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plotall.R
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plotall.R
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library(igraph)
cancermodule=read.csv("edge509.csv")
#edgeweight=cancermodule[,4]
network <- graph.data.frame(cancermodule[,c(2,3)],directed = F)
fc <- cluster_fast_greedy(network)
a=membership(fc)
b=as.data.frame(t(sapply(a, "[", i = 1:max(sapply(a, length)))))
sizes(fc)
plot.membership<-function(graph,membership,main=""){
V(graph)$member<-membership
mem.col<-rainbow(length(unique(membership)),alpha=0.4)
V(graph)$width=100
V(graph)$color<-mem.col[membership]
V(graph)$size = 6
V(graph)$label=NA
plot(graph,edge.width=E(graph)$weight,vertex.color=V(graph)$color,main=main,layout=layout.lgl)
}
plot.membership(network,a)
library(pheatmap)
#创建数据集test测试矩阵
test = matrix(rnorm(200), 20, 10)
test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:20, sep = "")
# 用pheatmap函数画热图
pheatmap(test,cluster_row = FALSE, cluster_col = FALSE)
name <- "HiSeqV2_BRCA_outcome.txt"
data <- as.matrix(read.table(name,sep = "\t",header=T))
dim(data)
#17924*1218
normal=cbind()
cancer=cbind()
for (i in 1:1218) {
if(data[1,i]==1) {
normal=cbind(normal,data[,i])
}
else{
cancer=cbind(cancer,data[,i])
}
}
dim(normal)
dim(cancer)
module1node <-c('JUN', 'FOS', 'EGR1', 'JUND', 'JUNB', 'FOSB', 'MYC', 'FOSL1', 'ETS2', 'ATF3', 'ZFP36', 'HIC1', 'SCARF1', 'CSRNP1', 'TNXB', 'SERPINE1', 'GADD45B', 'IL6', 'ITGA7', 'NR4A3', 'LRRN4CL', 'USP2', 'DUSP2', 'PTGS2', 'PLK3', 'MAFF', 'CTGF', 'RGS2', 'KLF4', 'CDKN1A',"Label")
dat=data[,module1node]
dat=data.frame(dat)
normal=t(normal)
cancer=t(cancer)
heatmapnode<-c('JUN', 'FOS', 'EGR1', 'JUND', 'JUNB', 'FOSB', 'MYC', 'FOSL1', 'ETS2', 'ATF3', 'ZFP36', 'HIC1', 'SCARF1', 'CSRNP1', 'TNXB', 'SERPINE1', 'GADD45B', 'IL6', 'ITGA7', 'NR4A3', 'LRRN4CL', 'USP2', 'DUSP2', 'PTGS2', 'PLK3', 'MAFF', 'CTGF', 'RGS2', 'KLF4', 'CDKN1A')
normaldata=normal[,heatmapnode]
cancerdata=cancer[,heatmapnode]
pheatmap(cor(normaldata),cluster_row = FALSE, cluster_col = FALSE)
pheatmap(cor(cancerdata),cluster_row = FALSE, cluster_col = FALSE)
data=t(data)
library(ggplot2)
library(tidyr)
library(dplyr)
dat2 = gather(dat,key = "gene",value = "expression",-Label)
boxfigure=ggplot(data = dat2,aes(x = as.character(Label),y = expression,color = as.character(Label)))+
geom_boxplot()+
scale_y_continuous(name = "Expression")+
scale_x_discrete(name = "Gene") +guides(color = guide_legend(title = 'Condition'))+
scale_color_discrete(name = "Conditions", labels = c("Disease", "Normal"))+
theme_bw()
boxfigure+facet_wrap(~gene,nrow = 3)+
scale_color_manual(values=c("#8B2323", "#66CDAA"),
name="Condition",
breaks=c("0", "1"),
labels=c("Disease", "Normal"))
boxfigure+
geom_boxplot(alpha=0.7) +
ggtitle("Boxplot of hub gene") +
# Color by group (dose)
e + geom_boxplot(aes(color = dose))+
scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
# Change fill color by group (dose)
e + geom_boxplot(aes(fill = dose)) +
scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))
# Choose which items to display: group "0.5" and "2"
e + geom_boxplot() +
scale_x_discrete(limits=c("0.5", "2"))
# Change the default order of items
e + geom_boxplot() +
scale_x_discrete(limits=c("2", "0.5", "1"))
e2 <- e + geom_boxplot(
aes(fill = supp),
position = position_dodge(0.9)
) +
scale_fill_manual(values = c("#999999", "#E69F00"))
e2
p=list()
#list 列表将储存所有的循环绘图，以实现后面的多图组合
library(ggplot2)
for (i in 1:(ncol(dat)-1)){
#之所以减一，是因为最后一列是组类别
p[[i]]=ggplot(data=dat,aes_string(x="Label",y=colnames(dat)[i]))+
geom_boxplot(aes(color=as.character(Label))) +
geom_jitter(aes(color=as.character(Label)))
}
library(patchwork)
# 第一次使用需要安装
wrap_plots(p,nrow=2, guides="collect")
#富集分析
#标准富集分析
library(DOSE)
library(org.Hs.eg.db)
library(topGO)
library(clusterProfiler)
keytypes(org.Hs.eg.db)
x <- c('PGR', 'MYB', 'CTNNB1', 'UHRF1', 'IGF1R')
test = bitr(x, #数据集
fromType="SYMBOL", #输入为SYMBOL格式
toType="ENTREZID", # 转为ENTERZID格式
OrgDb="org.Hs.eg.db") #人类 数据库
head(test,2)
data(geneList, package="DOSE") #富集分析的背景基因集
gene <- names(geneList)[abs(geneList) > 2]
gene.df <- bitr(gene, fromType = "ENTREZID", toType = c("ENSEMBL", "SYMBOL"), OrgDb = org.Hs.eg.db)
head(gene.df,2)
ego_ALL <- enrichGO(gene = test$ENTREZID,
universe = names(geneList), #背景基因集
OrgDb = org.Hs.eg.db, #没有organism="human"，改为OrgDb=org.Hs.eg.db
#keytype = 'ENSEMBL',
ont = "ALL", #也可以是 CC BP MF中的一种
pAdjustMethod = "BH", #矫正方式 holm”, “hochberg”, “hommel”, “bonferroni”, “BH”, “BY”, “fdr”, “none”中的一种
pvalueCutoff = 1, #P值会过滤掉很多，可以全部输出
qvalueCutoff = 1,
readable = TRUE) #Gene ID 转成gene Symbol 易读
head(ego_ALL,2)
ego_MF <- enrichGO(gene = test$ENTREZID, universe = names(geneList),OrgDb = org.Hs.eg.db,ont = "MF", pAdjustMethod = "BH",pvalueCutoff = 1,qvalueCutoff = 1,readable = FALSE)
ego_MF1 <- setReadable(ego_MF, OrgDb = org.Hs.eg.db)
write.csv(as.data.frame(ego_ALL),"ALL-enrich3.csv",row.names =FALSE)
dotplot(ego_MF,title="Module3 EnrichmentGO")#点图，按富集的数从大到小的
##可视化--条形图
barplot(ego_MF, showCategory=20,title="Module3 EnrichmentGO")#条状图，按p从小到大排，绘制前20个Term