构建一个循环的系统发育树

Question

我有一张他们相关的基因和疾病表。我想构建一个系统发育树并将基因分组到他们的疾病中。下面是一个样本数据集，其中gene1列属于疾病1，基因2属于疾病2。主要基因1和基因2彼此相关，并映射到它们所属的疾病。

gene1   gene2   disease1           disease2
AGTR1   ACHE    cancer              tumor
AGTR1   ACHE    parkinson's         asthma
ALOX5   ADRB1   myocardial infarct  heart failure
AR      ADORA1  breast cancer       anxiety disorder

我希望有一个循环的系统发育树用于我的目的，在下面的链接中给出： http://itol.embl.de/itol.cgi

有任何建议在R或任何软件中执行此操作吗？

由于

我现在正在运行的代码：

d=read.csv("genes_disease.txt",sep="\t",header=TRUE)
phyl_gad <-as.phylo(hclust(dist(d)))
 plot(phyl_gad,type="fan",edge.col=c("red","green","blue","orange","yellow","pink","magenta","white"),show.tip.label=FALSE)

如果我执行show.tip.label = TRUE，则会有太多标签被绘制并使提示混乱。

我修改后的数据集现在只有两列，一列用于基因，一列用于疾病。

Answer 1

啊，我以前做过这个。正如Bryan所说，你想使用ape包。假设您有hclust个对象。例如，

library(ape)
fit<-hclust(d,method='ward')
plot(as.phylo(fit),type='fan',label.offset=0.1,no.margin=TRUE)

如果要修改树末端的颜色，可以使用cutree和tip.color参数。这将为不同的簇创建一组重复的颜色（例如，color=c('red','blue')将为分支的末尾添加交替的蓝色和红色文本。

nclus=...#insert number of clusters you want to cut to
color=...#insert a vector of colors here
fit<-hclust(d,method='ward')
color_list=rep(color,nclus/length(color))
clus=cutree(fit,nclus)
plot(as.phylo(fit),type='fan',tip.color=color_list[clus],label.offset=0.1,no.margin=TRUE) 

我不确定你想使用什么类型的聚类方法（我使用的是Ward的方法），但这就是你的方法。

Answer 2

我认为你想做的不是系统发育，而是远距离聚类。这是一个可重复的例子。

library(XML)
library(RCurl)#geturl
library(rlist)
library(plyr)
library(reshape2)
library(ggtree)

#get the genes/ diseases info from internet
#example from http://www.musclegenetable.fr/
urllist<-paste0("http://195.83.227.65/4DACTION/GS/",LETTERS[1:24] )
theurl <- lapply(urllist, function(x) RCurl::getURL(x,.opts = list(ssl.verifypeer = T) ) )# wait
theurl2<-lapply(theurl, function(x) gsub("<span class='Style18'>","__",x))
tables <- lapply(theurl2, function (x) XML::readHTMLTable(x) )
tables2 <- lapply(tables, function(x) rlist::list.clean(x, fun = is.null, recursive = FALSE) )
unlist1 = lapply(tables2, plyr::ldply)
newdf<-do.call(rbind, unlist1)
colnames(newdf)[4]<-"diseases"
colnames(newdf)[2]<-"Gene"
newdf$gene<-sub("([A-z0-9]+)(__)(.*)","\\1",newdf$Gene)
newdf$diseases<-sub("(\\* )","",newdf$diseases, perl=T)

#split info of several diseases per gene, and simplify text 
#to allow better clustering
newdf2<-as.data.frame(data.table::setDT(newdf)[, strsplit(as.character(diseases), "* ", fixed=TRUE), by = .(gene, diseases)
][,.(diseases = V1, gene)])
newdf2$disease<-sub("([A-z0-9,\\-\\(\\)\\/ ]+)( \\- )(.*)","\\1",newdf2$diseases)
newdf2$disease<-gsub("[0-9,]","",newdf2$disease)
newdf2$disease<-gsub("( [A-Z]{1,2})$","",newdf2$disease)
newdf2$disease<-gsub("(\\-)","",newdf2$disease)
newdf2$disease<-gsub("\\s*\\([^\\)]+\\)","",newdf2$disease)
newdf2$disease<-gsub("\\s*type.*","",newdf2$disease, ignore.case = T)
newdf2$disease<-gsub("(X{0,3})(IX|IV|V?I{0,3})","", newdf2$disease)
newdf2$disease<-gsub("( [A-z]{1,2})$","",newdf2$disease)
newdf2$disease<-sub("^([a-z])(.*)","\\U\\1\\E\\2",newdf2$disease, perl=T)
newdf2$disease<-trimws(newdf2$disease)
newdf2<-newdf2[,c(2,3)]

#make clustering and tree
newcasted <- reshape2::dcast(newdf2, gene ~ disease)
phyl_gad <-ape::as.phylo(hclust(dist(newcasted)))

#use names of genes and diseases in tree
DT <- data.table::as.data.table(newdf2)
newdf4<-as.data.frame(DT[, lapply(.SD, paste, collapse=","), by = gene, .SDcols = 2])
newdf4$genemerge<-paste(newdf4$gene, newdf4$disease)
phyl_gad$tip.label<-newdf4$genemerge

#plot tree
ggtree::ggtree(phyl_gad, layout = "circular")+ ggtree::geom_tiplab2(offset=0.1, align = F, size=4)