今天给大家介绍一款发表在Nature Methods(IF=28.467)上的R包--trackViewer，可以对基因结构，SNP/突变结果，甲基化结果，组蛋白结果等进行高颜值的可视化，让你那些无处安放的多组学数据完美展示出来。
那么如何来实现呢，小编带着大家一步步来。
第一步：安装R包并导入
代码语言：javascript复制if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("trackViewer")
library(trackViewer)第二步：绘制第一层gene结构
为了让大家更清楚每一步细节，小编在这里从构建基因结构的数据开始讲起。需要事先安装并加载一些相关的R包：
代码语言：javascript复制library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
gr <- GRanges("chr11", IRanges(122929275, 122930122), strand="-")
trs <- geneModelFromTxdb(TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db,gr=gr)假设我们构造了两个相关基因结构的数据：gene1和gene2
对于gene1，需要准备好两个文件: gene1.bed(Figure 2)和gene1info.txt（Figure 3）。
Figure 2：gene1.bed文件格式
Figure 3：gene1info.txt文件格式
gene2同理，准备好两个文件: gene2.bed(Figure 4)和gene2info.txt（Figure 5）。
Figure 4：gene2.bed文件格式
Figure 5：geneinfo2.txt文件格式
读取两个基因的相关文件，产生gene结构信息：
代码语言：javascript复制chrname <- "chr21"
filedir <- "D://trackViewer"
#################设置需要显示的基因组范围
gr <- GRanges(chrname,IRanges(100,1100))
#################设置genestructure1的结构
geneinfo <- read.table("D://trackViewer/gene1info.txt",header=TRUE,sep="t",stringsAsFactors=FALSE)
genebed <- importScore(file.path(filedir, "gene1.bed"), format="BED",ranges=gr)
#################gene结构文件中不需要score信息，因此将其设为NULL
genebed$dat$score <- NULL
genebed$name <- "gene00"
genebed$type <- "transcript"
genebed$format <- character(0)
strand(genebed$dat) <- geneinfo$strand
genebed$dat$feature <- geneinfo$feature
genebed$dat$id <- geneinfo$id
genebed$dat$exon <- geneinfo$exon
genebed$dat$transcript <- geneinfo$transcript
genebed$dat$gene <- geneinfo$gene
genebed$dat$symbol <- geneinfo$symbol
genebed$dat$density <- geneinfo$density
genestructure1 <- genebed
#################设置genestructure2的结构
geneinfo2 <- read.table("D://trackViewer/gene2info.txt",header=TRUE,sep="t",stringsAsFactors=FALSE)
genebed2 <- importScore(file.path(filedir, "gene2.bed"), format="BED",ranges=gr)
genebed2$dat$score <- NULL
genebed2$name <- "gene00"
genebed2$type <- "transcript"
genebed2$format <- character(0)
strand(genebed2$dat) <- geneinfo$strand
genebed2$dat$feature <- geneinfo$feature
genebed2$dat$id <- geneinfo$id
genebed2$dat$exon <- geneinfo$exon
genebed2$dat$transcript <- geneinfo$transcript
genebed2$dat$gene <- geneinfo$gene
genebed2$dat$symbol <- geneinfo$symbol
genebed2$dat$density <- geneinfo$density
genestructure2 <- genebed2
#################
optSty <- optimizeStyle(trackList(genestructure1,genestructure2))
trackList <- optSty$tracks
viewerStyle <- optSty$style
#################
#设置图片位置
setTrackViewerStyleParam(viewerStyle, "margin", c(.01, .05, .01, .05))
#设置颜色及高度
setTrackStyleParam(trackList[[1]], "color", c("#3CB371"))
setTrackStyleParam(trackList[[1]], "height",0.05)
setTrackStyleParam(trackList[[2]], "color", c("#D2691E"))
setTrackStyleParam(trackList[[2]], "height",0.05)
#设置名称
names(trackList) <- c("gene1","gene2")
#设置Y轴名称字体大小
setTrackStyleParam(trackList[[1]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[2]], "ylabgp", list(cex=.6))
#绘制第一层基因结构
viewTracks(trackList,gr=gr,autoOptimizeStyle=TRUE,newpage=TRUE,viewerStyle=viewerStyle)这样最底层的基因结构就构建完毕：
第三步：绘制第二层Coverage信息
这里你需要准备Coverage的信息文件，如示例文件coverExample.bed格式：
读取Coverage信息，并绘制图片：
代码语言：javascript复制fox2 <- importScore(file.path(filedir, "coverExample.bed"), format="BED",ranges=gr)
#################
optSty <- optimizeStyle(trackList(genestructure1,genestructure2,fox2))
trackList <- optSty$tracks
viewerStyle <- optSty$style
#################
#设置图片位置
setTrackViewerStyleParam(viewerStyle, "margin", c(.01, .05, .01, .05))
#设置颜色及高度
setTrackStyleParam(trackList[[1]], "color", c("#3CB371"))
setTrackStyleParam(trackList[[1]], "height",0.05)
setTrackStyleParam(trackList[[2]], "color", c("#D2691E"))
setTrackStyleParam(trackList[[2]], "height",0.05)
setTrackStyleParam(trackList[[3]], "color", c("#4682B4"))
setTrackStyleParam(trackList[[3]], "height",0.1)
#设置名称
names(trackList) <- c("gene1","gene2","Coverage")
#设置Y轴名称字体大小
setTrackStyleParam(trackList[[1]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[2]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[3]], "ylabgp",list(cex=.6))
#绘制第二层Coverage信息
viewTracks(trackList,gr=gr,autoOptimizeStyle=TRUE,newpage=TRUE,viewerStyle=viewerStyle)这一步结束后，就可以在基因结构的基础上产生了reads的堆叠图：
第四步：绘制第三层SNP信息
需要准备SNP的文件，格式如示例文件：snpinfo.txt
读取SNP信息，并绘制图片：
代码语言：javascript复制chrname = "chr21"
snpinfo = read.table("D://trackViewer/snpinfo.txt",header=TRUE,sep="t")
snppos = snpinfo$rspos
snpname = snpinfo$rsname
snpscore = snpinfo$rsscore
#SNP得分设置为0~10，此时显示为1分为一个圆圈
snpwidth <- c(1)
snprange <- IRanges(start=snppos, width=snpwidth, names=snpname)
sample.gr <- GRanges(seqnames=chrname, snprange)
#设置snp圆圈内的颜色
sample.gr$color <- sample(c("#CC9999", "#666699"), length(snpname), replace=TRUE)
#设置snp圆圈上的颜色
sample.gr$border <- sample(c("#999999"), length(snpname), replace=TRUE)
sample.gr$score <- snpscore
gr = GRanges(chrname, IRanges(100,1100))
#设置snp圆圈大小
sample.gr$cex = 0.8
sample.gr$label.parameter.rot <- 45
lollipopData <- new("track", dat=sample.gr, type="lollipopData")
#################
optSty <- optimizeStyle(trackList(genestructure1,genestructure2,fox2,lollipopData))
trackList <- optSty$tracks
viewerStyle <- optSty$style
#################
#设置图片位置
setTrackViewerStyleParam(viewerStyle, "margin", c(.01, .05, .01, .05))
#设置颜色及高度
setTrackStyleParam(trackList[[1]], "color", c("#3CB371"))
setTrackStyleParam(trackList[[1]], "height",0.05)
setTrackStyleParam(trackList[[2]], "color", c("#D2691E"))
setTrackStyleParam(trackList[[2]], "height",0.05)
setTrackStyleParam(trackList[[3]], "color", c("#4682B4"))
setTrackStyleParam(trackList[[3]], "height",0.1)
#设置名称
names(trackList) <- c("gene1","gene2","Coverage","SNPinfo1")
#设置Y轴名称字体大小
setTrackStyleParam(trackList[[1]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[2]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[3]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[4]], "ylabgp", list(cex=.6))
#绘制第三层SNP信息
viewTracks(trackList,gr=gr,autoOptimizeStyle=TRUE,newpage=TRUE,viewerStyle=viewerStyle)这一步结束后，就会在最上层加入了SNP的可视化结果：
第五步：绘制第四层SNP信息，以棒棒图的形式显示：
代码语言：javascript复制sample.gr2 <- sample.gr
sample.gr2$score <- snpscore*2
#当分数大于10时，得分多少转变为线段的长短而不是一个个的圆圈
lollipopData2 <- new("track", dat=sample.gr2, type="lollipopData")
#################
optSty <- optimizeStyle(trackList(genestructure1,genestructure2,fox2,lollipopData,lollipopData2))
trackList <- optSty$tracks
viewerStyle <- optSty$style
#################
#设置图片位置
setTrackViewerStyleParam(viewerStyle, "margin", c(.01, .05, .01, .05))
#设置颜色及高度
setTrackStyleParam(trackList[[1]], "color", c("#3CB371"))
setTrackStyleParam(trackList[[1]], "height",0.07)
setTrackStyleParam(trackList[[2]], "color", c("#D2691E"))
setTrackStyleParam(trackList[[2]], "height",0.07)
setTrackStyleParam(trackList[[3]], "color", c("#4682B4"))
setTrackStyleParam(trackList[[3]], "height",0.12)
setTrackStyleParam(trackList[[4]], "height",0.35)
setTrackStyleParam(trackList[[5]], "height",0.35)
#设置名称
names(trackList) <- c("gene1","gene2","Coverage","SNPinfo1","SNPinfo2")
#设置Y轴名称字体大小
setTrackStyleParam(trackList[[1]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[2]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[3]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[4]], "ylabgp", list(cex=.6))
setTrackStyleParam(trackList[[5]], "ylabgp", list(cex=.6))
viewTracks(trackList,gr=gr,autoOptimizeStyle=TRUE,newpage=TRUE,viewerStyle=viewerStyle)
#添加参考线
vp <- viewTracks(trackList,gr=gr,autoOptimizeStyle=TRUE,newpage=TRUE,viewerStyle=viewerStyle)
addGuideLine(c(320,510), vp=vp,col="blue")这一步结束后就完成了示例文件的图形啦！
再多唠叨几句：
我们还可以通过以下命令修改元素：
代码语言：javascript复制browseTracks(trackList, gr=gr)trackViewer的功能远不止如此，还支持SNP相关的其他类型的图片，随手抛几张图给大家养养眼，快一起学起来吧！
