諾和培訓語言與數(shù)據(jù)可視化

1、R語言與數(shù)據(jù)可視化chaijin諾禾致源轉錄調(diào)控事業(yè)部主要內(nèi)容R語言繪圖的講解繪圖實例介紹R語言簡介開源：免費；各種擴展包；Bioconductor；易學：零編程基礎也快速上手統(tǒng)計：R Development Core Team由統(tǒng)計學家組成作圖：ggplot2其他：、語言、環(huán)境，跨R 語言終端RGui通過R 語言的終端可以進行交互操作，對學習R 語言幫助巨大。RGui新一代R工作環(huán)境RStudioRstudio 提供一個高度集成的工作環(huán)境。R程序包安裝1、連網(wǎng)時，用函數(shù)install.packages()選擇鏡像后，程序?qū)⒆詣硬惭b程序包。例如：打開，在控制臺輸入install.packag

2、es(ggplot2) 2、安裝本地zip包路徑：Packagesinstall.packages from local fileszip包的文件夾。選擇光盤或者本地磁盤上https:/3、加載程序包，用函數(shù)library()例如： library(ggplot2)R程序包安裝install.packages(ggplot2)install.packages(reshinstall.packages(gplots)2)install.packages(VennDiagram)install.packages(pheatmap)install.packages(plotrix)install.p

3、ackages(scattlot3d)未安裝或者安裝未成功TRD包安裝TRD包成功安裝TRD包數(shù)據(jù)可視化可視化是利用計算機圖形和圖像處理技術，將數(shù)據(jù)轉換成圖形或者圖像在屏幕上顯示出來，并進行交互處理的理論，方法和技術。可視化展現(xiàn)形式：氣泡圖、 流程圖、樹、平行坐標軸、矩陣圖、時間軸、散點圖、折線圖、網(wǎng)絡圖、地圖、圖、熱力圖等轉錄組的數(shù)據(jù)可視化R繪圖功能繪圖參數(shù)文本和符號的大小、點的類型和顏色、線的類型、顏色、字體等繪圖函數(shù)高級繪圖函數(shù)和低級繪圖函數(shù)繪圖參數(shù)：設置文本和符號的大小繪圖參數(shù)：設置點的類型pch=0到14時，點是空心的，col可以設置邊框顏色。 pch=15到20是實心的，col可以

4、設置其填充顏色。pch=21到25實心點，col可以設置其邊框，bg可以設置其填充顏色繪圖參數(shù)：設置線的類型繪圖參數(shù)：設置顏色繪圖參數(shù)：設置顏色繪圖參數(shù)：設置字體其他繪圖參數(shù)help(par)繪圖函數(shù)的分類高級（high-level）繪圖函數(shù)在圖形設備上產(chǎn)生一個新的繪圖區(qū)域，并生成一個新的圖形通過其參數(shù)可以設置坐標軸，標題等等低級（low-level）繪圖函數(shù)在已存在的圖形上加上的圖形元素，是繪制圖形的基礎函數(shù)如額外的點、線、多邊形和R的高級繪圖函數(shù)：plot()plot()是最常用R繪圖函數(shù)，是一個泛型函數(shù)，它產(chǎn)生的圖形依賴于第一個參數(shù)的類型或者類plot(mtcars$wt,mtcars$

5、mpg)R的高級繪圖函數(shù)：plot()set.seed(1); x - sset.seed(2); y - sxy - cbind(x, y)le(c(1:50),10)le(c(1:50),10)f x - mtcarsorder(mtcars$mpg), # sort by mpg x$cyl - factor(x$cyl) # it must be a factor x$colorx$cyl=4 - redx$colorx$cyl=6 - blue x$colorx$cyl=8 x - c(1:5); y - x; par(pch=22, col=red); par(mfrow=c(2,

6、4)opts - c(p,l,o,b,c,s,S,h) for(i in 1:length(opts)plot(x, y, type=n, main=paste(type=,optsi) lines(x, y, type=optsi)R的高級繪圖函數(shù):直方圖hist()、核密度圖density()直方圖（Histogram）：# 基本實現(xiàn)，使用hist觀察變量的分布hist(mtcars$mpg)# 設置桶的數(shù)量和填充顏色hist(mtcars$mpg, breaks=5, col=red)hist(mtcars$mpg, breaks=c(10,25,30,40), col=red) #精確

7、設置斷點核密度圖（Kernel Density Plots）：# 基本實現(xiàn)，使用density觀察變量分布d counts - table(mtcars$gear)barplot(counts, main=Car Distribution, xlab=Number of Gears) barplot(counts, main=Car Distribution, horiz=T, names.arg=c(3 Gears, 4 Gears, 5 Gears) # 水平放置，設置# 堆積條形圖，設置填充顏色和圖例counts sli- c(10, 12, 4, 16, 8)lbls pct - ro

8、und(slilbls 基本實現(xiàn)-使用plot()plot(mtcars$wt, mtcars$mpg, main=Scattlot Exle, xlab=CarWeight , ylab=Per Gallon , pch=19)abline(lm(mtcars$mpgmtcars$wt), col=red) # 添加回歸線# 3D散點圖library(scattlot3d)scattlot3d(mtcars$wt,mtcars$disp,mtcars$mpg, main=3D Scattlot)R的低級繪圖函數(shù)低級繪圖函數(shù)：點、直線、線段、箭頭、網(wǎng)格線R的低級繪圖函數(shù)：點、直線、線段# 基本

9、實現(xiàn)plot(-4:4, -4:4, type = p, col=blue) # 繪制點、連接點pos(x=c(3,-2,-1,3,2), y=c(1,2,-2,2,3), col = red)lines(x=c(3,-2,-1,3,2), y=c(1,2,-2,2,3),col=black)# 繪制直線abline(h=0) abline(v=0) abline(a=1,b=1)abline(lm(mtcars$mpg mtcars$qsec),col=red)R的低級繪圖函數(shù)：線段、箭頭、網(wǎng)格線# 繪制線段segments(x0=2, y0=-4.5, x1=4, y1=-2, col=r

10、ed, lty=dotted) # 繪制箭頭,并設置箭頭的長度、角度、樣式arrows(x0=-4, y0=4, x1=-2, y1=0, length=0.15, angle=30, code=3) #繪制網(wǎng)格線grid(nx=3, ny=5, col = lightgray, lty = dotted)R的低級繪圖函數(shù)：多邊形繪制由(x,y)作為頂點的多邊形的低級函數(shù)如下：polygon(x, y = NULL, density = NULL, angle = 45, border = NULL, col = NA, lty = par(lty), ., fillOddEven = FAL

11、SE)# 基本實現(xiàn)plot(-4:4, -4:4, type = p, col=blue)polygon(x=c(3,-2,-1,3,2), y=c(1,2,-2,2,3), col = red) # 繪制多邊形R的低級繪圖函數(shù)：標題、任意文本、周邊文本# 使用title()函數(shù)添加紅色標題和藍色子標題，設置坐標軸體相對大小為0.75plot(mtcars$wt, mtcars$mpg, main=,sub=,xlab=,ylab=)title(main=My Title, col.main=red, sub=My Sub-title, col.sub=blue, xlab=My X labe

12、l, ylab=My Y label, col.lab=green, cex.lab=0.75)# 使用text()/mtext()函數(shù)為繪圖區(qū)域/邊緣區(qū)域添加文本注釋為綠色，字plot(x=mtcars$wt, y=mtcars$mpg, main=Milage vs. Car Weight,xlab=Weight, ylab=age, pch=18, col=blue)text(x=mtcars$wt, y=mtcars$mpg, labels=s(mtcars), cex=0.6,=4, col=red)mtext(Added by mtext(), side=2, line=2,col

13、=blue) #在邊界加文本R的低級繪圖函數(shù)：坐標軸和圖例#添加坐標軸x - c(1:10); y - x; z counts 組合多個圖形R允許在同一個頁面中創(chuàng)建多組圖形，以便對這些圖形進行對比使用par()函數(shù)的mfrow/mfcol參數(shù)mypar plot(mtcars$wt,mtcars$disp,main=Scattlot of wt vs disp)hist(mtcars$wt, main=Histogram of wt) boxplot(mtcars$wt, main=Boxplot of wt) par(mypar)數(shù)據(jù)的讀寫設置工作路徑：gese() 獲取當前工作路徑() 指

14、定工作路徑list.files () 當前工作路徑下的文件數(shù)據(jù)的讀寫read 和write 系列函數(shù)：最為常用的數(shù)據(jù)方式是用read.table()函數(shù)或reav()函數(shù)外部txt或csv格式的文件。txt文件：制表符間隔(鍵盤左上角的Tab鍵） csv文件：逗號間隔xls文件：二進制文件，不能直接VolcanoplotVolcanoplotk - p- read.table(diffgene.txt,header = TRUE)p - ggplot(k,aes(x = log2FoldChange,y = -log10(padj),color = sig)p - p + geom_po(si

15、ze = 0.8)cols - c(up regulated: 1229 = blue,FALSE = green, down regulated:1921 = red )p - p + scale_color_manual(Differential Expressed Genes,values = cols, breaks = c(up regulated: 1229,FALSE,down regulated: 1921), limits = c(up regulated: 1229,down regulated: 1921)p - p + geom_hline(yercept = -log

16、10(0.05),linetype = dotdash,size = 0.4)p - p+scale_y_continuous(breaks=c(30,60,80),labels=c(30,60,80)p - p + ggtitle(A vs B)+xlab(expres(pasog2,(fold change),sep = ) + ylab(expres(paste(-log10,(padj),sep = )p - p + theme(panel.background = element_rect(fill = transparent),panel.border=element_rect(c

17、olour=black,fill=NA),panel.grid.minor = element_bl(), panel.grid.major = element_bl()pboxplotboxplotlibrary(ggplot2)library(resh2)rp-read.table(fpkm.txt,header=TRUE)df - log10(rp,-1+1)df - mef)colnames(df)-c(Group,value)p - ggplot(df, aes(x = Group, y = value)p - p + geom_boxplot(aes(fill = Group),a

18、lpha = 0.4,outr.size = 1)p - p + xlab() + ylab(expres(pasog10,(FPKM+1),sep = )+ labs(title = FPKM distribution)p - p + theme(panel.background = element_rect(fill = transparent)+ theme(panel.border = element_rect(colour = black,fill = NA)+theme(panel.grid.minor = element_bl()violinviolinq - ggplot(df

19、, aes(x = Group, y = value)q - q + geom_boxplot(aes(fill = Group),alpha=0.4,width=0.1,outr.colour=NA)q - q + geom_violin(aes(colour=Group,fill=Group),alpha=0.2, width=1)q - q + xlab() + ylab(expres(pasog10,(FPKM+1),sep = )+ labs(title=FPKM distribution)q - q + theme(panel.background = element_rect(f

20、ill = transparent), panel.border=element_rect(colour=black,fill=NA),panel.grid.minor = element_bl(), panel.grid.major =element_bl()multiple graphsmultiple graphspar(mfrow=c(2,2)plot(rnorm(50),pch=17)plot(rnorm(20),type=l,lty=5)plot(rnorm(100),cex=0.5)plot(rnorm(200),lwd=2)library(grid)grid.nege()pus

21、hViewport(viewport(layout = grid.layout(nrow = 1, ncol = 2)prpr(p, vp = viewport(layout.(q, vp = viewport(layout.row = 1, layout.row = 1, layout.col = 1).col = 2)vennlibrary(gplots)A-letters1:10B-letters5:16C-lettersc(3:6, 9:13, 17:20)x-list(A, B, C)venn(x)vennlibrary(VennDiagram)V - venn.diagram(li

22、st(A=A,B=B),filenaV - venn.diagram(list(A=A,B=B),filenaLL)LL,fill=c(red,green),col=c(red,green),cat.col=c(red,green)V - venn.diagram(list(A=A,B=B),filenaV - venn.diagram(list(A=A,B=B),filena lwd=2)V - venn.diagram(list(A=A,B=B),filena c(0.5,0.5)LL,cex=1.4, cat.cex=1.6)LL,fill=c(red,green),lty=0,LL,fill=c(red,green),alpha=grid.nege()grid.draw(V)vennLength_A-leng)Length_B-length(B)Length_AB-length(ersect(A,B)grid.nedrage()irwise.venn(area1=Length_A,area2=Length_B,cross.area=Length_AB,ca