R绘制Venn Diagram的学习笔记

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moonstone 发表于 2015-11-26 16:50:26 |AI写论文

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Venn Diagram，简称文氏图，又称为维恩图，是一种非常常用的图示手段，主要用于显示组与组之间重叠的程度。比如为了验证某一基因的功能学意义，在不确定其靶基因的情况下，可以先从公用数据库中，预测出可能的靶基因。但是这种预测靶基因的数据库目前来说比较多，如何才能获得一个相对准确的靶基因？相对可靠的办法是分析比较不同数据库预测的靶基因的一致性，然后从中找出所有数据库都能预测到的靶基因作为感兴趣的基因进行功能验证。这是生物信息学分析里面一个非常普遍的思路。

三个数据集的文氏图：
Three-set Venn diagram.jpeg

四个数据集的文氏图：
Four-set Venn diagram.jpeg

五个数据集的文氏图：

目前基于R绘制Venn Diagram（简称文氏图）的程序有四种：
1、       limma::vennDiagram
2、       gplots::venn
3、       venneuler
4、       VennDiagram

四种方法中第一和第四种方法相对比较常用。
另外，绘制文氏图需要特别注意以下几点：
1、       limma::vennDiagram及gplots::venn不能绘制彩色的文氏图，而VennDiagram绘制的文氏图相对更为美观。
2、       gplots::venn及VennDiagram绘制的文氏图是基于列表数据的，也即是必须先知道不同数据集的各自交叉子集数才能绘制文氏图。而limma::vennDiagram却是基于R对象的，这在不知道具体不同数据集的交叉子集数的情况下，也即探索不同数据集的交叉子集时，有着明显优势。
3、       由于limma (全称：Linear Models for Microarray Data)是Bioconductor中用于分析微阵列数据的非常重要及常用的程序包。因此，对于生物信息学研究者来说，采用limma::vennDiagram绘制文氏图更为多见。但是为了更加美观的文氏图，可以通过limma::vennDiagram先得到不同数据集的交叉子集数，然后采用VennDiagram绘制更加美观的彩色文氏图。

附件中分别介绍了如何采用VennDiagram及limma::vennDiagram绘制文氏图的效果及相应的R编码。需要说明的是：由于VennDiagram绘制文氏图是基于已知的交叉子集数下绘制，但是在生物统计分析中，通常并不知道不同数据集的交叉子集数。所以最后采用实例分析如何采用limma::vennDiagram来绘制不清楚交叉子集数情况下的文氏图。

附件下载网址：http://yunpan.cn/cLhiA3x5csU4J  访问密码 b8b1

/# VennDiagram
/# TWo-set Venn diagram
# A simple two-set diagram
venn.plot <- draw.pairwise.venn(100, 70, 30, c("First", "Second"));
grid.draw(venn.plot);
# Same diagram as above, but without scaling
grid.newpage();
venn.plot <- draw.pairwise.venn(100, 70, 30, c("First", "Second"), scaled = FALSE);
grid.draw(venn.plot);
# A more complicated diagram Demonstrating external area labels
grid.newpage();
venn.plot <- draw.pairwise.venn(
area1 = 100,
area2 = 70,
cross.area = 30,
category = c("First", "Second"),
fill = c("blue", "red"),
lty = "blank",
cex = 2,
cat.cex = 2,
cat.pos = c(285, 105),
cat.dist = 0.09,
cat.just = list(c(-1, -1), c(1, 1)),
ext.pos = 30,
ext.dist = -0.05,
ext.length = 0.85,
ext.line.lwd = 2,
ext.line.lty = "dashed"
);
grid.draw(venn.plot);
/# Three-set Venn diagram
grid.newpage();
venn.plot <- draw.triple.venn(
area1 = 72,
area2 = 86,
area3 = 50,
n12 = 44,
n13 = 27,
n23 = 38,
n123 = 18,
category = c("First", "Second", "Third"),
fill = c("orange", "red", "green"),
lty = "dashed",
cex = 2,
cat.cex = 2,
cat.col = c("orange", "red", "green")
);
/# Four-set Venn diagram
grid.newpage();
venn.plot <- draw.quad.venn(
area1 = 72,
area2 = 86,
area3 = 50,
area4 = 52,
n12 = 44,
n13 = 27,
n14 = 32,
n23 = 38,
n24 = 32,
n34 = 20,
n123 = 18,
n124 = 17,
n134 = 11,
n234 = 13,
n1234 = 6,
category = c("First", "Second", "Third", "Fourth"),
fill = c("orange", "red", "green", "blue"),
lty = "dashed",
cex = 2,
cat.cex = 2,
cat.col = c("orange", "red", "green", "blue")
);
/# Five-set Venn diagram
grid.newpage();
venn.plot <- draw.quintuple.venn(
area1 = 301,
area2 = 321,
area3 = 311,
area4 = 321,
area5 = 301,
n12 = 188,
n13 = 191,
n14 = 184,
n15 = 177,
n23 = 194,
n24 = 197,
n25 = 190,
n34 = 190,
n35 = 173,
n45 = 186,
n123 = 112,
n124 = 108,
n125 = 108,
n134 = 111,
n135 = 104,
n145 = 104,
n234 = 111,
n235 = 107,
n245 = 110,
n345 = 100,
n1234 = 61,
n1235 = 60,
n1245 = 59,
n1345 = 58,
n2345 = 57,
n12345 = 31,
category = c("A", "B", "C", "D", "E"),
fill = c("dodgerblue", "goldenrod1", "darkorange1", "seagreen3", "orchid3"),
cat.col = c("dodgerblue", "goldenrod1", "darkorange1", "seagreen3", "orchid3"),
cat.cex = 2,
margin = 0.05,
cex = c(1.5, 1.5, 1.5, 1.5, 1.5, 1, 0.8, 1, 0.8, 1, 0.8, 1, 0.8, 1, 0.8,
1, 0.55, 1, 0.55, 1, 0.55, 1, 0.55, 1, 0.55, 1, 1, 1, 1, 1, 1.5),
ind = TRUE
);
library("multtest"); library("annaffy"); library("hgu95av2.db");library("ALL")
library("genefilter")
data(ALL, package = "ALL")
ALLB <- ALL[,ALL$BT %in% c("B1","B2","B3","B4")]
f1 <- function(x) (shapiro.test(x)$p.value > 0.05)
sel1 <- genefilter(exprs(ALL[,ALLB$BT=="B1"]), filterfun(f1))
sel2 <- genefilter(exprs(ALL[,ALLB$BT=="B2"]), filterfun(f1))
sel3 <- genefilter(exprs(ALL[,ALLB$BT=="B3"]), filterfun(f1))
sel4 <- genefilter(exprs(ALL[,ALLB$BT=="B4"]), filterfun(f1))
selected <- sel1 & sel2 & sel3 & sel4
library(limma)
/# Three-set Venn diagram
x <- matrix(as.integer(c(sel2,sel3,sel4)),ncol = 3,byrow=FALSE)
colnames(x) <- c("sel2","sel3","sel4")
vc <- vennCounts(x, include="both")
vennDiagram(vc)
/# Four-set Venn diagram
x <- matrix(as.integer(c(sel1,sel2,sel3,sel4)),ncol = 4,byrow=FALSE)
colnames(x) <- c("sel1","sel2","sel3","sel4")
vc <- vennCounts(x, include="both")
vennDiagram(vc)