom_parliament_bar(colour = color, party = group, label = FALSE) +
theme_ggparliament() +
labs(title = x) +
scale_colour_manual(values = ru_semicircle$color,
limits = ru_semicircle$group) + theme(legend.position = "none")
#pdf(file=paste(x,"no_legend.pdf",sep=""), width=12, height = 5)
print(eplot)
#dev.off()
}
```
Profile plots
```{r,}
zscore <- read.table("sdeg-cluster.txt_processed.tsv", sep="\t", header = TRUE, stringsAsFactors=FALSE)
f <- list.files(path="clusterlist")
cnt=0
for(x in f){
cnt=cnt+1
c <- paste("clusterlist/", x,"", sep="")
l <- readLines(c)
len = length(l)
#l <- readLines("clusterlist/1.cluster.list") # test
zscore.sub <- subset(zscore, zscore$Genes %in% l )
tit <- paste("Cluster ",cnt," (",len," genes)",sep="")
eplot <- ggparcoord(data = zscore.sub, scale="globalminmax", columns = 2:9, title = tit,
groupColumn = 2) +
theme(axis.text.x = element_text(angle = 90, hjust = 0),
axis.text.y = element_text(angle = 0),
axis.text=element_text(size=16),
axis.title=element_text(size=16)) +
xlab("Groups") + ylab("Z-score")
# pdf(file=paste(x,".pdf",sep=""), width=6, height = 5)
print(eplot)
# dev.off()
}
```
Profile plots try two
```{r,}
zscore <- read.table("sdeg-cluster.txt_processed.tsv", sep="\t", header = TRUE, stringsAsFactors=FALSE)
f <- list.files(path="clusterlist")
cnt=0
ls <- list()
for(x in f){
cnt=cnt+1
c <- paste("clusterlist/", x,"", sep="")
l <- readLines(c)
len = length(l)
zscore.sub <- subset(zscore, zscore$Genes %in% l )
cl <- paste("clust",x, sep="")
zscore.sub$Cluster <- paste("Clust",cnt, sep="")
ls[[cnt]] <- zscore.sub
}
znew <- rbindlist(ls,use.names=TRUE)
h <- c("Control3","Control8","Inoculated3","Inoculated8")
mag <- znew[,c(1:5,10)]
mag$Cultivar <- "Magenta"
colnames(mag)[2:5]<- h
scou <- znew[,c(1,6:10)]
scou$Cultivar <- "Scout"
colnames(scou)[2:5]<- h
df <- rbindlist(list(mag,scou))
```
```{r, fig.width=14, fig.height=8}
df$Group <- paste(df$Cluster,":", df$Cultivar, sep="")
eplot <- ggparcoord(data = df, scale="globalminmax", scaleSummary = "mean",
columns = 2:5, groupColumn = 8,
showPoints = FALSE,
boxplot = TRUE,
mapping=aes(color=as.factor(Cluster))) +
theme(axis.text.x = element_text(angle = 90, hjust = 0),
axis.text.y = element_text(angle = 0),
axis.text=element_text(size=16),
axis.title=element_text(size=16), legend.position = "none") +
xlab("Groups") + ylab("Z-score") +
facet_wrap(~Group, ncol = 8) +
theme(strip.text.x = element_text(size = 14))
#pdf(file="Cluster-facet.pdf",width=16, height = 8)
print(eplot)
#dev.off()
```
add mean point
```{r, fig.width=14, fig.height=8}
h <- c("C.3","C.8","I.3","I.8")
colnames(df)[2:5]<- h
grp.lab <- c("MR", "S","MR", "S","MR", "S","MR", "S",
"MR", "S","MR", "S","MR", "S","MR", "S")
names(grp.lab) <- c("Clust1:Magenta", "Clust1:Scout","Clust2:Magenta", "Clust2:Scout","Clust3:Magenta", "Clust3:Scout","Clust4:Magenta", "Clust4:Scout",
"Clust5:Magenta", "Clust5:Scout","Clust6:Magenta", "Clust6:Scout","Clust7:Magenta", "Clust7:Scout","Clust8:Magenta", "Clust8:Scout")
c3_means <- df %>%
dplyr::select(Group,C.3,C.8,I.3,I.8) %>%
group_by(Group) %>%
summarize_all(mean)
df.means <- gather(c3_means, treatment, value, C.3:I.8, factor_key=TRUE)
eplot <- ggparcoord(data = df, scale="globalminmax", scaleSummary = "mean",
columns = 2:5, groupColumn = 8,
showPoints = FALSE,
boxplot = FALSE,
mapping=aes(color=as.factor(Cluster))) +
theme(axis.text.x = element_text(angle = 60, vjust = 0.5, hjust=0.5),
axis.text.y = element_text(angle = 0),
axis.text=element_text(size=16),
axis.title=element_text(size=16), legend.position = "bottom", legend.key.size = unit(2, 'cm'),
legend.title = element_text(size = 14),
legend.text = element_text(size = 12)) +
xlab("Groups") + ylab("Z-score") +
geom_point(data = df.means, aes(x = treatment,y=value),
size = 2,
inherit.aes = FALSE) +
facet_wrap(~Group, ncol = 8, labeller = labeller(Group = grp.lab)) +
theme(strip.text.x = element_text(size = 14)) +
guides(colour = guide_legend(override.aes = list(size=10),nrow = 1)) +
scale_colour_discrete("Clusters")
#pdf(file="Cluster-facet.pdf",width=16, height = 8)
print(eplot)
#dev.off()
```
trying to clear bg and position legend
```{r, fig.width=14, fig.height=8}
eplot <- ggparcoord(data = df, scale="globalminmax", scaleSummary = "mean",
columns = 2:5, groupColumn = 8,
showPoints = FALSE,
boxplot = FALSE,
mapping=aes(color=as.factor(Cluster))) +
theme_bw() +
theme(axis.text.x = element_text(angle = 60, vjust = 0.5, hjust=0.5),
axis.text.y = element_text(angle = 0),
axis.text=element_text(size=16),
axis.title=element_text(size=16), legend.position = "bottom", legend.key.size = unit(2, 'cm'),
legend.title = element_text(size = 16),
legend.text = element_text(size = 14)) +
xlab("Groups") + ylab("Z-score") +
geom_point(data = df.means, aes(x = treatment,y=value),
size = 2,
inherit.aes = FALSE) +
facet_wrap(~Group, ncol = 8, labeller = labeller(Group = grp.lab)) +
theme(strip.text.x = element_text(size = 14)) +
guides(colour = guide_legend(override.aes = list(size=12),nrow = 1)) +
scale_colour_discrete("Clusters")
#pdf(file="Cluster-facet.pdf",width=16, height = 8)
print(eplot)
#dev.off()
```
Merge mean and black plot
```{r, fig.width=14, fig.height=8}
eplot <- ggparcoord(data = df, scale="globalminmax", scaleSummary = "mean",
columns = 2:5, groupColumn = 8,
showPoints = FALSE,
boxplot = FALSE,
mapping=aes(color="black")) +
theme_bw() +
theme(axis.text.x = element_text(angle = 60, vjust = 0.5, hjust=0.5),
axis.text.y = element_text(angle = 0),
axis.text=element_text(size=16),
axis.title=element_text(size=16), legend.position = "none") +
xlab("Groups") + ylab("Z-score") +
geom_point(data = df.means, aes(x = treatment,y=value),
size = 2,
inherit.aes = FALSE) +
facet_wrap(~Group, ncol = 8, labeller = labeller(Group = grp.lab)) +
theme(strip.text.x = element_text(size = 14))
#+ scale_colour_discrete("Clusters")
#pdf(file="Cluster-facet.pdf",width=16, height = 8)
print(eplot)
#dev.off()
```
Table plots function
```{r,fig.height=12,fig.width=8}
p <- read.table("table-summary-bar.txt", sep="\t", header = TRUE, stringsAsFactors=FALSE)
p$Group[p$Cultivar == "Scout" & p$dpi == "dpi3"] <- "I"
p$Group[p$Cultivar == "Scout" & p$dpi == "dpi8"] <- "III"
p$Group[p$Cultivar == "Magenta" & p$dpi == "dpi3"] <- "VI"
p$Group[p$Cultivar == "Magenta" & p$dpi == "dpi8"] <- "VII"
set <- colorRampPalette(brewer.pal(12, "Paired"))(42)
#pdf(file=paste("Group.pdf",sep=""), width=10, height = 18)
gp <- ggplot(p, aes(Group)) +
theme(axis.text=element_text(size=18),
axis.title=element_text(size=24, face="bold"), text=element_text(family="Calibri"),
legend.text=element_text(size=18), legend.title=element_text(size=18, face="bold"),
strip.text = element_text(size = 18, face = "bold"),
strip.background=element_rect(fill="white"), panel.background = element_blank(),
panel.grid = element_line(color="grey90"), legend.position = "right", panel.border = element_rect(fill = NA)) +
geom_bar(data = subset(p, Regulation == "Up"),
aes(y = SDEG, fill = Function), stat = "identity", position="stack") +
geom_bar(data = subset(p, Regulation == "Down"),
aes(y = -SDEG, fill = Function), stat = "identity", position="stack") +
xlab("Group") + ylab("Number of Significant DE genes") +
guides(fill=guide_legend(nrow=42, title="Number of SDEGs in defence (terms)")) +
geom_hline(yintercept = 0,colour = "grey90") +
scale_fill_manual(values = set)
gp
ggsave(gp, file="Group-terms.png", width=10, height=12)
```
Table plots Category
```{r,fig.height=10,fig.width=8}
p <- read.table("table-summary-bar.txt", sep="\t", header = TRUE, stringsAsFactors=FALSE)
p$Group[p$Cultivar == "Scout" & p$dpi == "dpi3"] <- "I"
p$Group[p$Cultivar == "Scout" & p$dpi == "dpi8"] <- "III"
p$Group[p$Cultivar == "Magenta" & p$dpi == "dpi3"] <- "VI"
p$Group[p$Cultivar == "Magenta" & p$dpi == "dpi8"] <- "VII"
len <- length(unique(p$Category))
set <- colorRampPalette(brewer.pal(12, "Paired"))(len)
#pdf(file=paste("Group.pdf",sep=""), width=10, height = 18)
gp <- ggplot(p, aes(Group)) +
theme(axis.text=element_text(size=18),
axis.title=element_text(size=24, face="bold"), text=element_text(family="Calibri"),
legend.text=element_text(size=18), legend.title=element_text(size=18, face="bold"),
strip.text = element_text(size = 18, 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(data = subset(p, Regulation == "Up"),
aes(y = SDEG, fill = Category), stat = "identity", position="stack") +
geom_bar(data = subset(p, Regulation == "Down"),
aes(y = -SDEG, fill = Category), stat = "identity", position="stack") +
xlab("Group") + ylab("Number of Significant DE genes") +
guides(fill=guide_legend(nrow=len, title="Number of SDEGs in defence (category)")) +
geom_hline(yintercept = 0,colour = "grey90") +
scale_fill_manual(values = set)
gp
ggsave(gp, file="Group-category.png", width=10, height=12)
```
effort 24th August 2022
```{r, echo=FALSE, warning=FALSE}
zscore <- read.table("Results_04_Aug_22_1/Processed_Data/sdeg-cluster.txt_processed.tsv", sep="\t", header = TRUE, stringsAsFactors=FALSE)
f <- list.files(path="Results_04_Aug_22_1/clusterlist")
cnt=0
for(x in f){
cnt=cnt+1
c <- paste("Results_04_Aug_22_1/clusterlist/", x,"", sep="")
l <- readLines(c)
len = length(l)
#l <- readLines("Results_04_Aug_22_1/clusterlist/1.cluster.list") # test
zscore.sub <- subset(zscore, zscore$Genes %in% l )
tit <- paste("Cluster ",cnt," (",len," genes)",sep="")
test <- zscore.sub[2:9]
df_means <- reshape2::melt(summarise_all(test, mean))
#colnames(df_means)[1]<- "group"
eplot <- ggparcoord(data = zscore.sub, scale="globalminmax", columns = 2:9, title = tit,
#groupColumn = 2,
#scaleSummary = "mean",
showPoints = FALSE,
boxplot = FALSE) +
theme(axis.text.x = element_text(angle = 90, hjust = 0),
axis.text.y = element_text(angle = 0),
axis.text=element_text(size=18),
axis.title=element_text(size=18),
legend.position = "none",
axis.title.x=element_blank(),
plot.title = element_text(size=18)) +
ylab("Z-score") +
geom_point(data = df_means, aes(x = variable,y=value),
size = 2,
color="red",
inherit.aes = FALSE)
pdf(file=paste(x,".pdf",sep=""), width=6, height = 4)
print(eplot)
dev.off()
}
```
```{r}
sessionInfo()
```