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step1.R
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step1.R
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load("~/backup/t8a/allimage_data/Sdeath202302.rds")
.libPaths(c("~/R/forMOVICS"))
.libPaths()
.libPaths(c(#"/home/data/aim/R/x86_64-pc-linux-gnu-library/aimold/Rlib",
"/home/data/aim/R/x86_64-pc-linux-gnu-library/4.3",
"/home/data/aim/R/x86_64-pc-linux-gnu-library/4.2",
"/usr/local/lib/R/library"
))
###新的分析流程
#更新绘图方式和配色
#颜色协调一致
###安排一套最完美代码
# .libPaths(c("/home/data/vip39/R/x86_64-pc-linux-gnu-library/4.1",
# "/home/data/refdir/Rlib/",
# "/home/data/vip39/R/x86_64-pc-linux-gnu-library/4.0",
# #"/home/data/vip39/R/x86_64-pc-linux-gnu-library/4.2/",
# "/usr/local/lib/R/library"))
# .libPaths(c("/home/data/refdir/Rlib/"))
library(RColorBrewer)
library(circlize)
library(gplots)
library(oompaBase)
library(viridis)
library(ggplot2)
library(clusterProfiler)
library(survival)
library(reshape2)
library(corrplot)
library(plyr)
library(igraph)
library(CMScaller)
#library(MOVICS,lib.loc = "~/R/forMOVICS/")
library(tidyverse)
library(MOVICS)
library(ggpubr)
library(maftools)
library(survminer)
library(pROC)
library(ConsensusClusterPlus)
library(ComplexHeatmap)
library(circlize)
library(pheatmap)
library(tidyverse)
library(stringr)
library(EnhancedVolcano)
library(data.table)
library(monocle)
library(DDRTree)
library(ggsci)
library(ggstatsplot)
library(viridis)
library(scales)
library(RTN)
library(gplots)
library(grid)
###数据读入
load("~/backup/t8a/2022backup/xuzijun/vip39/TCGA/Copper_death/Copper_all.rds")
##保存需要的变量即可
gene_list <- c("NUBPL","NDUFA11","LRPPRC","OXSM","NDUFS1","GYS1","SLC7A11","SLC3A2","RPN1","NCKAP1")
save()
load("~/backup/t8a/vip39database/database/tcga_counts_fpkm_tpm/TCGA-KIRC_tpm_gene_symbol.Rdata")
tpms <- as.data.frame(tpms)
tpms[1:4,1:4]
tpms <- as.data.frame(log2(tpms+1))
range(tpms)
colnames(tpms) <- str_sub(colnames(tpms),1,15)
##HR 单因素多因素 多个KM in RCC###
Coxoutput <- data.frame(OS=sub$EVENT,
OS.time=sub$OS.time)
rownames(Coxoutput) <- sub$Sample
Coxoutput2 <- as.data.frame(t(tpms[unique(gene_list),sub$Sample]))
Coxoutput <- cbind(Coxoutput,Coxoutput2)
Coxoutput[1:4,1:4]
realdata <- Coxoutput
realdata[1:3,1:6]
setwd("~/Desktop/TCGA_work/vip33new/Sdeath_project/")
dir.create("COX")
setwd("./COX/")
Coxoutput=data.frame()
for(i in colnames(realdata[,3:ncol(realdata)])){
cox <- coxph(Surv(OS.time, OS) ~ realdata[,i], data = realdata)
coxSummary = summary(cox)
Coxoutput=rbind(Coxoutput,cbind(gene=i,HR=coxSummary$coefficients[,"exp(coef)"],
z=coxSummary$coefficients[,"z"],
pvalue=coxSummary$coefficients[,"Pr(>|z|)"],
lower=coxSummary$conf.int[,3],
upper=coxSummary$conf.int[,4]))
}
for(i in c(2:6)){
Coxoutput[,i] <- as.numeric(as.vector(Coxoutput[,i]))
}
# Coxoutput <- arrange(Coxoutput,pvalue) %>% #按照p值排序
# filter(pvalue < 0.01)
#Coxoutput <- arrange(Coxoutput,pvalue)
#Coxoutput <- Coxoutput[Coxoutput$pvalue<0.01,]
Coxoutput[1:4,1:4]
input_data <- Coxoutput2
input_data[1:4,1:4]
corr <- cor(input_data, method = "spearman")
corrplot(corr,title = "",
method = "pie", #或"circle" (default), "square", "ellipse", "number", "pie", "shade" and "color"
outline = T, addgrid.col = "darkgray",
order="hclust", addrect = 4, #hclust聚为4类，根据数据的具体情况调整
mar = c(4,0,4,0), #撑大画布，让细胞名显示完全
rect.col = "black", rect.lwd = 5, cl.pos = "b",
tl.col = "black", tl.cex = 1.08, cl.cex = 1.5, tl.srt=60)
cor.mtest <- function(corr, ...) {
corr <- as.matrix(corr)
n <- ncol(corr)
p.corr <- matrix(NA, n, n)
diag(p.corr) <- 0
for (i in 1:(n - 1)) {
for (j in (i + 1):n) {
tmp <- cor.test(corr[, i],method = "spearman", corr[, j], ...)
p.corr[i, j] <- p.corr[j, i] <- tmp$p.value
}
}
colnames(p.corr) <- rownames(p.corr) <- colnames(corr)
p.corr
}
p.corr <- cor.mtest(input_data)
head(p.corr[, 1:5])
#合并相关系数和P值
rr <- as.data.frame(corr)
rr$ID <- rownames(rr)
cor <- melt(rr,"ID",value.name = "cor"); #head(cor)
pp <- as.data.frame(p.corr)
pp$ID <- rownames(pp)
pvalue <- melt(pp,"ID",value.name = "pvalue"); #head(pvalue)
colnames(pvalue) <- c("from","to","pvalue")
corpvlue <- cbind(pvalue, cor)
head(corpvlue)
corpvlue <- corpvlue[, -c(4:5)]
head(corpvlue)
dim(corpvlue)
#去掉相关性较弱的连接
corpvlue <- corpvlue[corpvlue$pvalue < 0.0001,] #只保留pvalue < 0.0001的
dim(corpvlue)
corpvlue$weight <- corpvlue$pvalue
corpvlue$weight <- -log10(corpvlue$weight)
head(corpvlue)
#去掉相关系数为1，也就是两个相同变量之间的连接
corpvlue <- corpvlue[!corpvlue$cor==1,]
dim(corpvlue)
#去掉相关系数一样的连接--也就是重复计算的连接
summary(duplicated(corpvlue$weight))
corpvlue <- corpvlue[!duplicated(corpvlue$weight),]
dim(corpvlue)
#相关系数的正负用不同颜色表示
corpvlue$color <- ifelse(corpvlue$cor<0, negcol, poscol)
#保存到文件，便于查看
write.csv(corpvlue, "output_links.csv")
##进行聚类 细胞聚类 或者gene 聚类 可不运行
# cellcluster <- as.data.frame(t(input_data))
# #cellcluster[1:5,1:5]
# hc <- hclust(dist((cellcluster)))
# hcd <- as.dendrogram(hc)
# (clus4 <- cutree(hc, 4)) #分4类
# A <- as.character(rownames(as.data.frame(subset(clus4,clus4==1))))
# B <- as.character(rownames(as.data.frame(subset(clus4,clus4==2))))
# C <- as.character(rownames(as.data.frame(subset(clus4,clus4==3))))
# D <- as.character(rownames(as.data.frame(subset(clus4,clus4==4))))
# cls <- list(A,B,C,D)
#
# nodes <- as.data.frame(unlist(cls))
# nodes$type <- c(rep("B",9),rep("A",4),rep("C",5),rep("D",5))
# names(nodes) <- c("media","type.label")
#
# #以hclust的结果为基础，调整部分细胞所属的cluster
# nodes$type.label[nodes$media=="T cells follicular helper"] <- "B"
# nodes$type.label[nodes$media=="B cells naive"] <- "A"
# nodes$type.label[nodes$media=="T cells CD4 naive"] <- "A"
# nodes$type.label[nodes$media=="Plasma cells"] <- "A"
# nodes$type.label[nodes$media=="Dendritic cells resting"] <- "C"
# nodes$type.label[nodes$media=="Eosinophils"] <- "C"
# nodes$type.label[nodes$media=="Mast cells resting"] <- "A"
#
# nodes <- as.data.frame(nodes)
# nodes$media <- as.character(nodes$media)
# nodes
nodes <- fread("/home/aim/Desktop/TCGA_work/vip33new/input/media_cor.csv",header = T,data.table = F)
nodes
# 合并生存分析的数据和细胞分类的数据
bb <- Coxoutput
bb$ID <- Coxoutput$gene
#bb$Cell.types <- as.character(bb$Cell.types)
#colnames(bb)[1] <- c("ID")
#用pvalue控制节点圆的大小
bb$weight <- abs(log10(bb$pvalue))
#用HR标圆心点的颜色
bb$weight_HR <- (as.numeric(bb$HR)-1)*100
bb$colr <- ifelse(bb$weight_HR<0, "green", "black")
head(bb)
summary(nodes$media %in% Coxoutput$gene)
summary(nodes$media %in% bb$ID) #检查细胞名是否一致
nodes <- merge(nodes, bb, by.x = "media", "ID", all.x = T, all.y = T) #按细胞名merge
nodes$Fraction <- abs(nodes$weight_HR)
nodes$id <- paste("S", 01:10, sep = "")
nodes <- nodes[order(nodes$type.label),]
nodes <- nodes[,c(ncol(nodes),1:ncol(nodes)-1)]
nodes <- nodes[order(nodes$type.label),]
nodes
#建立nodes和links的连接id，把细胞名换成ID
paste0("'",nodes$media,"'","=","'",nodes$id,"'",collapse = ",")
corpvlue$from <- revalue(corpvlue$from,c('NCKAP1'='S3','RPN1'='S8','SLC3A2'='S9',
'SLC7A11'='S10','GYS1'='S1','LRPPRC'='S2',
'NDUFA11'='S4','NDUFS1'='S5','NUBPL'='S6','OXSM'='S7'))
corpvlue$to <- revalue(corpvlue$to,c('NCKAP1'='S3','RPN1'='S8','SLC3A2'='S9',
'SLC7A11'='S10','GYS1'='S1','LRPPRC'='S2',
'NDUFA11'='S4','NDUFS1'='S5','NUBPL'='S6','OXSM'='S7'))
(links <- corpvlue)
View(links)
links[24,6]
dim(links)
#利用nodes和links构建网络的input文件
net <- graph_from_data_frame(d=links, vertices=nodes, directed=T)
#table(links$from%in%nodes$media)
# Generate colors based on cell clusters:
V(net)$color <- revalue(nodes$type.label,c("A"=mycol[1],"B"=mycol[2]))
# Compute node degrees (#links) and use that to set node size:
# Set edge width based on weight-log10(p_value):
V(net)$size <- (1 + V(net)$weight)*3 #节点圆的大小，可根据自己的数据再做调整
V(net)$label <- V(net)$media #设置标签
E(net)$arrow.mode <- 0 #不需要箭头
E(net)$edge.color <- "tomato" # tomato gray80
E(net)$width <- 1+E(net)$weight/6 #连接之间权重
pdf("Immune_network.pdf", width = 9.75, height = 8.78 )
plot(net,
layout=layout_in_circle, #按圆圈布局
edge.curved=.2, #画弯曲的连线
vertex.label.color=V(net)$color, #细胞名的颜色
vertex.label.dist= -2, #标签和节点的位置错开，后期还是要用AI调整
edge.color=links$color)
#cluster的图例
legend("topright", #图例的位置
c("Cell cluster-A", "Cell cluster-B"),
pch=21, col="black", pt.bg=mycol, pt.cex=3,
cex=1.3, bty="n", ncol=1)
#节点圆大小的图例，参考了FigureYa75base_volcano
f <- c(0.05, 0.001, 0.00001, 0.00000001)
s <- sqrt(abs(log10(f)))*3
legend("bottomright",
inset=c(0,-.1), #向下移
legend=f, text.width = .2,
title = "logrank test, P value", title.adj = -.5,
pch=21, pt.cex=s, bty='n',
horiz = TRUE, #横向排列
col = "black")
#连线的图例
legend("bottomright",
c("Positive correlation with P < 0.0001",
"Negative correlation with P < 0.0001"),
col = c(poscol, negcol), bty="n",
cex = 1, lty = 1, lwd = 5)
dev.off()
###cluster####
setwd("~/Desktop/TCGA_work/vip33new/Sdeath_project/")
##提取出T1到T3
table(sub$pstage)
# T1 T2 T3 T4
# 264 68 173 11
expr <- tpms[gene_list,sub$Sample]
dim(expr)
setwd("./result/")
dir.create('ConsensusCluster/')
results = ConsensusClusterPlus(as.matrix(expr),
maxK=9,
reps=100,
pItem=0.8,
pFeature=1,
title='ConsensusCluster/',
clusterAlg="km",
distance="euclidean",
seed=123456,
plot="png")
Kvec = 2:9
x1 = 0.1; x2 = 0.9
PAC = rep(NA,length(Kvec))
names(PAC) = paste("K=",Kvec,sep="")
for(i in Kvec){
M = results[[i]]$consensusMatrix
Fn = ecdf(M[lower.tri(M)])
PAC[i-1] = Fn(x2) - Fn(x1)
}
optK = Kvec[which.min(PAC)]
optK
#[1] 4
PAC <- as.data.frame(PAC)
PAC$K <- 2:9
library(ggplot2)
ggplot(PAC,aes(factor(K),PAC,group=1))+
geom_line()+
theme_bw()+theme(panel.grid = element_blank())+
geom_point(size=4,shape=21,color='darkred',fill='skyblue')+
ylab('Proportion of ambiguous clustering')+
xlab('Cluster number K')
library(export)
library(eoffice)
ggsave(filename = "ConsensusCluster/PAC.pdf",width = 6,height = 5)
icl <- calcICL(results,title = 'ConsensusCluster/',plot = 'png')
clusterNum=4
cluster=results[[clusterNum]][["consensusClass"]]
sub <- data.frame(Sample=names(cluster),Cluster=cluster)
sub$Cluster <- paste0('C',sub$Cluster)
table(sub$Cluster)
# C1 C2 C3 C4
# 92 167 74 183
head(sub)
rownames(sub) <- sub$Sample
sub$OS <- as.vector(allclin[rownames(sub),"OS"])
sub$OStime <- as.vector(allclin[rownames(sub),"OS.time"])
sub <- sub[sub$OS>30,]
class(sub)
meta <- sub[,1:4]
head(sub)
colnames(meta) <- c("ID","cluster","event","time")
meta <- meta[,-1]
meta$time <- meta$time/365
head(meta)
sfit <- survfit(Surv(time, event)~cluster, data=meta)
ggsurvplot(sfit, conf.int=F, pval=TRUE)
sfit <- survfit(Surv(OS,EVENT) ~ Cluster,data = sub)
ggsurvplot(sfit, conf.int=F, pval=TRUE)
sfit <- survfit(Surv(OS,EVENT)~Cluster,data = sub)
sfit <- survfit(Surv(OS.time,EVENT)~Cluster,data = sub)
sub$PFI <- allclin[rownames(sub),"PFI"]
sub$PFI.time <- allclin[rownames(sub),"PFI.time"]
sfit <- survfit(Surv(PFI.time, PFI)~Cluster, data=sub)
mytheme <- theme_survminer(font.legend = c(14,"plain", "black"),
font.x = c(14,"plain", "black"),
font.y = c(14,"plain", "black"))
ggsurvplot(sfit,
#data = sub,
palette= c(pal_nejm()(4),'grey60'),
conf.int=FALSE,size=1.3,
pval=T,pval.method = T,
legend.labs=c("C1","C2","C3","C4"),
legend.title="cluster",
xlab="Time (years)",
#ylab='Overall survival',
ylab='Overall survival',
risk.table=TRUE,
#break.time.by = 2,
#risk.table.title="Number at risk",
risk.table.height=.3,
risk.table.y.text = FALSE,
ggtheme = mytheme)
###PFI
meta$PFI <- allclin[rownames(meta),"PFI"]
meta$PFI.time <- allclin[rownames(meta),"PFI.time"]/365
sfit2 <- survfit(Surv(PFI.time, PFI)~cluster, data=meta)
ggsurvplot(sfit2,
palette= c(pal_nejm()(4),'grey60'),
conf.int=FALSE,size=1.3,
pval=T,pval.method = T,
legend.labs=c("C1","C2","C3","C4"),
legend.title="cluster",
xlab="Time (years)",
ylab='Progression Free Survival',
risk.table=TRUE,
#break.time.by = 2,
#risk.table.title="Number at risk",
risk.table.height=.3,
risk.table.y.text = FALSE,
ggtheme = mytheme)
ggsave(filename = "PFI_twogroup.pdf",height = 6,width = 6)
#加上正常组织三组绘图
dir.create("complexheatmap")
setwd("/home/aim/Desktop/TCGA_work/vip33new/Sdeath_project/result/complexheatmap/")
Sys.setenv(LANGUAGE = "en") #显示英文报错信息
options(stringsAsFactors = FALSE) #禁止chr转成factor
###准备两个数据 mygene_data 和 Subtype 分组信息
normal_id <- colnames(KIRC_mRNA_fpkm)[str_sub(colnames(KIRC_mRNA_fpkm),14,15)=="11"]
c(normal_id,sub$Sample)
Subtype <- data.frame(Subtype=c(rep("N",length(normal_id)),sub$Cluster),
id=c(normal_id,rownames(sub)))
rownames(Subtype) <- Subtype$id
Subtype <- Subtype[-2]
tpms[1:4,1:4]
table(rownames(Subtype)%in%colnames(tpms))
table(gene_list%in%rownames(tpms))
mygene_data <- tpms[gene_list,rownames(Subtype)]
mygene_data[1:4,1:4]
#Subtype <- Subtype[com_sam,,drop = F]
head(Subtype)
table(Subtype$Subtype)
## 用前面的自定义函数计算组间统计差异
comprTab <- cross_subtype_compr(expr = mygene_data, # 或log2(mygene_data + 1)，如果用参数检验，请注意对数转化；若非参均可
subt = Subtype,
#two_sam_compr_method = "wilcox", # 两组"t.test", "wilcox"
multi_sam_compr_method = "kruskal", # 多组"anova", "kruskal"
res.path = ".")
# 用全部基因来画
n.show_top_gene <- nrow(mygene_data)
# 按分组排序
subt.order <- Subtype[order(Subtype$Subtype),,drop = F]
indata <- mygene_data[comprTab$gene[1:n.show_top_gene],rownames(subt.order)]
# 数据标准化和边界设置
plotdata <- t(scale(t(indata)))
plotdata[plotdata > 2] <- 2
plotdata[plotdata < -2] <- -2
# 调整行名
blank <- " " # 行名和p值之间的间隔
p.value <- comprTab$adjusted.p.value[1:n.show_top_gene]
sig.label <- ifelse(p.value < 0.001,"****",
ifelse(p.value < 0.005,"***",
ifelse(p.value < 0.01,"**",
ifelse(p.value < 0.05,"*",""))))
p.label <- formatC(p.value, # 将p值变成保留两位小数的科学计数法
format = "e",
digits = 2)
add.label <- str_pad(paste0(rownames(plotdata),sig.label), # 固定行名宽度并再右侧补齐" "
max(nchar(paste0(rownames(plotdata),sig.label))),
side = "right")
annCol <- subt.order # 获得排序后的亚型注释信息，这里只有一个变量需要注释
colnames(annCol)[1] <- paste(str_pad(colnames(annCol)[1], # 注释列名补上"P-value"，宽度和刚才一致
max(nchar(paste0(rownames(plotdata),sig.label))),
side = "right"),
"P-value",
sep = blank)
annColors <- list(c( "C1"="#BC3C29FF", "C2"="#0072B5FF","C3"="#E18727FF","C4"="#7876B1FF","N"="#33A02CFF")) # 如果有多个变量要注释颜色请补充c()
names(annColors) <- colnames(annCol)[1] # 如果有多个变量要注释颜色请补充每张list的name
# 绘制热图
table(Subtype$Subtype)
pheatmap(cellheight = 10, cellwidth = 1,
mat = plotdata, # 输入数据
scale = "none", # 不标准化因为数据已经被标准化
annotation_col = annCol, # 列注释信息
annotation_colors = annColors, # 列注释对应的颜色
cluster_cols = F, # 列不聚类
cluster_rows = F, # 行不聚类
show_colnames = F, # 不显示列名
show_rownames = T, # 显示基因名
annotation_legend = F, # 不显示图例
gaps_col = c(92,259,333,516),
color = paletteer_c("scico::berlin", n = 100),
labels_row = paste(add.label, p.label, sep=blank),
)
table(Subtype$Subtype)
####clinical heatmap+ table +subtype HR####
#View(rt)
rt <- sub[,c("Sample","Cluster")]
head(rt)
rt <- rt[-1]
colnames(rt) <- "risk"
rt=rt[order(rt$risk),,drop=F]
View(allclin)
rt$age <- allclin[rownames(rt),4]
rt$age <-ifelse(rt$age<=62,'<=65','>65')
rt$age=factor(rt$age,labels = c('<=65','>65'))
rt$gender <- allclin[rownames(rt),5]
table(rt$gender)
rt$gender <- factor(rt$gender,labels=c("MALE", "FEMALE"))
rt$T <- str_sub(pd[rownames(rt),"pathologic_T"],1,2)
table(rt$T)
rt$T <- factor(rt$T,labels=c("T1", "T2", "T3", "T4"))
rt$N <- str_sub(pd[rownames(rt),"pathologic_N"],1,2)
table(rt$N)
rt$N <- gsub("NX","unknown",rt$N)
rt$N <- factor(rt$N,labels=c("N0", "N1", "unknown"))
rt$M <- str_sub(pd[rownames(rt),"pathologic_M"],1,2)
table(rt$M)
rt$M <- gsub("MX","unknown",rt$M)
rt$M <- factor(rt$M,labels=c("M0", "M1","unknown"))
rt$stage <- allclin[rownames(rt),7]
table(rt$stage)
rt$stage <- gsub("\\[Discrepancy]","unknown",rt$stage)
table(rt$stage)
rt$stage <- factor(rt$stage,labels=c("Stage I", "Stage II", "Stage III", "Stage IV","unknown"))
myCol=c(
'#98D5F4', '#3B7EF4', '#FEEDDE','#E6550D', '#B5B4B4', '#727270', '#444746', '#070707', '#00DBA0',
'#15788E', '#C45132', '#EDF8E9', '#31A354' ,'#B5B4B4', '#727270', '#444746', '#070707')
ColorList=list(risk=c("C1"="#BC3C29FF", "C2"="#0072B5FF","C3"="#E18727FF","C4"="#7876B1FF"))
i=0
for(cli in colnames(rt[,2:ncol(rt)])){
cliLength=length(levels(factor(rt[,cli])))
cliCol=myCol[(i+1):(i+cliLength)]
i=i+cliLength
names(cliCol)=levels(factor(rt[,cli]))
cliCol["unknown"]="grey75"
ColorList[[cli]]=cliCol
}
library(ComplexHeatmap)
ha=HeatmapAnnotation(df=rt, col=ColorList)
zero_row_mat=matrix(nrow=0, ncol=nrow(rt))
Hm=Heatmap(zero_row_mat, top_annotation=ha,)
#输出热图
pdf(file="heatmap_clincal.pdf", width=7, height=3)
draw(Hm, merge_legend = TRUE, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
dev.off()
###差异分析适合MOVICS分析 四组####
load("~/database/kirc.tcga5_omics.rds")
sub$pstage <- pd[sub$Sample,"pathologic_T"]
sub$pstage <- str_sub(sub$pstage,1,2)
head(sub)
newsub <- sub
newsub <- sub[c(1,2,3,6,7)]
newsub <- sub[c(1,2,3,4,7)]
head(newsub)
colnames(newsub) <- c("samID","cluster","fustat","futime","pstage")
head(newsub)
newsub$clust <-str_sub(newsub$cluster,2,2)
head(newsub)
id258 <- colnames(kirc.tcga5_omics$mRNA.expr)
table(id258%in%rownames(newsub))##只有247个
inter_sam <- id258[id258%in%rownames(newsub)]
inter_sub <- newsub[inter_sam,]
pseudo.moic.res<- list("clust.res" = inter_sub,"mo.method" = "PAM50")
# survival comparison
pam50.brca <- compSurv(moic.res = pseudo.moic.res,
surv.info = surv.info,
convt.time = "y", # convert day unit to year
surv.median.line = "h", # draw horizontal line at median survival
fig.name = "KAPLAN-MEIER CURVE OF PAM50 BY PSEUDO")
cmoic.brca <- kirc.tcga5_omics[1:5]
tmb.brca <- compTMB(moic.res = pseudo.moic.res,
maf = kirc.tcga5_omics$maf,
rmDup = TRUE, # remove duplicated variants per sample
rmFLAGS = FALSE, # keep FLAGS mutations
exome.size = 38, # estimated exome size
test.method = "nonparametric", # statistical testing method
fig.name = "DISTRIBUTION OF TMB AND TITV")
colnames(segment) <- c("sample","chrom","start","end","value")
head(segment)
segment <- kirc.tcga5_omics$segment
segment <- as.data.frame(segment)
segment <- segment[segment$sample%in%inter_sam,]
fga.brca <- compFGA(moic.res = pseudo.moic.res,
segment = segment,
iscopynumber = F, # this is a segmented copy number file
cnathreshold = 0.2, # threshold to determine CNA gain or loss
test.method = "nonparametric", # statistical testing method
fig.name = "BARPLOT OF FGA")
maf <- kirc.tcga5_omics$mut.status[,colnames(kirc.tcga5_omics$mut.status)%in%inter_sam]
mut.brca <- compMut(moic.res = pseudo.moic.res,
mut.matrix = kirc.tcga5_omics$mut.status, # binary somatic mutation matrix
doWord = TRUE, # generate table in .docx format
doPlot = TRUE, # draw OncoPrint
freq.cutoff = 0.05, # keep those genes that mutated in at least 5% of samples
p.adj.cutoff = 0.05, # keep those genes with adjusted p value < 0.05 to draw OncoPrint
innerclust = TRUE, # perform clustering within each subtype
#annCol = annCol, # same annotation for heatmap
#annColors = annColors, # same annotation color for heatmap
width = 6,
height = 5,
fig.name = "ONCOPRINT FOR SIGNIFICANT MUTATIONS",
tab.name = "INDEPENDENT TEST BETWEEN SUBTYPE AND MUTATION")
drug.brca <- compDrugsen(moic.res = pseudo.moic.res,
norm.expr = KIRC_mRNA_fpkm[,pseudo.moic.res$clust.res$samID], # double guarantee sample order
drugs = c("Sunitinib","Afatinib","Erlotinib","Gefitinib"), # a vector of names of drug in GDSC
tissueType = "urogenital_system", # choose specific tissue type to construct ridge regression model
test.method = "nonparametric", # statistical testing method
prefix = "BOXVIOLIN OF ESTIMATED IC50")
drug.brca <- compDrugsen(moic.res = pseudo.moic.res,
norm.expr = KIRC_mRNA_fpkm[,pseudo.moic.res$clust.res$samID], # double guarantee sample order
drugs = c("Imatinib","Crizotinib","Saracatinib","Dasatinib","Lisitinib"), # a vector of names of drug in GDSC
tissueType = "urogenital_system", # choose specific tissue type to construct ridge regression model
test.method = "nonparametric", # statistical testing method
prefix = "BOXVIOLIN OF ESTIMATED IC50 ")
###完善版本
drug.brca <- compDrugsen(moic.res = pseudo.moic.res,
norm.expr = KIRC_mRNA_fpkm[,pseudo.moic.res$clust.res$samID], # double guarantee sample order
drugs = c("Saracatinib","Crizotinib","Axitinib","Erlotinib","Pazopanib","Temsirolimus"), # a vector of names of drug in GDSC
tissueType = "urogenital_system", # choose specific tissue type to construct ridge regression model
test.method = "nonparametric", # statistical testing method
prefix = "BOXVIOLIN OF ESTIMATED IC50 ")
dir.create("MOVICS")
setwd("./MOVICS")
runDEA(dea.method = "edger",
expr = KIRC_mRNA_count, # raw count data
moic.res = pseudo.moic.res,
prefix = "TCGA-KIRC") # prefix of figure name
marker.up <- runMarker(moic.res = pseudo.moic.res,
dea.method = "edger", # name of DEA method
prefix = "TCGA-KIRC", # MUST be the same of argument in runDEA()
dat.path = getwd(), # path of DEA files
res.path = getwd(), # path to save marker files
p.cutoff = 0.05, # p cutoff to identify significant DEGs
p.adj.cutoff = 0.05, # padj cutoff to identify significant DEGs
dirct = "up", # direction of dysregulation in expression
n.marker = 100, # number of biomarkers for each subtype
doplot = TRUE, # generate diagonal heatmap
norm.expr = KIRC_mRNA_fpkm, # use normalized expression as heatmap input
#annCol = annCol, # sample annotation in heatmap
#annColors = annColors, # colors for sample annotation
show_rownames = FALSE, # show no rownames (biomarker name)
fig.name = "UPREGULATED BIOMARKER HEATMAP")
head(marker.up$templates)
marker.down <- runMarker(moic.res = pseudo.moic.res,
dea.method = "edger", # name of DEA method
prefix = "TCGA-KIRC", # MUST be the same of argument in runDEA()
dat.path = getwd(), # path of DEA files
res.path = getwd(), # path to save marker files
p.cutoff = 0.05, # p cutoff to identify significant DEGs
p.adj.cutoff = 0.05, # padj cutoff to identify significant DEGs
dirct = "down", # direction of dysregulation in expression
n.marker = 100, # number of biomarkers for each subtype
doplot = TRUE, # generate diagonal heatmap
norm.expr = KIRC_mRNA_fpkm, # use normalized expression as heatmap input
#annCol = annCol, # sample annotation in heatmap
#annColors = annColors, # colors for sample annotation
show_rownames = FALSE, # show no rownames (biomarker name)
fig.name = "DOWNREGULATED BIOMARKER HEATMAP")
head(marker.down$templates)
MSIGDB.FILE <- system.file("extdata", "c5.bp.v7.1.symbols.xls", package = "MOVICS", mustWork = TRUE)
gsea.dn <- runGSEA(moic.res = pseudo.moic.res,
dea.method = "edger",
prefix = "TCGA-KIRC",
msigdb.path = MSIGDB.FILE,
norm.expr = KIRC_mRNA_fpkm,
dirct = "down",
p.cutoff = 0.05,
p.adj.cutoff = 0.25,
gsva.method = "ssgsea", # switch to ssgsea
norm.method = "median", # switch to median
fig.name = "DOWNREGULATED PATHWAY HEATMAP")
gsea.up <- runGSEA(moic.res = pseudo.moic.res,
dea.method = "edger",
prefix = "TCGA-KIRC",
msigdb.path = MSIGDB.FILE,
norm.expr = KIRC_mRNA_fpkm,
dirct = "up",
p.cutoff = 0.05,
p.adj.cutoff = 0.25,
gsva.method = "ssgsea", # switch to ssgsea
norm.method = "median", # switch to median
fig.name = "UPREGULATED PATHWAY HEATMAP")
CC <- ""
gsea.dn <- runGSEA(moic.res = pseudo.moic.res,
dea.method = "edger",
prefix = "TCGA-KIRC",
msigdb.path = CC,
norm.expr = KIRC_mRNA_fpkm,
dirct = "down",
p.cutoff = 0.05,
p.adj.cutoff = 0.25,
gsva.method = "ssgsea", # switch to ssgsea
norm.method = "median", # switch to median
fig.name = "DOWNREGULATED PATHWAY HEATMAP")
gsea.up <- runGSEA(moic.res = pseudo.moic.res,
dea.method = "edger",
prefix = "TCGA-KIRC",
msigdb.path = CC,
norm.expr = KIRC_mRNA_fpkm,
dirct = "up",
p.cutoff = 0.05,
p.adj.cutoff = 0.25,
gsva.method = "ssgsea", # switch to ssgsea
norm.method = "median", # switch to median
fig.name = "UPREGULATED PATHWAY HEATMAP")
###Dasatinib 白血病
###Linsitinib 肾上腺肿瘤
##Saracatinib renal cancer
###Crizotinib 乳头状肾癌 https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(21)00152-5/fulltext
##Imatinib 白血病
##Axitinib 肾癌
##Sunitinib 肾癌
##Afatinib 肺癌
###Erlotinib 可用于特殊肾癌
## Gefitinib 局部晚期或转移性非小细胞肺癌(NSCLC)
##Pazopanib 晚期肾癌
##Temsirolimus 肾癌
# convert beta value to M value for stronger signal
indata <- kirc.tcga5_omics[c(1,2,4,5)]
indata$meth.beta <- log2(indata$meth.beta / (1 - indata$meth.beta))
# data normalization for heatmap
feat <- iClusterBayes.res$feat.res
feat1 <- feat[which(feat$dataset == "mRNA.expr"),][1:10,"feature"]
feat2 <- feat[which(feat$dataset == "lncRNA.expr"),][1:10,"feature"]
feat3 <- feat[which(feat$dataset == "meth.beta"),][1:10,"feature"]
feat4 <- feat[which(feat$dataset == "mut.status"),][1:10,"feature"]
annRow <- list(feat1, feat2, feat3, feat4)
plotdata <- getStdiz(data = indata,
halfwidth = c(2,2,2,NA), # no truncation for mutation
centerFlag = c(T,T,T,F), # no center for mutation
scaleFlag = c(T,T,T,F)) # no scale for mutation
mRNA.col <- c("#00FF00", "#008000", "#000000", "#800000", "#FF0000")
lncRNA.col <- c("#6699CC", "white" , "#FF3C38")
meth.col <- c("#0074FE", "#96EBF9", "#FEE900", "#F00003")
mut.col <- c("grey90" , "black")
col.list <- list(mRNA.col, lncRNA.col, meth.col, mut.col)
# comprehensive heatmap (may take a while)
getMoHeatmap(data = plotdata,
row.title = c("mRNA","lncRNA","Methylation","Mutation"),
is.binary = c(F,F,F,T), # the 4th data is mutation which is binary
legend.name = c("mRNA.FPKM","lncRNA.FPKM","M value","Mutated"),
clust.res = pseudo.moic.res$clust.res, # cluster results
clust.dend = NULL, # no dendrogram
show.rownames = c(F,F,F,F), # specify for each omics data
show.colnames = FALSE, # show no sample names
annRow = annRow, # mark selected features
color = col.list,
annCol = NULL, # no annotation for samples
annColors = NULL, # no annotation color
width = 10, # width of each subheatmap
height = 5, # height of each subheatmap
fig.name = "COMPREHENSIVE HEATMAP OF ICLUSTERBAYES")
##GSVA_免疫
GSET.FILE <-
system.file("extdata", "gene sets of interest.gmt", package = "MOVICS", mustWork = TRUE)
# run GSVA to estimate single sample enrichment score based on given gene set of interest
gsva.res <-
runGSVA(moic.res = pseudo.moic.res,
norm.expr = KIRC_mRNA_fpkm,
gset.gmt.path = GSET.FILE, # ABSOLUTE path of gene set file
gsva.method = "gsva", # method to calculate single sample enrichment score
# annCol = annCol,
#annColors = annColors,
#color =magma(64),
fig.path = getwd(),
show_rownames = T,
fig.name = "GENE SETS OF INTEREST HEATMAP",
height = 5,
width = 8)
###加上其他的数据集IOBR
library(IOBR)
IOBR::signature_metabolism
metabolism <- IOBR::signature_metabolism
GSET.FILE2 <- "/t8a/2022backup/xuzijun/vip39/TCGA/jiaowang_KIRC/met_model/data/try.gmt"
gsva.res <-
runGSVA(moic.res = pseudo.moic.res,
norm.expr = KIRC_mRNA_fpkm,
gset.gmt.path = GSET.FILE2, # ABSOLUTE path of gene set file
gsva.method = "gsva", # method to calculate single sample enrichment score
# annCol = annCol,
#annColors = annColors,
fig.path = getwd(),
show_rownames = T,
fig.name = "GENE SETS OF daixie HEATMAP",
height = 20,
width = 8)
get_gmt <- function(gmtinput,filename){
output <- file(filename, open="wt")
lapply(gmtinput[["name"]],function(name){
outlines = paste0(c(name, gmtinput[["description"]][[name]],
gmtinput[["genes"]][[name]]),collapse='\t')
writeLines(outlines, con=output)
})
close(output)
}
write.table(metabolism,file = "./data/Hallmark_my_geneset.gmt",sep = "\t",row.names = F,col.names = F,quote = F)
unlist(metabolism)
lst <- metabolism
library(GSEABase)
gsc <- GeneSetCollection(mapply(function(geneIds, keggId) {
GeneSet(geneIds, geneIdType=EntrezIdentifier(),
collectionType=KEGGCollection(keggId),
setName=keggId)
}, lst, names(lst)))
gsc
write.table(gsc,file = "./data/Hallmark_my_geneset.gmt")
sink("./data/try.gmt")
yourlist <- metabolism
for (i in 1:length(yourlist)){
cat(names(yourlist)[i])
cat('\tNA\t')
cat(paste(yourlist[[i]], collapse = '\t'))
cat('\n')
}
sink()
tme <- IOBR::signature_tme
sink("./data/tme.gmt")
yourlist <- tme
for (i in 1:length(yourlist)){
cat(names(yourlist)[i])
cat('\tNA\t')
cat(paste(yourlist[[i]], collapse = '\t'))
cat('\n')
}
sink()
GSET.FILE3 <- "/t8a/2022backup/xuzijun/vip39/TCGA/jiaowang_KIRC/met_model/data/tme.gmt"
gsva.res <-
runGSVA(moic.res = pseudo.moic.res,
norm.expr = KIRC_mRNA_fpkm,
gset.gmt.path = GSET.FILE3, # ABSOLUTE path of gene set file
gsva.method = "gsva", # method to calculate single sample enrichment score
# annCol = annCol,
#annColors = annColors,
fig.path = getwd(),
show_rownames = T,
fig.name = "GENE SETS OF tme HEATMAP",
height = 20,
width = 8)
tumor <- IOBR::signature_tumor
sink("./data/tumor.gmt")
yourlist <- tumor
for (i in 1:length(yourlist)){
cat(names(yourlist)[i])
cat('\tNA\t')
cat(paste(yourlist[[i]], collapse = '\t'))
cat('\n')
}
sink()
GSET.FILE4 <- "/t8a/2022backup/xuzijun/vip39/TCGA/jiaowang_KIRC/met_model/data/tumor.gmt"
gsva.res <-
runGSVA(moic.res = pseudo.moic.res,
norm.expr = KIRC_mRNA_fpkm,
gset.gmt.path = GSET.FILE4, # ABSOLUTE path of gene set file
gsva.method = "gsva", # method to calculate single sample enrichment score
# annCol = annCol,
#annColors = annColors,
fig.path = getwd(),
show_rownames = T,
fig.name = "GENE SETS OF tumor HEATMAP",
height = 6,
width = 8)
GSET.FILE5 <- "/t8a/2022backup/xuzijun/vip39/database/h.all.v7.5.symbols.gmt"
gsva.res <-
runGSVA(moic.res = pseudo.moic.res,
norm.expr = KIRC_mRNA_fpkm,
gset.gmt.path = GSET.FILE5, # ABSOLUTE path of gene set file
gsva.method = "gsva", # method to calculate single sample enrichment score
# annCol = annCol,
#annColors = annColors,
fig.path = getwd(),
show_rownames = T,
fig.name = "GENE SETS OF gsva HEATMAP",
height = 8,
width = 12)
rouliugenelist <- fread("/home/data/vip39/database/rouliu_genelist.csv",header = T,data.table = F)
rouliugenelist <- lapply(rouliugenelist, function(x) {
unique(na.omit(x))
})
length(rouliugenelist)
sink("./data/rouliu.gmt")
yourlist <- rouliugenelist
for (i in 1:length(yourlist)){
cat(names(yourlist)[i])
cat('\tNA\t')
cat(paste(yourlist[[i]], collapse = '\t'))
cat('\n')
}
sink()
GSET.FILE6 <- "/home/data/vip39/TCGA/WWOX/data/rouliu.gmt"
gsva.res <-
runGSVA(moic.res = pseudo.moic.res,
norm.expr = KIRC_mRNA_fpkm,
gset.gmt.path = GSET.FILE6, # ABSOLUTE path of gene set file
gsva.method = "gsva", # method to calculate single sample enrichment score
# annCol = annCol,
#annColors = annColors,
fig.path = getwd(),
show_rownames = T,
fig.name = "GENE SETS OF rouliu HEATMAP",
height = 10,
width = 12)
####ceRNA####