基因组的可视化展示大家应该都熟悉，今天给大家看一下在R语言中的一个用来进行基因组可视化的包Sushi。一些基础的理论就不再赘述了，首先我们看下包的安装：

BiocManager::install("Sushi")

接下来我们直接通过实例来看下包中的各种展示方式：

1. 包支持的数据输入类型

library('Sushi')Sushi_data = data(package = 'Sushi')data(list = Sushi_data$results[,3])

2. 信号轨迹图，所需的基本参数包括要绘制的数据、染色体和开始和停止位置。

chrom = "chr11"chromstart = 1650000chromend = 2350000plotBedgraph(Sushi_DNaseI.bedgraph,chrom,chromstart,chromend,colorbycol=SushiColors(5))

当然，我们也可以展示各信号的位置信息以及坐标轴值：

labelgenome(chrom,chromstart,chromend,n=4,scale="Mb")mtext("ReadDepth",side=2,line=1.75,cex=1,font=2)axis(side=2,las=2,tcl=.2)

如果多个序列同时显示，那就需要设置参数overlay=TRUE，同时如果想多个序列坐标轴范围一致需要用到rescaleoverlay=TRUE

chrom = "chr11"chromstart = 1955000chromend = 1960000plotBedgraph(Sushi_ChIPSeq_CTCF.bedgraph,chrom,chromstart,chromend,transparency=.50,color=SushiColors(2)(2)[1])plotBedgraph(Sushi_DNaseI.bedgraph,chrom,chromstart,chromend,transparency=.50,color=SushiColors(2)(2)[2],overlay=TRUE,rescaleoverlay=TRUE)labelgenome(chrom,chromstart,chromend,n=3,scale="Kb")legend("topright",inset=0.025,legend=c("DNaseI","ChIP-seq(CTCF)"), fill=opaque(SushiColors(2)(2)),border=SushiColors(2)(2),text.font=2,cex=1.0)

#独立展示对比效果需要设置参数flip=TRUEpar(mfrow=c(2,1),mar=c(1,4,1,1))plotBedgraph(Sushi_ChIPSeq_CTCF.bedgraph,chrom,chromstart,chromend,transparency=.50,color=SushiColors(2)(2)[1])axis(side=2,las=2,tcl=.2)mtext("ReadDepth",side=2,line=1.75,cex=1,font=2)legend("topright",inset=0.025,legend=c("DNaseI","ChIP-seq(CTCF)"),fill=opaque(SushiColors(2)(2)),border=SushiColors(2)(2),text.font=2, cex=1.0) plotBedgraph(Sushi_DNaseI.bedgraph, chrom,chromstart, chromend, transparency=.50, flip=TRUE, color=SushiColors(2)(2)[2])labelgenome(chrom,chromstart,chromend,side=3,n=3,scale="Kb")axis(side=2,las=2,tcl=.2,at=pretty(par("yaxp")[c(1,2)]),labels=-1*pretty(par("yaxp")[c(1,2)]))mtext("ReadDepth",side=2,line=1.75,cex=1,font=2)

3. Hi-C测序结果的染色体互作可视化。Hi-C可以展现出整个染色体all-to-all的互作关系。

chrom = "chr11"chromstart = 500000chromend = 5050000phic = plotHic(Sushi_HiC.matrix,chrom,chromstart, chromend, max_y = 20,zrange=c(0,28), palette=SushiColors(7))addlegend(phic[[1]], palette=phic[[2]],title="score", side="right", bottominset=0.4, topinset=0,xoffset=-.035, labelside="left", width=0.025, title.offset=0.035)labelgenome(chrom, chromstart, chromend,n=4, scale="Mb", edgeblankfraction=0.20)

此图和热图想表达的意义是一样的根据颜色的变化可以看出各位点之间相关性打分的大小。当然，还可以对其中的一些细节可以进一步的操作，我们直接看下实例：

chrom = "chr11"chromstart = 500000chromend = 5050000phic =plotHic(Sushi_HiC.matrix,chrom,chromstart,chromend,max_y = 20,zrange=c(0,28),flip=TRUE,palette=topo.colors)addlegend(phic[[1]],palette=phic[[2]],title="score",side="left",bottominset=0.1,topinset=0.5,xoffset=-.035,labelside="right",width=0.025,title.offset=0.035)labelgenome(chrom,chromstart,chromend,side=3,n=4,scale="Mb",edgeblankfraction=0.20)

4. bedpe格式数据的可视化展示

chrom = "chr11"chromstart = 1650000chromend = 2350000pbpe =plotBedpe(Sushi_5C.bedpe,chrom,chromstart,chromend,heights =Sushi_5C.bedpe$score,plottype="loops",colorby=Sushi_5C.bedpe$samplenumber,colorbycol=SushiColors(3))labelgenome(chrom,chromstart,chromend,n=3,scale="Mb")legend("topright",inset=0.01,legend=c("K562","HeLa","GM12878"),col=SushiColors(3)(3),pch=19,bty='n',text.font=2)axis(side=2,las=2,tcl=.2)mtext("Z-score",side=2,line=1.75,cex=.75,font=2)

上图中的拱形的高度代表了Z-score of the 5C interaction。不同的颜色代表不同的细胞系，线的粗细是一个恒量。

#绘制线型的可视化结果：chrom = "chr11"chromstart = 1650000chromend = 2350000pbpe =plotBedpe(Sushi_5C.bedpe,chrom,chromstart,chromend,flip=TRUE,plottype="lines",colorby=Sushi_5C.bedpe$score,colorbycol=SushiColors(5))labelgenome(chrom,chromstart,chromend,side=3,n=3,scale="Mb")addlegend(pbpe[[1]],palette=pbpe[[2]],title="Z-score",side="right",bottominset=0.05,topinset=0.05,xoffset=-.035,labelside="right",width=0.025,title.offset=0.045)

上图就是将拱形进行直线化，直接通过直线将两个互作的片段之间进行连线，并通过颜色代表Z-score大小。

5. 对bed格式的chip数据的可视化展示。

chrom = "chr11"chromstart = 2281200chromend = 2282200plotBed(beddata = Sushi_ChIPSeq_pol2.bed,chrom = chrom,chromstart =chromstart,chromend =chromend,colorby = Sushi_ChIPSeq_pol2.bed$strand,colorbycol= SushiColors(2),row = "auto",wiggle=0.001)labelgenome(chrom,chromstart,chromend,n=2,scale="Kb")legend("topright",inset=0,legend=c("reverse","forward"),fill=SushiColors(2)(2),border=SushiColors(2)(2),text.font=2,cex=0.75)

上图通过颜色将strand进行分别标记

#独立分割srandchrom = "chr11"chromstart = 2281200chromend = 2282200plotBed(beddata = Sushi_ChIPSeq_pol2.bed,chrom = chrom,chromstart =chromstart,chromend =chromend,colorby = Sushi_ChIPSeq_pol2.bed$strand,colorbycol= SushiColors(2),row = "auto",wiggle=0.001,splitstrand=TRUE)labelgenome(chrom,chromstart,chromend,n=2,scale="Kb")legend("topright",inset=0,legend=c("reverse","forward"),fill=SushiColors(2)(2),border=SushiColors(2)(2),text.font=2,cex=0.75)

#多样本数据的比较，点图和矩形图Sushi_ChIPSeq_severalfactors.bed$color=maptocolors(Sushi_ChIPSeq_severalfactors.bed$row,col=SushiColors(6))chrom = "chr15"chromstart = 72800000chromend = 73100000plotBed(beddata =Sushi_ChIPSeq_severalfactors.bed,chrom = chrom,chromstart = chromstart,chromend=chromend,rownumber = Sushi_ChIPSeq_severalfactors.bed$row, type ="circles",color=Sushi_ChIPSeq_severalfactors.bed$color,row="given",plotbg="grey95",rowlabels=unique(Sushi_ChIPSeq_severalfactors.bed$name),rowlabelcol=unique(Sushi_ChIPSeq_severalfactors.bed$color),rowlabelcex=0.75)labelgenome(chrom,chromstart,chromend,n=3,scale="Mb")mtext("ChIP-seq",side=3,adj=-0.065,line=0.5,font=2)

plotBed(beddata =Sushi_ChIPSeq_severalfactors.bed,chrom = chrom,chromstart = chromstart,chromend=chromend,rownumber = Sushi_ChIPSeq_severalfactors.bed$row, type ="region",color=Sushi_ChIPSeq_severalfactors.bed$color,row="given",plotbg="grey95",rowlabels=unique(Sushi_ChIPSeq_severalfactors.bed$name),rowlabelcol=unique(Sushi_ChIPSeq_severalfactors.bed$color),rowlabelcex=0.75)labelgenome(chrom,chromstart,chromend,n=3,scale="Mb")mtext("ChIP-seq",side=3,adj=-0.065,line=0.5,font=2)

我们也可以通过位置信息获得我们想要的基因信息：

chrom = "chr15"chromstart = 60000000chromend = 80000000chrom_biomart =gsub("chr","",chrom)mart=useMart(host='may2009.archive.ensembl.org',biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl')geneinfobed = getBM(attributes =c("chromosome_name","start_position","end_position"),filters= c("chromosome_name","start","end"), values=list(chrom_biomart,chromstart,chromend),mart=mart)geneinfobed[,1] =paste("chr",geneinfobed[,1],sep="")

那么接下来我们就可以绘制基因密度图，所谓基因密度是指一个基因中所含碱基的相对数量，相对数量越大，基因密度越大。比较难懂，通俗点就是在所提供的序列长度中包含的基因越多，那么基因密度越大。

plotBed(beddata =geneinfobed[!duplicated(geneinfobed),],chrom = chrom,chromstart =chromstart,chromend =chromend,row='supplied',palettes = list(SushiColors(7)),type = "density")labelgenome(chrom, chromstart, chromend,n=4,scale="Mb",edgeblankfraction=0.10)mtext("Gene Density",side=3,adj=0,line=0.20,font=2)

6. 曼哈顿图绘制

plotManhattan(bedfile=Sushi_GWAS.bed,pvalues=Sushi_GWAS.bed[,5],col=SushiColors(6),genome=Sushi_hg18_genome,cex=0.75)labelgenome(genome=Sushi_hg18_genome,n=4,scale="Mb",edgeblankfraction=0.20,cex.axis=.5)axis(side=2,las=2,tcl=.2)mtext("log10(P)",side=2,line=1.75,cex=1,font=2)mtext("chromosome",side=1,line=1.75,cex=1,font=2)

7. 基因结构的绘制

chrom = "chr15"chromstart = 72998000chromend = 73020000pg =plotGenes(Sushi_genes.bed,chrom,chromstart,chromend ,types=Sushi_genes.bed$type,maxrows=1,bheight=0.2,plotgenetype="arrow",bentline=FALSE,labeloffset=.4,fontsize=1.2,arrowlength= 0.025,labeltext=TRUE)labelgenome( chrom,chromstart,chromend,n=3,scale="Mb")

#RNA结构的绘制chrom = "chr15"chromstart = 72965000chromend = 72990000pg =plotGenes(Sushi_transcripts.bed,chrom,chromstart,chromend ,types =Sushi_transcripts.bed$type,colorby=log10(Sushi_transcripts.bed$score+0.001),colorbycol=SushiColors(5),colorbyrange=c(0,1.0),labeltext=TRUE,maxrows=50,height=0.4,plotgenetype="box")labelgenome( chrom,chromstart,chromend,n=3,scale="Mb")addlegend(pg[[1]],palette=pg[[2]],title="log10(FPKM)",side="right",bottominset=0.4,topinset=0,xoffset=-.035,labelside="left",width=0.025,title.offset=0.055)

8. 局部放大显示

layout(matrix(c(1,1,2,3),2, 2, byrow =TRUE))par(mar=c(3,4,1,1)) #基础架构chrom = "chr11"chromstart = 1900000chromend = 2350000plotBedgraph(Sushi_DNaseI.bedgraph,chrom,chromstart=chromstart,chromend=chromend,colorbycol=SushiColors(5))labelgenome(chrom,chromstart=chromstart,chromend=chromend,n=4,scale="Mb")mtext("ReadDepth",side=2,line=1.75,cex=1,font=2)axis(side=2,las=2,tcl=.2) #放大的位置zoomregion1 = c(1955000,1960000)zoomregion2 = c(2279000,2284000)zoomsregion(zoomregion1,extend=c(0.01,0.13),wideextend=0.05,offsets=c(0,0.580))zoomsregion(zoomregion2,extend=c(0.01,0.13),wideextend=0.05,offsets=c(0.580,0))#放大区域数据可视化plotBedgraph(Sushi_DNaseI.bedgraph,chrom,chromstart=zoomregion1[1],chromend=zoomregion1[2],colorbycol=SushiColors(5))labelgenome(chrom,chromstart=zoomregion1[1],chromend=zoomregion1[2],n=4,scale="Kb",edgeblankfraction=0.2,cex.axis=.75)zoombox()mtext("ReadDepth",side=2,line=1.75,cex=1,font=2)axis(side=2,las=2,tcl=.2) plotBedgraph(Sushi_DNaseI.bedgraph,chrom,chromstart=zoomregion2[1],chromend=zoomregion2[2],colorbycol=SushiColors(5))labelgenome(chrom,chromstart=zoomregion2[1],chromend=zoomregion2[2],n=4,scale="Kb",edgeblankfraction=0.2,cex.axis=.75)zoombox()mtext("ReadDepth",side=2,line=1.75,cex=1,font=2)axis(side=2,las=2,tcl=.2)

欢迎大家互相学习交流！