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MfuzzAnalysis
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```{r Mfuzz anova openswath IMA ADR }
rm(list=ls())
dfn <- read.table("06_protein_matrix/proteotypic/Openswath_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F)
protein<-read.table("12_matrix_analyze/protein_overlap.txt",sep="\t",header = T)
dfm<-data.frame(dfn[which(dfn$protein%in% protein$elements),])
row.names(dfm)<-dfm$protein
df<-dfm[,-1]
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
ima_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
names(ima_Matrix)<-c("label",as.matrix(names(ima_Matrix[,-1])))
ima_Matrix2<-ima_Matrix[,-which((apply(ima_Matrix,2,function(x) sum(is.na(x) )))> nrow(ima_Matrix)*0.25)]
col<-ncol(ima_Matrix2)
ima_anova<-data.frame(names(ima_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(ima_Matrix2[,i] ~ ima_Matrix2[,1],ima_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
ima_anova[c(i-1),2]<-aov
}
names(ima_anova)<-c('protein',"pvalue")
ima_anova$adjust_pvalue<-p.adjust(ima_anova$pvalue, method="BH")
ima_anova2<-data.frame(ima_anova[c(which(ima_anova$adjust_pvalue<=0.05)),])
write.csv(ima_anova2,"08_mfuzz/Openswath_ima_anvoa.csv",row.names = F)
ima_Matrix3<-ima_Matrix2[,c(names(ima_Matrix2)%in% ima_anova2$protein)]
ima_Matrix3$label<-ima_Matrix2$label
# ImaMatrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
df4<-aggregate(ima_Matrix3[,colnames(ima_Matrix3)[1:ncol(ima_Matrix3)-1]],by=list(ima_Matrix3$label),mean,na.rm= TRUE)
############ima-matrix########################1
df5<-data.frame(df4[c(4,1,2,3),])
df6<-data.frame(t(data.frame(df5[,-1],row.names = df5$Group.1)))
for (i in 1:nrow(df6)) {
df6[i,5]<-sum(is.na(df6[i,c(1:4)]))
}
######################################################1
df7<-data.frame(df6[c(which(df6$V5%in%c("0"))),-5])###1
######################################################1
library(Mfuzz)
set.seed(2020)
df3a<-as.matrix(df7)
df3Ex<- ExpressionSet(assayData = df3a)
if(interactive()){
df3F <- filter.NA(df3Ex)
df3F <- fill.NA(df3F)
df3F <- standardise(df3F)
}
df3F <- filter.NA(df3F)
m<-mestimate(df3F)
cselection(df3F,m=m,crange = seq(2,10,1),repeats = 5,visu = T)
cl <- mfuzz(df3F,c=4,m=m)
pdf("08_mfuzz/Openswath_KMDR_cluster_ima.pdf")
mfuzz.plot2(df3F, cl=cl,mfrow=c(3,3),centre=TRUE,x11=F,centre.lwd=0.2)#min.mem=0.99
dev.off()
###################################1
proteinInCluster <- function(c,hcol,tcol){
selectDfName<-c(" ")
namesCount<-1
for(i in 1:nrow(df7)){
if(!is.na(df7[i,hcol]) && !is.na(df7[i,tcol]) && (df7[i,hcol]-df7[i,tcol])>2){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
else if(is.na(df7[i,hcol]) || is.na(df7[i,tcol])){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
}
#membership value>0.2
clustername<-names(cl$cluster[unname(cl$cluster)==c])
tmpa<-unname(cl[[4]][clustername,c])>0.2
clustername<-clustername[which(tmpa>0.2)]
selectCname<-(selectDfName %in% clustername)
sameIndex<-which(selectCname>0.2)
write.csv(cl[[4]][selectDfName[sameIndex],c],paste("08_mfuzz/",c,"_","Openswath_ima.csv",sep = ""))
}
proteinInCluster(1,1,4)
proteinInCluster(3,4,1)
#########rbind###
options(scipen = 200)
#########自定义函数################1
readCSV <- function(dir_dta){
file_list <- list.files(path=dir_dta,full.names=T)
file_list1<-data.frame(file_list)
file_list2<-as.character(file_list1[c(grep(pattern = 'Openswath_ima.csv',file_list1[,1])),])
varSave_func <- function(x){
table_x <- read.csv(file=x)
}
a<-invisible(lapply(file_list2,FUN=varSave_func))
b<-as.data.frame(a[[1]])
for (i in 2:length(a)){
c<-rbind(b,a[[i]])
b <- c
}
return(b)
}
##########调用函数
dir_dta <- "08_mfuzz/"
result <-readCSV(dir_dta)
##########################################################################################Mfuzz openswath ADR
df <- read.table("06_protein_matrix/proteotypic/Openswath_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
df[is.na(df)]<-min(df[,-1],na.rm = T)
mfuzz_prot<-result
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
############ima-matrix#################################################################1
ima_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
#######################################################################################1
prot<-as.matrix(mfuzz_prot$X)
ima_Matrix2<-data.frame(ima_Matrix[,1],ima_Matrix[,c(which(names(ima_Matrix)%in% prot))])
col<-ncol(ima_Matrix2)
names(ima_Matrix2)<-c("label",as.matrix(names(ima_Matrix2[,-1])))
ima_anova<-data.frame(names(ima_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(ima_Matrix2[,i] ~ ima_Matrix2[,1],ima_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
fc<-log2(mean(ima_Matrix2[12:14,i],na.rm=T)/mean(ima_Matrix2[1:4,i],na.rm=T))
# aov<-as.numeric(aov,digits = 4, scientific = F)
ima_anova[c(i-1),2]<-aov
ima_anova[c(i-1),3]<-fc
}
names(ima_anova)<-c('protein',"pvalue","fc")
write.csv(ima_anova,"08_mfuzz/Openswath_ima_anvoa.csv",row.names = F)
###**********##
rm(list=ls())
dfn <- read.table("06_protein_matrix/proteotypic/Openswath_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F)
protein<-read.table("12_matrix_analyze/protein_overlap.txt",sep="\t",header = T)
dfm<-data.frame(dfn[which(dfn$protein%in% protein$elements),])
row.names(dfm)<-dfm$protein
df<-dfm[,-1]
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
adr_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","A1","A2","A3"))),])
names(adr_Matrix)<-c("label",as.matrix(names(adr_Matrix[,-1])))
adr_Matrix2<-adr_Matrix[,-which((apply(adr_Matrix,2,function(x) sum(is.na(x) )))> nrow(adr_Matrix)*0.25)]
col<-ncol(adr_Matrix2)
adr_anova<-data.frame(names(adr_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(adr_Matrix2[,i] ~ adr_Matrix2[,1],adr_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
adr_anova[c(i-1),2]<-aov
}
names(adr_anova)<-c('protein',"pvalue")
adr_anova$adjust_pvalue<-p.adjust(adr_anova$pvalue, method="BH")
adr_anova2<-data.frame(adr_anova[c(which(adr_anova$adjust_pvalue<=0.05)),])
adr_Matrix3<-adr_Matrix2[,c(names(adr_Matrix2)%in% adr_anova2$protein)]
adr_Matrix3$label<-adr_Matrix2$label
# adrMatrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
df4<-aggregate(adr_Matrix3[,colnames(adr_Matrix3)[1:ncol(adr_Matrix3)-1]],by=list(adr_Matrix3$label),mean,na.rm= TRUE)
############adr-matrix########################1
df5<-data.frame(df4[c(4,1,2,3),])
df6<-data.frame(t(data.frame(df5[,-1],row.names = df5$Group.1)))
for (i in 1:nrow(df6)) {
df6[i,5]<-sum(is.na(df6[i,c(1:4)]))
}
######################################################1
df7<-data.frame(df6[c(which(df6$V5%in%c("0"))),-5])###1
######################################################1
library(Mfuzz)
set.seed(2020)
df3a<-as.matrix(df7)
df3Ex<- ExpressionSet(assayData = df3a)
if(interactive()){
df3F <- filter.NA(df3Ex)
df3F <- fill.NA(df3F)
df3F <- standardise(df3F)
}
df3F <- filter.NA(df3F)
m<-mestimate(df3F)
cselection(df3F,m=m,crange = seq(2,10,1),repeats = 5,visu = T)
cl <- mfuzz(df3F,c=4,m=m)
pdf("08_mfuzz/Openswath_KMDR_cluster_adr.pdf")
mfuzz.plot2(df3F, cl=cl,mfrow=c(3,3),centre=TRUE,x11=F,centre.lwd=0.2)#min.mem=0.99
dev.off()
###################################1
proteinInCluster <- function(c,hcol,tcol){
selectDfName<-c(" ")
namesCount<-1
for(i in 1:nrow(df7)){
if(!is.na(df7[i,hcol]) && !is.na(df7[i,tcol]) && (df7[i,hcol]-df7[i,tcol])>2){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
else if(is.na(df7[i,hcol]) || is.na(df7[i,tcol])){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
}
#membership value>0.2
clustername<-names(cl$cluster[unname(cl$cluster)==c])
tmpa<-unname(cl[[4]][clustername,c])>0.2
clustername<-clustername[which(tmpa>0.2)]
selectCname<-(selectDfName %in% clustername)
sameIndex<-which(selectCname>0.2)
write.csv(cl[[4]][selectDfName[sameIndex],c],paste("08_mfuzz/",c,"_","Openswath_adr.csv",sep = ""))
}
proteinInCluster(1,1,4)
proteinInCluster(2,4,1)
#########rbind###
options(scipen = 200)
#########自定义函数################1
readCSV <- function(dir_dta){
file_list <- list.files(path=dir_dta,full.names=T)
file_list1<-data.frame(file_list)
file_list2<-as.character(file_list1[c(grep(pattern = 'Openswath_adr.csv',file_list1[,1])),])
varSave_func <- function(x){
table_x <- read.csv(file=x)
}
a<-invisible(lapply(file_list2,FUN=varSave_func))
b<-as.data.frame(a[[1]])
for (i in 2:length(a)){
c<-rbind(b,a[[i]])
b <- c
}
return(b)
}
##########调用函数
dir_dta <- "08_mfuzz/"
result <-readCSV(dir_dta)
##########################################################################################Mfuzz openswath ADR
df <- read.table("06_protein_matrix/proteotypic/Openswath_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
df[is.na(df)]<-min(df[,-1],na.rm = T)
mfuzz_prot<-result
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
############adr-matrix#################################################################1
adr_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","A1","A2","A3"))),])
#######################################################################################1
prot<-as.matrix(mfuzz_prot$X)
adr_Matrix2<-data.frame(adr_Matrix[,1],adr_Matrix[,c(which(names(adr_Matrix)%in% prot))])
col<-ncol(adr_Matrix2)
names(adr_Matrix2)<-c("label",as.matrix(names(adr_Matrix2[,-1])))
adr_anova<-data.frame(names(adr_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(adr_Matrix2[,i] ~ adr_Matrix2[,1],adr_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
fc<-log2(mean(adr_Matrix2[12:14,i],na.rm=T)/mean(adr_Matrix2[1:4,i],na.rm=T))
# aov<-as.numeric(aov,digits = 4, scientific = F)
adr_anova[c(i-1),2]<-aov
adr_anova[c(i-1),3]<-fc
}
names(adr_anova)<-c('protein',"pvalue","fc")
write.csv(adr_anova,"08_mfuzz/Openswath_adr_anvoa.csv",row.names = F)
```
```{r Mfuzz Encyo IMA ADR}
rm(list=ls())
dfn <- read.table("06_protein_matrix/proteotypic/Encyo_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F)
protein<-read.table("12_matrix_analyze/protein_overlap.txt",sep="\t",header = T)
dfm<-data.frame(dfn[which(dfn$protein%in% protein$elements),])
row.names(dfm)<-dfm$protein
df<-dfm[,-1]
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
ima_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
names(ima_Matrix)<-c("label",as.matrix(names(ima_Matrix[,-1])))
ima_Matrix2<-ima_Matrix[,-which((apply(ima_Matrix,2,function(x) sum(is.na(x) )))> nrow(ima_Matrix)*0.25)]
col<-ncol(ima_Matrix2)
ima_anova<-data.frame(names(ima_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(ima_Matrix2[,i] ~ ima_Matrix2[,1],ima_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
ima_anova[c(i-1),2]<-aov
}
names(ima_anova)<-c('protein',"pvalue")
ima_anova$adjust_pvalue<-p.adjust(ima_anova$pvalue, method="BH")
ima_anova2<-data.frame(ima_anova[c(which(ima_anova$adjust_pvalue<=0.05)),])
ima_Matrix3<-ima_Matrix2[,c(names(ima_Matrix2)%in% ima_anova2$protein)]
ima_Matrix3$label<-ima_Matrix2$label
# ImaMatrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
df4<-aggregate(ima_Matrix3[,colnames(ima_Matrix3)[1:ncol(ima_Matrix3)-1]],by=list(ima_Matrix3$label),mean,na.rm= TRUE)
############ima-matrix########################1
df5<-data.frame(df4[c(4,1,2,3),])
df6<-data.frame(t(data.frame(df5[,-1],row.names = df5$Group.1)))
for (i in 1:nrow(df6)) {
df6[i,5]<-sum(is.na(df6[i,c(1:4)]))
}
######################################################1
df7<-data.frame(df6[c(which(df6$V5%in%c("0"))),-5])###1
######################################################1
library(Mfuzz)
set.seed(2020)
df3a<-as.matrix(df7)
df3Ex<- ExpressionSet(assayData = df3a)
if(interactive()){
df3F <- filter.NA(df3Ex)
df3F <- fill.NA(df3F)
df3F <- standardise(df3F)
}
df3F <- filter.NA(df3F)
m<-mestimate(df3F)
cselection(df3F,m=m,crange = seq(2,10,1),repeats = 5,visu = T)
cl <- mfuzz(df3F,c=4,m=m)
pdf("08_mfuzz/Encyo_KMDR_cluster_ima.pdf")
mfuzz.plot2(df3F, cl=cl,mfrow=c(3,3),centre=TRUE,x11=F,centre.lwd=0.2)#min.mem=0.99
dev.off()
###################################1
proteinInCluster <- function(c,hcol,tcol){
selectDfName<-c(" ")
namesCount<-1
for(i in 1:nrow(df7)){
if(!is.na(df7[i,hcol]) && !is.na(df7[i,tcol]) && (df7[i,hcol]-df7[i,tcol])>2){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
else if(is.na(df7[i,hcol]) || is.na(df7[i,tcol])){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
}
#membership value>0.2
clustername<-names(cl$cluster[unname(cl$cluster)==c])
tmpa<-unname(cl[[4]][clustername,c])>0.2
clustername<-clustername[which(tmpa>0.2)]
selectCname<-(selectDfName %in% clustername)
sameIndex<-which(selectCname>0.2)
write.csv(cl[[4]][selectDfName[sameIndex],c],paste("08_mfuzz/",c,"_","Encyo_ima.csv",sep = ""))
}
proteinInCluster(4,1,4)
proteinInCluster(3,4,1)
proteinInCluster(2,4,1)
#########rbind###
options(scipen = 200)
#########自定义函数################1
readCSV <- function(dir_dta){
file_list <- list.files(path=dir_dta,full.names=T)
file_list1<-data.frame(file_list)
file_list2<-as.character(file_list1[c(grep(pattern = 'Encyo_ima.csv',file_list1[,1])),])
varSave_func <- function(x){
table_x <- read.csv(file=x)
}
a<-invisible(lapply(file_list2,FUN=varSave_func))
b<-as.data.frame(a[[1]])
for (i in 2:length(a)){
c<-rbind(b,a[[i]])
b <- c
}
return(b)
}
##########调用函数
dir_dta <- "08_mfuzz/"
result <-readCSV(dir_dta)
##########################################################################################Mfuzz Encyo ADR
df <- read.table("06_protein_matrix/proteotypic/Encyo_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
df[is.na(df)]<-min(df[,-1],na.rm = T)
mfuzz_prot<-result
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
############ima-matrix#################################################################1
ima_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
#######################################################################################1
prot<-as.matrix(mfuzz_prot$X)
ima_Matrix2<-data.frame(ima_Matrix[,1],ima_Matrix[,c(which(names(ima_Matrix)%in% prot))])
col<-ncol(ima_Matrix2)
names(ima_Matrix2)<-c("label",as.matrix(names(ima_Matrix2[,-1])))
ima_anova<-data.frame(names(ima_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(ima_Matrix2[,i] ~ ima_Matrix2[,1],ima_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
fc<-log2(mean(ima_Matrix2[12:14,i],na.rm=T)/mean(ima_Matrix2[1:4,i],na.rm=T))
# aov<-as.numeric(aov,digits = 4, scientific = F)
ima_anova[c(i-1),2]<-aov
ima_anova[c(i-1),3]<-fc
}
names(ima_anova)<-c('protein',"pvalue","fc")
write.csv(ima_anova,"08_mfuzz/Encyo_ima_anvoa.csv",row.names = F)
###**********##
rm(list=ls())
dfn <- read.table("06_protein_matrix/proteotypic/Encyo_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F)
protein<-read.table("12_matrix_analyze/protein_overlap.txt",sep="\t",header = T)
dfm<-data.frame(dfn[which(dfn$protein%in% protein$elements),])
row.names(dfm)<-dfm$protein
df<-dfm[,-1]
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
adr_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","A1","A2","A3"))),])
names(adr_Matrix)<-c("label",as.matrix(names(adr_Matrix[,-1])))
adr_Matrix2<-adr_Matrix[,-which((apply(adr_Matrix,2,function(x) sum(is.na(x) )))> nrow(adr_Matrix)*0.25)]
col<-ncol(adr_Matrix2)
adr_anova<-data.frame(names(adr_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(adr_Matrix2[,i] ~ adr_Matrix2[,1],adr_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
adr_anova[c(i-1),2]<-aov
}
names(adr_anova)<-c('protein',"pvalue")
adr_anova$adjust_pvalue<-p.adjust(adr_anova$pvalue, method="BH")
adr_anova2<-data.frame(adr_anova[c(which(adr_anova$adjust_pvalue<=0.05)),])
adr_Matrix3<-adr_Matrix2[,c(names(adr_Matrix2)%in% adr_anova2$protein)]
adr_Matrix3$label<-adr_Matrix2$label
# adrMatrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
df4<-aggregate(adr_Matrix3[,colnames(adr_Matrix3)[1:ncol(adr_Matrix3)-1]],by=list(adr_Matrix3$label),mean,na.rm= TRUE)
############adr-matrix########################1
df5<-data.frame(df4[c(4,1,2,3),])
df6<-data.frame(t(data.frame(df5[,-1],row.names = df5$Group.1)))
for (i in 1:nrow(df6)) {
df6[i,5]<-sum(is.na(df6[i,c(1:4)]))
}
######################################################1
df7<-data.frame(df6[c(which(df6$V5%in%c("0"))),-5])###1
######################################################1
library(Mfuzz)
set.seed(2020)
df3a<-as.matrix(df7)
df3Ex<- ExpressionSet(assayData = df3a)
if(interactive()){
df3F <- filter.NA(df3Ex)
df3F <- fill.NA(df3F)
df3F <- standardise(df3F)
}
df3F <- filter.NA(df3F)
m<-mestimate(df3F)
cselection(df3F,m=m,crange = seq(2,10,1),repeats = 5,visu = T)
cl <- mfuzz(df3F,c=4,m=m)
pdf("08_mfuzz/Encyo_KMDR_cluster_adr.pdf")
mfuzz.plot2(df3F, cl=cl,mfrow=c(3,3),centre=TRUE,x11=F,centre.lwd=0.2)#min.mem=0.99
dev.off()
###################################1
proteinInCluster <- function(c,hcol,tcol){
selectDfName<-c(" ")
namesCount<-1
for(i in 1:nrow(df7)){
if(!is.na(df7[i,hcol]) && !is.na(df7[i,tcol]) && (df7[i,hcol]-df7[i,tcol])>2){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
else if(is.na(df7[i,hcol]) || is.na(df7[i,tcol])){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
}
#membership value>0.2
clustername<-names(cl$cluster[unname(cl$cluster)==c])
tmpa<-unname(cl[[4]][clustername,c])>0.2
clustername<-clustername[which(tmpa>0.2)]
selectCname<-(selectDfName %in% clustername)
sameIndex<-which(selectCname>0.2)
write.csv(cl[[4]][selectDfName[sameIndex],c],paste("08_mfuzz/",c,"_","Encyo_adr.csv",sep = ""))
}
proteinInCluster(1,1,4)
proteinInCluster(4,4,1)
#########rbind###
options(scipen = 200)
#########自定义函数################1
readCSV <- function(dir_dta){
file_list <- list.files(path=dir_dta,full.names=T)
file_list1<-data.frame(file_list)
file_list2<-as.character(file_list1[c(grep(pattern = 'Encyo_adr.csv',file_list1[,1])),])
varSave_func <- function(x){
table_x <- read.csv(file=x)
}
a<-invisible(lapply(file_list2,FUN=varSave_func))
b<-as.data.frame(a[[1]])
for (i in 2:length(a)){
c<-rbind(b,a[[i]])
b <- c
}
return(b)
}
##########调用函数
dir_dta <- "08_mfuzz/"
result <-readCSV(dir_dta)
##########################################################################################Mfuzz Encyo ADR
df <- read.table("06_protein_matrix/proteotypic/Encyo_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
df[is.na(df)]<-min(df[,-1],na.rm = T)
mfuzz_prot<-result
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
############adr-matrix#################################################################1
adr_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","A1","A2","A3"))),])
#######################################################################################1
prot<-as.matrix(mfuzz_prot$X)
adr_Matrix2<-data.frame(adr_Matrix[,1],adr_Matrix[,c(which(names(adr_Matrix)%in% prot))])
col<-ncol(adr_Matrix2)
names(adr_Matrix2)<-c("label",as.matrix(names(adr_Matrix2[,-1])))
adr_anova<-data.frame(names(adr_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(adr_Matrix2[,i] ~ adr_Matrix2[,1],adr_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
fc<-log2(mean(adr_Matrix2[12:14,i],na.rm=T)/mean(adr_Matrix2[1:4,i],na.rm=T))
# aov<-as.numeric(aov,digits = 4, scientific = F)
adr_anova[c(i-1),2]<-aov
adr_anova[c(i-1),3]<-fc
}
names(adr_anova)<-c('protein',"pvalue","fc")
write.csv(adr_anova,"08_mfuzz/Encyo_adr_anvoa.csv",row.names = F)
```
```{r Mfuzz DIAnn IMA ADR}
rm(list=ls())
dfn <- read.table("06_protein_matrix/proteotypic/DIAnn_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F)
protein<-read.table("12_matrix_analyze/protein_overlap.txt",sep="\t",header = T)
dfm<-data.frame(dfn[which(dfn$protein%in% protein$elements),])
row.names(dfm)<-dfm$protein
df<-dfm[,-1]
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
ima_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
names(ima_Matrix)<-c("label",as.matrix(names(ima_Matrix[,-1])))
ima_Matrix2<-ima_Matrix[,-which((apply(ima_Matrix,2,function(x) sum(is.na(x) )))> nrow(ima_Matrix)*0.25)]
col<-ncol(ima_Matrix2)
ima_anova<-data.frame(names(ima_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(ima_Matrix2[,i] ~ ima_Matrix2[,1],ima_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
ima_anova[c(i-1),2]<-aov
}
names(ima_anova)<-c('protein',"pvalue")
ima_anova$adjust_pvalue<-p.adjust(ima_anova$pvalue, method="BH")
ima_anova2<-data.frame(ima_anova[c(which(ima_anova$adjust_pvalue<=0.05)),])
ima_Matrix3<-ima_Matrix2[,c(names(ima_Matrix2)%in% ima_anova2$protein)]
ima_Matrix3$label<-ima_Matrix2$label
# ImaMatrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
df4<-aggregate(ima_Matrix3[,colnames(ima_Matrix3)[1:ncol(ima_Matrix3)-1]],by=list(ima_Matrix3$label),mean,na.rm= TRUE)
############ima-matrix########################1
df5<-data.frame(df4[c(4,1,2,3),])
df6<-data.frame(t(data.frame(df5[,-1],row.names = df5$Group.1)))
for (i in 1:nrow(df6)) {
df6[i,5]<-sum(is.na(df6[i,c(1:4)]))
}
######################################################1
df7<-data.frame(df6[c(which(df6$V5%in%c("0"))),-5])###1
######################################################1
library(Mfuzz)
set.seed(2020)
df3a<-as.matrix(df7)
df3Ex<- ExpressionSet(assayData = df3a)
if(interactive()){
df3F <- filter.NA(df3Ex)
df3F <- fill.NA(df3F)
df3F <- standardise(df3F)
}
df3F <- filter.NA(df3F)
m<-mestimate(df3F)
cselection(df3F,m=m,crange = seq(2,10,1),repeats = 5,visu = T)
cl <- mfuzz(df3F,c=4,m=m)
pdf("08_mfuzz/DIAnn_KMDR_cluster_ima.pdf")
mfuzz.plot2(df3F, cl=cl,mfrow=c(3,3),centre=TRUE,x11=F,centre.lwd=0.2)#min.mem=0.99
dev.off()
###################################1
proteinInCluster <- function(c,hcol,tcol){
selectDfName<-c(" ")
namesCount<-1
for(i in 1:nrow(df7)){
if(!is.na(df7[i,hcol]) && !is.na(df7[i,tcol]) && (df7[i,hcol]-df7[i,tcol])>2){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
else if(is.na(df7[i,hcol]) || is.na(df7[i,tcol])){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
}
#membership value>0.2
clustername<-names(cl$cluster[unname(cl$cluster)==c])
tmpa<-unname(cl[[4]][clustername,c])>0.2
clustername<-clustername[which(tmpa>0.2)]
selectCname<-(selectDfName %in% clustername)
sameIndex<-which(selectCname>0.2)
write.csv(cl[[4]][selectDfName[sameIndex],c],paste("08_mfuzz/",c,"_","DIAnn_ima.csv",sep = ""))
}
proteinInCluster(1,1,4)
proteinInCluster(2,4,1)
#########rbind###
options(scipen = 200)
#########自定义函数################1
readCSV <- function(dir_dta){
file_list <- list.files(path=dir_dta,full.names=T)
file_list1<-data.frame(file_list)
file_list2<-as.character(file_list1[c(grep(pattern = 'DIAnn_ima.csv',file_list1[,1])),])
varSave_func <- function(x){
table_x <- read.csv(file=x)
}
a<-invisible(lapply(file_list2,FUN=varSave_func))
b<-as.data.frame(a[[1]])
for (i in 2:length(a)){
c<-rbind(b,a[[i]])
b <- c
}
return(b)
}
##########调用函数
dir_dta <- "08_mfuzz/"
result <-readCSV(dir_dta)
##########################################################################################Mfuzz DIAnn ADR
df <- read.table("06_protein_matrix/proteotypic/DIAnn_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
df[is.na(df)]<-min(df[,-1],na.rm = T)
mfuzz_prot<-result
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
############ima-matrix#################################################################1
ima_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
#######################################################################################1
prot<-as.matrix(mfuzz_prot$X)
ima_Matrix2<-data.frame(ima_Matrix[,1],ima_Matrix[,c(which(names(ima_Matrix)%in% prot))])
col<-ncol(ima_Matrix2)
names(ima_Matrix2)<-c("label",as.matrix(names(ima_Matrix2[,-1])))
ima_anova<-data.frame(names(ima_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(ima_Matrix2[,i] ~ ima_Matrix2[,1],ima_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
fc<-log2(mean(ima_Matrix2[12:14,i],na.rm=T)/mean(ima_Matrix2[1:4,i],na.rm=T))
# aov<-as.numeric(aov,digits = 4, scientific = F)
ima_anova[c(i-1),2]<-aov
ima_anova[c(i-1),3]<-fc
}
names(ima_anova)<-c('protein',"pvalue","fc")
write.csv(ima_anova,"08_mfuzz/DIAnn_ima_anvoa.csv",row.names = F)
###**********##
rm(list=ls())
dfn <- read.table("06_protein_matrix/proteotypic/DIAnn_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F)
protein<-read.table("12_matrix_analyze/protein_overlap.txt",sep="\t",header = T)
dfm<-data.frame(dfn[which(dfn$protein%in% protein$elements),])
row.names(dfm)<-dfm$protein
df<-dfm[,-1]
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
adr_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","A1","A2","A3"))),])
names(adr_Matrix)<-c("label",as.matrix(names(adr_Matrix[,-1])))
adr_Matrix2<-adr_Matrix[,-which((apply(adr_Matrix,2,function(x) sum(is.na(x) )))> nrow(adr_Matrix)*0.25)]
col<-ncol(adr_Matrix2)
adr_anova<-data.frame(names(adr_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(adr_Matrix2[,i] ~ adr_Matrix2[,1],adr_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
adr_anova[c(i-1),2]<-aov
}
names(adr_anova)<-c('protein',"pvalue")
adr_anova$adjust_pvalue<-p.adjust(adr_anova$pvalue, method="BH")
adr_anova2<-data.frame(adr_anova[c(which(adr_anova$adjust_pvalue<=0.05)),])
adr_Matrix3<-adr_Matrix2[,c(names(adr_Matrix2)%in% adr_anova2$protein)]
adr_Matrix3$label<-adr_Matrix2$label
# adrMatrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
df4<-aggregate(adr_Matrix3[,colnames(adr_Matrix3)[1:ncol(adr_Matrix3)-1]],by=list(adr_Matrix3$label),mean,na.rm= TRUE)
############adr-matrix########################1
df5<-data.frame(df4[c(4,1,2,3),])
df6<-data.frame(t(data.frame(df5[,-1],row.names = df5$Group.1)))
for (i in 1:nrow(df6)) {
df6[i,5]<-sum(is.na(df6[i,c(1:4)]))
}
######################################################1
df7<-data.frame(df6[c(which(df6$V5%in%c("0"))),-5])###1
######################################################1
library(Mfuzz)
set.seed(2020)
df3a<-as.matrix(df7)
df3Ex<- ExpressionSet(assayData = df3a)
if(interactive()){
df3F <- filter.NA(df3Ex)
df3F <- fill.NA(df3F)
df3F <- standardise(df3F)
}
df3F <- filter.NA(df3F)
m<-mestimate(df3F)
cselection(df3F,m=m,crange = seq(2,10,1),repeats = 5,visu = T)
cl <- mfuzz(df3F,c=4,m=m)
pdf("08_mfuzz/DIAnn_KMDR_cluster_adr.pdf")
mfuzz.plot2(df3F, cl=cl,mfrow=c(3,3),centre=TRUE,x11=F,centre.lwd=0.2)#min.mem=0.99
dev.off()
###################################1
proteinInCluster <- function(c,hcol,tcol){
selectDfName<-c(" ")
namesCount<-1
for(i in 1:nrow(df7)){
if(!is.na(df7[i,hcol]) && !is.na(df7[i,tcol]) && (df7[i,hcol]-df7[i,tcol])>2){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
else if(is.na(df7[i,hcol]) || is.na(df7[i,tcol])){
selectDfName[namesCount]<-rownames(df7[i,])
namesCount<-namesCount+1
}
}
#membership value>0.2
clustername<-names(cl$cluster[unname(cl$cluster)==c])
tmpa<-unname(cl[[4]][clustername,c])>0.2
clustername<-clustername[which(tmpa>0.2)]
selectCname<-(selectDfName %in% clustername)
sameIndex<-which(selectCname>0.2)
write.csv(cl[[4]][selectDfName[sameIndex],c],paste("08_mfuzz/",c,"_","DIAnn_adr.csv",sep = ""))
}
proteinInCluster(1,1,4)
proteinInCluster(4,1,4)
proteinInCluster(2,4,1)
proteinInCluster(3,4,1)
#########rbind###
options(scipen = 200)
#########自定义函数################1
readCSV <- function(dir_dta){
file_list <- list.files(path=dir_dta,full.names=T)
file_list1<-data.frame(file_list)
file_list2<-as.character(file_list1[c(grep(pattern = 'DIAnn_adr.csv',file_list1[,1])),])
varSave_func <- function(x){
table_x <- read.csv(file=x)
}
a<-invisible(lapply(file_list2,FUN=varSave_func))
b<-as.data.frame(a[[1]])
for (i in 2:length(a)){
c<-rbind(b,a[[i]])
b <- c
}
return(b)
}
##########调用函数
dir_dta <- "08_mfuzz/"
result <-readCSV(dir_dta)
##########################################################################################Mfuzz DIAnn ADR
df <- read.table("06_protein_matrix/proteotypic/DIAnn_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F,row.names = 1)
df[is.na(df)]<-min(df[,-1],na.rm = T)
mfuzz_prot<-result
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
############adr-matrix#################################################################1
adr_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","A1","A2","A3"))),])
#######################################################################################1
prot<-as.matrix(mfuzz_prot$X)
adr_Matrix2<-data.frame(adr_Matrix[,1],adr_Matrix[,c(which(names(adr_Matrix)%in% prot))])
col<-ncol(adr_Matrix2)
names(adr_Matrix2)<-c("label",as.matrix(names(adr_Matrix2[,-1])))
adr_anova<-data.frame(names(adr_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(adr_Matrix2[,i] ~ adr_Matrix2[,1],adr_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
fc<-log2(mean(adr_Matrix2[12:14,i],na.rm=T)/mean(adr_Matrix2[1:4,i],na.rm=T))
# aov<-as.numeric(aov,digits = 4, scientific = F)
adr_anova[c(i-1),2]<-aov
adr_anova[c(i-1),3]<-fc
}
names(adr_anova)<-c('protein',"pvalue","fc")
write.csv(adr_anova,"08_mfuzz/DIAnn_adr_anvoa.csv",row.names = F)
```
```{r Mfuzz Spec IMA ADR}
rm(list=ls())
dfn <- read.table("06_protein_matrix/proteotypic/Spec_part_ave_KMDR_proteotypic_matrix.txt",sep="\t",header = T,stringsAsFactors = F)
protein<-read.table("12_matrix_analyze/protein_overlap.txt",sep="\t",header = T)
dfm<-data.frame(dfn[which(dfn$protein%in% protein$elements),])
row.names(dfm)<-dfm$protein
df<-dfm[,-1]
df1<-data.frame(2^t(df))
df2<-data.frame(row.names(df1),df1)
col<-ncol(df2)
for (i in 1:nrow(df2)) {
df2[i,col+1]<-strsplit(c(as.matrix(df2[i,1])),"_",fixed=T)[[1]][5]
}
df3<-data.frame(df2[,col+1],df2[,-c(1,col+1)])
ima_Matrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])
names(ima_Matrix)<-c("label",as.matrix(names(ima_Matrix[,-1])))
ima_Matrix2<-ima_Matrix[,-which((apply(ima_Matrix,2,function(x) sum(is.na(x) )))> nrow(ima_Matrix)*0.25)]
col<-ncol(ima_Matrix2)
ima_anova<-data.frame(names(ima_Matrix2[,-1]))
for (i in 2:col) {
aov<-(summary(aov(ima_Matrix2[,i] ~ ima_Matrix2[,1],ima_Matrix2))[[1]])$`Pr(>F)`[1]
aov<-as.numeric(aov)
ima_anova[c(i-1),2]<-aov
}
names(ima_anova)<-c('protein',"pvalue")
ima_anova$adjust_pvalue<-p.adjust(ima_anova$pvalue, method="BH")
ima_anova2<-data.frame(ima_anova[c(which(ima_anova$adjust_pvalue<=0.05)),])
ima_Matrix3<-ima_Matrix2[,c(names(ima_Matrix2)%in% ima_anova2$protein)]
ima_Matrix3$label<-ima_Matrix2$label
# ImaMatrix<-data.frame(df3[c(which(df3$df2...col...1.%in%c("K","I1","I2","I3"))),])