通过lncRNA获得结合的miRNA（3个miRNA预测数据库中都存在），再获得miRNA作用的靶基因mRNA，靶基因mRNA与任一肿瘤差异mRNA取交集，交集mRNA与其相关miRNA,lncRNA作互作图，并将交集mRNA作GO和KEGG富集分析。
通过lncRNA获得结合的miRNA
1.lncRNA与miRNA的区别
2.mircode数据库
3.这可能是最轻松易懂的lncRNA与miRNA介绍了
4.LncRNA与miRNA结合预测
进入主页点击download
mircode数据库主页.png
下载miRNA数据.png
解压修改文件名为mircode.txt
mircode.txt.png
将差异分析后的lncRNA复制粘贴到新建diff_lncRNA.txt文件中
差异分析后的lncRNA.png
diff_lncRNA.txt.png
输入文件为：
输入准备文件.png
perl代码：
use strict;
use warnings;
my %hash=();
open(RF,"diff_lncRNA.txt") or die $!;
while(my $line=<RF>){
chomp($line);
$hash{$line}=1;
}
close(RF);
my %rep=();
open(RF,"mircode.txt") or die $!;
open(WF,">lncRNA_mircode.txt") or die $!;
while(my $line=<RF>){
if($.==1){
print WF "lncRNA\tmiRNA\n";
next;
}
my @arr=split(/\t/,$line);
my @oneArr=split(/\./,$arr[1]);
if(exists $hash{$oneArr[0]}){
my @threeArr=split(/\//,$arr[3]);
foreach my $mir(@threeArr){
if($mir=~/^miR/){
$mir="hsa-$mir";
}
else{
$mir="hsa-miR-$mir";
}
my $out="$oneArr[0]\t$mir";
unless(exists $rep{$out}){
print WF "$out\n";
$rep{$out}=1;
}
}
}
}
close(WF);
close(RF);
输出文件为
输出文件.png
获取在三个数据库共有的miRNA及其结合的mRNA
输入文件.png
use strict;
use warnings;
my %miHash=();
open(RF,"lncRNA_mircode.txt") or die $!;
while(my $line=<RF>){
chomp($line);
my @arr=split(/\t/,$line);
$arr[1]=~s/^\s+|\s+$//g;
if($arr[1]=~/hsa/){
$miHash{$arr[1]}=1;
}
else{
$miHash{"hsa-$arr[1]"}=1;
}
}
close(RF);
my %hash=();
my @files=glob("*.tsv");
my @dbs=();
foreach my $file(@files){
my $db=$file;
$db=~s/\.tsv//g;
push(@dbs,$db);
open(RF,"$file") or die $!;
while(my $line=<RF>){
chomp($line);
my @arr=split(/\t/,$line);
$arr[0]=~s/mir/miR/g;
if(exists $miHash{$arr[0]}){
my $mirnaGene="$arr[0]\t$arr[1]";
${$hash{$mirnaGene}}{$db}=1;
}
}
close(RF);
}
open(WF,">target.txt") or die $!;
print WF "miRNA\tGene\t" . join("\t",@dbs) . "\tSum\n";
foreach my $key(keys %hash){
my $outLine=$key;
my $sum=0;
foreach my $db(@dbs){
if(exists ${$hash{$key}}{$db}){
$sum++;
$outLine=$outLine . "\t1";
}
else{
$outLine=$outLine . "\t0";
}
}
if($sum>=3){
print WF $outLine . "\t$sum\n";
}
}
close(WF);
输出文件为：
target.txt.png
Cytoscape输入文件准备
将之前作差异分析后得到的mRNA和logFC复制粘贴到新建gene.txt文件中
gene.txt.png
输入文件为：
输入文件.png
perl代码如下：
use strict;
use warnings;
my %geneHash=();
open (RF,"gene.txt") or die $!;
while(my $line=<RF>){
chomp($line);
my @arr=split(/\t/,$line);
$geneHash{$arr[0]}=$arr[1];
}
close(RF);
my %miHash=();
open (RF,"target.txt") or die $!;
while(my $line=<RF>){
next if($.==1);
chomp($line);
my @arr=split(/\t/,$line);
if(exists $geneHash{$arr[1]}){
$miHash{$arr[0]}=1;
}
}
close(RF);
my %miHash2=();
my %repHash=();
open(NET,">network.txt") or die $!;
print NET "Node1\tNode2\tLine\n";
open(TYPE,">type.txt") or die $!;
print TYPE "Name\tType\n";
open(MRNA,">mRNA.txt") or die $!;
open(LNC,">lncRNA.txt") or die $!;
open(MI,">miRNA.txt") or die $!;
open (SYMBOL,">symbol.txt") or die $!;
print SYMBOL "gene\tlogFC\n";
open (RF,"lncRNA_mircode.txt") or die $!;
while(my $line=<RF>){
next if($.==1);
chomp($line);
my @arr=split(/\t/,$line);
if(exists $miHash{$arr[1]}){
print NET "$arr[0]\t$arr[1]\tlncRNA\n";
unless($repHash{$arr[0]}){
print TYPE "$arr[0]\tlncRNA\n";
$repHash{$arr[0]}=1;
print LNC "$arr[0]\n";
}
$miHash2{$arr[1]}=1;
}
}
close(RF);
open (RF,"target.txt") or die $!;
while(my $line=<RF>){
next if($.==1);
chomp($line);
my @arr=split(/\t/,$line);
if( (exists $geneHash{$arr[1]}) && (exists $miHash2{$arr[0]}) ){
print NET "$arr[0]\t$arr[1]\tmRNA\n";
unless($repHash{$arr[0]}){
print TYPE "$arr[0]\tmiRNA\n";
$repHash{$arr[0]}=1;
print MI "$arr[0]\n";
}
unless($repHash{$arr[1]}){
print TYPE "$arr[1]\tmRNA\n";
$repHash{$arr[1]}=1;
print MRNA "$arr[1]\n";
print SYMBOL "$arr[1]\t$geneHash{$arr[1]}\n";
}
}
}
close(SYMBOL);
close(RF);
close(MRNA);
close(LNC);
close(MI);
close(NET);
close(TYPE);
输出文件.png
lncRNA.txt.png
miRNA.txt.png
mRNA.txt.png
network.txt.png
symbol.txt.png
type.txt.png
ceRNA网络构建
下载Java jre版本，下载Cytoscape软件，两软件下载在同一文件夹下
导入network.txt
步骤1.png
步骤2.png
步骤3.png
导入type.txt
步骤1.png
步骤2.png
直接点OK.png
根据喜好选择颜色，形状等
参数调节.png
根据类型选择颜色.png
根据类型选择形状.png
选择lncRNA.png
点击打开.png
根据名字形成圆圈.png
鼠标在空白处点一下，进行重复操作
边参数设置.png
导出图片
导出方式.png
zoom拉到最大.png
基因名字转换为基因id
输入文件symbol.txt.png
setwd("E:\\research")
library("org.Hs.eg.db")
rt=read.table("symbol.txt",sep="\t",check.names=F,header=T)
genes=as.vector(rt[,1])
entrezIDs <- mget(genes, org.Hs.egSYMBOL2EG, ifnotfound=NA)
entrezIDs <- as.character(entrezIDs)
out=cbind(rt,entrezID=entrezIDs)
write.table(out,file="id.txt",sep="\t",quote=F,row.names=F)
输出文件id.txt.png
GO富集分析
输入文件id.txt.png
setwd("E:\\research")
library("clusterProfiler")
library("org.Hs.eg.db")
rt=read.table("id.txt",sep="\t",header=T,check.names=F)
rt=rt[is.na(rt[,"entrezID"])==F,]
geneFC=rt$logFC
gene=rt$entrezID
names(geneFC)=gene
#GO富集分析
kk <- enrichGO(gene = gene,OrgDb = org.Hs.eg.db, pvalueCutoff =0.05, qvalueCutoff = 0.05) #若筛选出的结果太少，可将qvalueCutoff = 0.05调为1
write.table(kk,file="GO.txt",sep="\t",quote=F,row.names = F)
#柱状图
tiff(file="barplot.tiff",width = 35,height = 20,units ="cm",compression="lzw",bg="white",res=600 #调节分辨率)
barplot(kk, drop = TRUE, showCategory = 100 #显示前100个通路)
dev.off()
#点图
tiff(file="dotplot.tiff",width = 35,height = 20,units ="cm",compression="lzw",bg="white",res=600)
dotplot(kk,showCategory = 100)
dev.off()
输出文件GO.txt.png
将id转换回基因名，使可读性更好，输入文件GO.txt
perl代码
use strict;
use warnings;
my %hash=();
open(RF,"id.txt") or die $!;
while(my $line=<RF>){
chomp($line);
my @arr=split(/\t/,$line);
$hash{$arr[$#arr]}="$arr[0]";
}
close(RF);
open(KEGG,"GO.txt") or die $!;
open(WF,">GO.xls") or die $!;
while(my $line=<KEGG>){
if($.==1){
print WF $line;
next;
}
chomp($line);
my @arr=split(/\t/,$line);
my @idArr=split(/\//,$arr[$#arr-1]);
my @symbols=();
foreach my $id(@idArr){
if(exists $hash{$id}){
push(@symbols,$hash{$id});
}
}
$arr[$#arr-1]=join("/",@symbols);
print WF join("\t",@arr) . "\n";
}
close(WF);
close(KEGG);
输出文件GO.xls.png
KEGG富集分析
输入文件id.txt.png
setwd("C:\\Users")
library("clusterProfiler")
rt=read.table("id.txt",sep="\t",header=T,check.names=F)
rt=rt[is.na(rt[,"entrezID"])==F,]
geneFC=rt$logFC
gene=rt$entrezID
names(geneFC)=gene
#kegg富集分析，定义物种类型人就是hsa
kk <- enrichKEGG(gene = gene, organism = "hsa" , pvalueCutoff =0.05, qvalueCutoff =0.05)
write.table(kk,file="KEGG.txt",sep="\t",quote=F,row.names = F)
#柱状图
tiff(file="barplot.tiff",width = 30,height = 20,units ="cm",compression="lzw",bg="white",res=600)
barplot(kk, drop = TRUE, showCategory = 100)
dev.off()
#点图
tiff(file="dotplot.tiff",width = 30,height = 20,units ="cm",compression="lzw",bg="white",res=600)
dotplot(kk, showCategory = 100)
dev.off()
#通路图
library("pathview")
keggxls=read.table("KEGG.txt",sep="\t",header=T)
#gene.data = geneFC基因颜色定义
for(i in keggxls$ID){
pv.out <- pathview(gene.data = geneFC, pathway.id = i, species = "hsa", out.suffix = "pathview")
}
输出文件KEGG.txt.png
将id转换回基因名，使可读性更好，输入文件KEGG.txt
perl代码
use strict;
use warnings;
my %hash=();
open(RF,"id.txt") or die $!;
while(my $line=<RF>){
chomp($line);
my @arr=split(/\t/,$line);
$hash{$arr[$#arr]}="$arr[0]";
}
close(RF);
open(KEGG,"KEGG.txt") or die $!;
open(WF,">KEGG.xls") or die $!;
while(my $line=<KEGG>){
if($.==1){
print WF $line;
next;
}
chomp($line);
my @arr=split(/\t/,$line);
my @idArr=split(/\//,$arr[$#arr-1]);
my @symbols=();
foreach my $id(@idArr){
if(exists $hash{$id}){
push(@symbols,$hash{$id});
}
}
$arr[$#arr-1]=join("/",@symbols);
print WF join("\t",@arr) . "\n";
}
close(WF);
close(KEGG);
KEGG.xls.png
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