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Fig.S27.EPICON.heatmap.2019.04.10.Rmd
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Fig.S27.EPICON.heatmap.2019.04.10.Rmd
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---
title: "Untitled"
author: "Cheng Gao"
date: "4/10/2019"
output: html_document
---
```{r, message=FALSE, warning=FALSE, fig.height = 8, fig.width = 5, fig.align = "center"}
setwd("/Users/chengg/Google Drive/EPICON/Mycobiome/Fungal ITS/statistic/Total.fungi")
library(reshape2)
library(ggplot2)
library(pheatmap)
library(randomForest)
library(tidyverse)
library(reshape2)
library(splitstackshape)
rm(list = ls())
load("EPICON.data.preparation.RC.bNTI.ted.2019.04.19.Rdata")
row.names(env)<-env$aa
tc_map<-env
otu_table<-data.frame(t(fung.raw))##Read in the raw data###ID<-read.csv("epicon.ID.2018.08.27.csv", head=T, row.names = 1)
ID1<-ID[,c("Guild","Phylum","Class","Order","Family", "Genus","aa")]
ID1[ID1=="_"]<-0
#ID1[ID1==" "]<-"."
ID$newID<-paste(ID1$Guild, ID1$Phylum, ID1$Class, ID1$Order, ID1$Family, ID1$Genus, ID1$aa, sep="..__")
row.names(otu_table)<-ID$newID
otu_table<-otu_table[ID$Fungi=="Fungi",]
Totutable<-data.frame(t(otu_table))
Totutable.pseudo.po<-Totutable[tc_map$TP < 7 & tc_map$TP >0 & tc_map$Treatment1 == "Control", ]
Totutable.pseudo.pr<-Totutable[tc_map$TP < 3 & tc_map$TP >0 & tc_map$Treatment1 == "Control", ]
otu_table<-data.frame(t(rbind(Totutable, Totutable.pseudo.po, Totutable.pseudo.pr)))
tc_map.po<-tc_map[tc_map$TP < 7 & tc_map$TP >0 & tc_map$Treatment1 == "Control", ]
tc_map.po$Treatment1 <- "Post_flowering_drought"
tc_map.pr<-tc_map[tc_map$TP < 3 & tc_map$TP >0 & tc_map$Treatment1 == "Control", ]
tc_map.pr$Treatment1 <- "Pre_flowering_drought"
tc_map<-rbind(tc_map, tc_map.po, tc_map.pr)
fRF<-function (ha, tr) {
sub_map = droplevels(tc_map[ tc_map$Habitat== ha & tc_map$Treatment1 == tr ,]) #
idx = rownames(sub_map) %in% colnames(otu_table)
sub_map = sub_map[idx,]
sub_otu = otu_table[, rownames(sub_map)]
set.seed(315)
rf = randomForest(t(sub_otu), sub_map$TP, importance=TRUE, proximity=TRUE, ntree = 1000)
print(rf)
set.seed(315)
result = rfcv(t(sub_otu), sub_map$TP, cv.fold=10) #Random Forest Cross Validation
xx<-data.frame(result$error.cv)
xx<-data.frame(row.names(xx),xx)
names(xx)<-c("name", "value")
nf<-as.numeric(as.character(xx$name[which.min(xx$value)])) # to pick out the no. of OTUs resulting to the best model
print(nf)
imp= as.data.frame(rf$importance)
imp = imp[order(imp[,1],decreasing = T),]
write.table(imp,file = "importance_class.txt",quote = F,sep = '\t', row.names = T, col.names = T)
imp.all = read.table("importance_class.txt", header=T, row.names= 1, sep="\t")
#head(imp)
imp = head(imp.all, n= nf)
imp=imp[order(1:nf,decreasing = T),]
imp$ID<-row.names(imp)
imp$ID1<-row.names(imp)
imp.w<-cSplit(imp, "ID", "..__")
colnames(imp.w)<-c("X.IncMSE","IncNodePurity","ID1" ,"guild" ,"phylum", "class", "order", "family", "genus","OTU")
row.names(imp.w)<-imp.w$ID1
#row.names(sub_otu)
sub_abu = sub_otu[rownames(imp.w),]
row.names(sub_abu)<-paste(imp.w$guild,imp.w$phylum, imp.w$class, imp.w$order, imp.w$family, imp.w$genus,imp.w$OTU, sep="..__")
sampFile = as.data.frame(sub_map$TP,row.names = row.names(sub_map))
colnames(sampFile)[1] = "group"
mat_t = t(sub_abu)
mat_t2 = merge(sampFile, mat_t, by="row.names")
mat_t2 = mat_t2[,-1]
mat_mean = aggregate(mat_t2[,-1], by=mat_t2[1], FUN=mean) # mean
otu_norm_group = do.call(rbind, mat_mean)[-1,]
colnames(otu_norm_group) = mat_mean$group
all.guildlevles<-c("Arbuscular.mycorrhizal","Endophyte","Plant.pathogen","Plant.pathogen_Yeast","Saprotroph","Saprotroph_Yeast", "Unidentified...others" )
d2<-all.guildlevles[all.guildlevles%in%imp.w$guild==FALSE]
addLevel <- function(x, newlevel=NULL) {
if(is.factor(x)) {
if (is.na(match(newlevel, levels(x))))
return(factor(x, levels=c(levels(x), newlevel)))
}
return(x)
}
imp.w$guild<-addLevel(imp.w$guild, d2)
imp.w$guild<-factor(imp.w$guild,
levels = c("Arbuscular.mycorrhizal","Endophyte","Plant.pathogen","Plant.pathogen_Yeast","Saprotroph","Saprotroph_Yeast", "Unidentified...others" ),
labels = c("Arbuscular mycorrhiza", "Endophyte", "Plant pathogen", "Plant pathogen (Yeast)", "Saprotroph", "Saprotroph (Yeast)", "Others"))
mat_row <- data.frame(row.names = row.names (otu_norm_group),Guild = imp.w$guild)
#mat_row$Guild
#levels(imp.w$guild)
#levels(imp.w$guild)
mycolors <- c("#0000FF","#FF3399","#FFCC33","#ff00ff","#00ff00", "deepskyblue", "bisque")
names(mycolors) <- levels(mat_row$Guild)
mycolors <- list(Guild = mycolors)
pheatmap(otu_norm_group,
scale="row",
annotation_row = mat_row,
annotation_colors = mycolors,
show_rownames = F,
cluster_cols = F,
legend = F,
cluster_rows = T)
}
fRF("Leaf","Control")
fRF("Root","Control")
#fRF("Rhizosphere","Control")
fRF("Soil","Control")
```
```{r, message=FALSE, warning=FALSE, fig.height = 8, fig.width = 5, fig.align = "center"}
fRF<-function (ha, tr) {
ha<-"Rhizosphere"
tr<-"Control"
sub_map = droplevels(tc_map[ tc_map$Habitat== ha & tc_map$Treatment1 == tr ,]) #
idx = rownames(sub_map) %in% colnames(otu_table)
sub_map = sub_map[idx,]
sub_otu = otu_table[, rownames(sub_map)]
set.seed(315)
rf = randomForest(t(sub_otu), sub_map$TP, importance=TRUE, proximity=TRUE, ntree = 1000)
print(rf)
set.seed(315)
result = rfcv(t(sub_otu), sub_map$TP, cv.fold=10) #Random Forest Cross Validation
xx<-data.frame(result$error.cv)
xx<-data.frame(row.names(xx),xx)
names(xx)<-c("name", "value")
nf<-as.numeric(as.character(xx$name[which.min(xx$value)])) # to pick out the no. of OTUs resulting to the best model
print(nf)
imp= as.data.frame(rf$importance)
imp = imp[order(imp[,1],decreasing = T),]
write.table(imp,file = "importance_class.txt",quote = F,sep = '\t', row.names = T, col.names = T)
imp.all = read.table("importance_class.txt", header=T, row.names= 1, sep="\t")
#head(imp)
imp = head(imp.all, n= nf)
imp=imp[order(1:nf,decreasing = T),]
imp$ID<-row.names(imp)
imp$ID1<-row.names(imp)
imp.w<-cSplit(imp, "ID", "..__")
colnames(imp.w)<-c("X.IncMSE","IncNodePurity","ID1" ,"guild" ,"phylum", "class", "order", "family", "genus","OTU")
row.names(imp.w)<-imp.w$ID1
#row.names(sub_otu)
sub_abu = sub_otu[rownames(imp.w),]
row.names(sub_abu)<-paste(imp.w$guild,imp.w$phylum, imp.w$class, imp.w$order, imp.w$family, imp.w$genus,imp.w$OTU, sep="..__")
sampFile = as.data.frame(sub_map$TP,row.names = row.names(sub_map))
colnames(sampFile)[1] = "group"
mat_t = t(sub_abu)
mat_t2 = merge(sampFile, mat_t, by="row.names")
mat_t2 = mat_t2[,-1]
mat_mean = aggregate(mat_t2[,-1], by=mat_t2[1], FUN=mean) # mean
otu_norm_group = do.call(rbind, mat_mean)[-1,]
colnames(otu_norm_group) = mat_mean$group
all.guildlevles<-c("Arbuscular.mycorrhizal","Endophyte","Plant.pathogen","Plant.pathogen_Yeast","Saprotroph","Saprotroph_Yeast", "Unidentified...others" )
imp.w$guild<-factor(imp.w$guild,
levels = c("Arbuscular.mycorrhizal","Endophyte","Plant.pathogen","Plant.pathogen_Yeast","Saprotroph","Saprotroph_Yeast", "Unidentified...others" ),
labels = c("Arbuscular mycorrhiza", "Endophyte", "Plant pathogen", "Plant pathogen (Yeast)", "Saprotroph", "Saprotroph (Yeast)", "Others"))
mat_row <- data.frame(row.names = row.names (otu_norm_group),Guild = imp.w$guild)
#mat_row$Guild
#levels(imp.w$guild)
#levels(imp.w$guild)
mycolors <- c("#0000FF","#FF3399","#FFCC33","#ff00ff","#00ff00", "deepskyblue", "bisque")
names(mycolors) <- levels(mat_row$Guild)
mycolors <- list(Guild = mycolors)
pheatmap(otu_norm_group,
scale="row",
annotation_row = mat_row,
annotation_colors = mycolors,
show_rownames = F,
cluster_cols = F,
legend = F,
cluster_rows = T)
}
fRF("Rhizosphere","Control")
```