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Example1.R
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Example1.R
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if (!require("foreach")) install.packages("foreach")
library(foreach)
if (!require("doParallel")) install.packages("doParallel")
library(doParallel)
#require randtoolbox for random number generations
if (!require("randtoolbox")) install.packages("randtoolbox")
library(randtoolbox)
if (!require("Rcpp")) install.packages("Rcpp")
library(Rcpp)
if (!require("Rfast")) install.packages("Rfast")
library(Rfast)
if (!require("matrixStats")) install.packages("matrixStats")
library(matrixStats)
raw1 <- read.csv(("GSE147507_RawReadCounts_Ferret.csv"))
#colnames(raw1)[1] <- 'NAME'
# --- mean value
meta1 <- read.csv(("metadata.csv"))
#colnames(meta1)[1] <- 'NAME'
orderpairGSE147507<-orderpair(rawdata = raw1,metadata = meta1,paired=FALSE,ci = FALSE)
orderpairGSE147507A<-orderpairGSE147507$result
write.csv(orderpairGSE147507A,paste("orderpairGSE147507_day.csv", sep = ","), row.names = FALSE)
raw1 <- read.csv(("AEC_SARSCoV2_17AAG_readcounts.csv"))
#colnames(raw1)[1] <- 'NAME'
# --- mean value
meta1 <- read.csv(("metadata_AEC_SARSCoV2_17AAG.csv"))
#colnames(meta1)[1] <- 'NAME'
orderpairAEC_SARSCoV2<-orderpair(rawdata = raw1,metadata = meta1,paired=FALSE,ci = FALSE)
orderpairAEC_SARSCoV2<-orderpairAEC_SARSCoV2$result
write.csv(orderpairAEC_SARSCoV2,paste("orderpairAEC_SARSCoV2_day.csv", sep = ","), row.names = FALSE)
raw1 <- read.csv(("AEC_SARSCoV2_17AAG_readcounts.csv"))
#colnames(raw1)[1] <- 'NAME'
# --- mean value
meta1 <- read.csv(("metadata_AEC_SARSCoV2_17AAGmock.csv"))
#colnames(meta1)[1] <- 'NAME'
orderpairAEC_SARSCoV2<-orderpair(rawdata = raw1,metadata = meta1,paired=FALSE,ci = FALSE)
orderpairAEC_SARSCoV2<-orderpairAEC_SARSCoV2$result
write.csv(orderpairAEC_SARSCoV2,paste("orderpairAEC_SARSCoV2_day_mock.csv", sep = ","), row.names = FALSE)
orderpair<- function(rawdata,metadata,phylogenetictree=NULL,paired=FALSE,ci = FALSE,alpha=0.05,nboot=100,robust = 1){
metadata$Order2 <- with(metadata,paste0(Subgroup,Order))
metadataorder <- split(metadata,metadata$Subgroup)
pairs0 <- data.frame()
results0 <- data.frame()
for (i in 1:length(metadataorder)) {
cnt1 <- length(unique(metadataorder[[i]]$Order2))
metadata1=metadataorder[[i]]
ordernumber=as.numeric(length(unique(metadataorder[[i]]$Order)))
orderlist=sort(unique(metadataorder[[i]]$Order),decreasing = FALSE)
for (j in 1:(ordernumber-1)) {
dissimilarity4<-dissimilarity(rawdata = raw1,metadata = metadata1,Order1 = toString(orderlist[j]), Order2 = toString(orderlist[j+1]),phylogenetictree=phylogenetictree,paired=paired,ci = ci,alpha=alpha,nboot=nboot,robust = robust)
pair1 <- paste0(unique(metadataorder[[i]]$Subgroup),orderlist[j])
pair2 <- paste0(unique(metadataorder[[i]]$Subgroup),orderlist[j+1])
#pairs <- data.frame(cbind(pair1,pair2,meandivergens$estimate,sparsitydivergens$estimate,variancedivergens$estimaste,meandivergens$ci,sparsitydivergens$ci,variancedivergens$ci))
pairs<-c(pair1=pair1,
pair2=pair2,
dissimilarity=(dissimilarity4$results))
pairs0 <- rbind(pairs0,pairs)
resul1<-c(pair1=pair1,
pair2=pair2,
rawdata=dissimilarity4$rawdata)
resultlist<-list(resul1)
names(resultlist) <- c(paste("A", i,j, sep = ""))
results0 <- append(results0,resultlist)
}
}
All<-list(result=pairs0,rawdata=results0)
return(All)#,'orderpair_batchhuman.csv',row.names = FALSE)
}
moments<-function (x){
n<-length(x)
mean1=mean(x)
var1<-sum((x-mean1)^2)/n
tm1<-sum((x-mean1)^3)/n
fm1<-sum((x-mean1)^4)/n
listall<-list(mean=mean1,var=var1,tm=tm1,fm=fm1)
(listall)
}
unbiasedmoments<-function (x){
n<-length(x)
moments1<-moments(x)
m1<-moments1$mean
var1<-moments1$var
tm1<-moments1$tm
fm1<-moments1$fm
var2<-var1*(n/(n-1))
tm2<-(tm1)*(n^2/((n-1)*(n-2)))
ufm1<--3*var1^2*(2*n-3)*n/((n-1)*(n-2)*(n-3))+(n^2-2*n+3)*fm1*n/((n-1)*(n-2)*(n-3))
listall<-c(mean=m1,var=var2,tm=tm2,fm=ufm1)
(listall)
}
standardizedmoments<-function (x,releaseall=FALSE){
unbiasedmoments1<-unbiasedmoments(x)
all1<-c(mean=unbiasedmoments1[1],var=sqrt(unbiasedmoments1[2]),skew=unbiasedmoments1[3]/((unbiasedmoments1[2])^(3/2)),kurt=unbiasedmoments1[4]/((unbiasedmoments1[2])^(2)))
if(releaseall){
c(unbiasedmoments1,all1)
}else{
(all1)
}
}
findmoments<- function(selected1=NULL,selected2=NULL,paired=FALSE){
if(paired){
selected3<-selected2-selected1
meandi <- data.frame(apply(selected3,1,FUN = mean))
meancombined3 <-data.frame(apply(meandi,1,abs))
sd1 <- data.frame(apply(selected1,1,FUN = sd))
sd2 <- data.frame(apply(selected2,1,FUN = sd))
sdall <- cbind(sd1,sd2)
sdcombined1 <-data.frame(apply(sdall,1,max))
sdcombined2 <-data.frame(apply(sdall,1,min))
sdcombined3 <-sdcombined1-sdcombined2
sddi<- sd2-sd1
select <- cbind(selected1,selected2)
sumall<- data.frame(apply(select,1,FUN = sum))
selected1zero<-rowSums(selected1==0)/(dim(selected1)[2])
selected2zero<-rowSums(selected2==0)/(dim(selected2)[2])
} else{
mean1 <- data.frame(apply(selected1,1,FUN = mean))
mean2 <- data.frame(apply(selected2,1,FUN = mean))
meanall <- cbind(mean1,mean2)
meancombined1 <-data.frame(apply(meanall,1,max))
meancombined2 <-data.frame(apply(meanall,1,min))
meancombined3 <-meancombined1-meancombined2
meandi<- mean2-mean1
sd1 <- data.frame(apply(selected1,1,FUN = sd))
sd2 <- data.frame(apply(selected2,1,FUN = sd))
sdall <- cbind(sd1,sd2)
sdcombined1 <-data.frame(apply(sdall,1,max))
sdcombined2 <-data.frame(apply(sdall,1,min))
sdcombined3 <-sdcombined1-sdcombined2
sddi<- sd2-sd1
select <- cbind(selected1,selected2)
sumall<- data.frame(apply(select,1,FUN = sum))
selected1zero<-rowSums(selected1==0)/(dim(selected1)[2])
selected2zero<-rowSums(selected2==0)/(dim(selected2)[2])
}
results<-list(meanmean=mean(unlist(meanall)),sdsd=mean(unlist(sdall)),sumall=sumall,meancombined3=meancombined3,meandi=meandi,sdcombined3=sdcombined3,sddi=sddi,selected1zero=selected1zero,selected2zero=selected2zero)
return(results)
}
#treetoweight(phylogenetictree)
#matrixtoweight(correlationmatrix)
#phyloadjust<-treetoweight(phylotree)
dissimilarity<- function(rawdata,metadata,phylogenetictree=FALSE,Order1,Order2,paired=NULL,ci=NULL,alpha=NULL,nboot=NULL,robust=NULL){
# rawdata<-raw1
# metadata<-meta1
# full=TRUE
# paired=TRUE
# Group1="faeces-amox-1"
# Group2="faeces-amox-2"
# NAME1=NULL
# Treatment1=NULL
# Subgroup1=NULL
# Order1=NULL
# NAME2=NULL
# Treatment2=NULL
# Subgroup2=NULL
# Order2=NULL
# phylo=TRUE
# robust = 1
rawdata[is.na(rawdata)] = 0
if (!require("foreach")) install.packages("foreach")
library(foreach)
if (!require("doParallel")) install.packages("doParallel")
library(doParallel)
#require randtoolbox for random number generations
if (!require("randtoolbox")) install.packages("randtoolbox")
library(randtoolbox)
if (!require("Rcpp")) install.packages("Rcpp")
library(Rcpp)
if (!require("Rfast")) install.packages("Rfast")
library(Rfast)
if (!require("matrixStats")) install.packages("matrixStats")
library(matrixStats)
indx1 <- metadata$NAME[metadata$Order==Order1]
indx2 <- metadata$NAME[metadata$Order==Order2]
selected1 <- rawdata[,indx1]
selected2 <- rawdata[,indx2]
selected1 <- data.frame(selected1)
selected2 <- data.frame(selected2)
results<-findmoments(selected1=selected1,selected2=selected2,paired=paired)
meanmean=results$meanmean
sdsd=results$sdsd
sumall=results$sumall
meancombined3=results$meancombined3
meandi=results$meandi
sdcombined3=results$sdcombined3
sddi=results$sddi
selected1zero=results$selected1zero
selected2zero=results$selected2zero
mean_dissimilarity <- wilcox.test(x=unlist(meancombined3),conf.int = TRUE)$estimate
mean_dissimilarity_direct <- wilcox.test(x=unlist(meandi),conf.int = TRUE)$estimate
standardized_mean_dissimilarity<-mean_dissimilarity/meanmean
standardized_mean_dissimilarity_direct<-mean_dissimilarity_direct/meanmean
sd_dissimilarity <- wilcox.test(x=unlist(sdcombined3),conf.int = TRUE)$estimate
sd_dissimilarity_direct <- wilcox.test(x=unlist(sddi),conf.int = TRUE)$estimate
standardized_sd_dissimilarity<-sd_dissimilarity/sdsd
standardized_sd_dissimilarity_direct<-sd_dissimilarity_direct/sdsd
sparsity_dissimilarity<-sum(meancombined3*abs(selected1zero-selected2zero))#*2/(sparsity_all[2]+sparsity_all[1])
standardized_sparsity_dissimilarity<-sum(meancombined3*abs(selected1zero-selected2zero))*2/(sum(unlist(sumall)))
sparsity_dissimilarity_direct<-sum(meandi*abs(selected1zero-selected2zero))#*2/(sparsity_all[2]+sparsity_all[1])
standardized_sparsity_dissimilarity_direct<-sum(meandi*abs(selected1zero-selected2zero))*2/(sum(unlist(sumall)))
R=50
indices_list <- replicate(R, list(indices1 = sample(1:(dim(selected1)[1]), replace = TRUE)), simplify = FALSE)
numCores <- detectCores()-4 # Detect the number of available cores
cl <- makeCluster(numCores) # Create a cluster with the number of cores
registerDoParallel(cl) # Register the parallel backend
bootresults1 <- foreach(indices = indices_list, .packages = c("stats")) %dopar% {
selected1_resampled <- selected1[indices$indices1,]
selected2_resampled <- selected2[indices$indices1,]
findmoments<- function(selected1=NULL,selected2=NULL,paired=FALSE){
if(paired){
selected3<-selected2-selected1
meandi <- data.frame(apply(selected3,1,FUN = mean))
meancombined3 <-data.frame(apply(meandi,1,abs))
sd1 <- data.frame(apply(selected1,1,FUN = sd))
sd2 <- data.frame(apply(selected2,1,FUN = sd))
sdall <- cbind(sd1,sd2)
sdcombined1 <-data.frame(apply(sdall,1,max))
sdcombined2 <-data.frame(apply(sdall,1,min))
sdcombined3 <-sdcombined1-sdcombined2
sddi<- sd2-sd1
select <- cbind(selected1,selected2)
sumall<- data.frame(apply(select,1,FUN = sum))
selected1zero<-rowSums(selected1==0)/(dim(selected1)[2])
selected2zero<-rowSums(selected2==0)/(dim(selected2)[2])
} else{
mean1 <- data.frame(apply(selected1,1,FUN = mean))
mean2 <- data.frame(apply(selected2,1,FUN = mean))
meanall <- cbind(mean1,mean2)
meancombined1 <-data.frame(apply(meanall,1,max))
meancombined2 <-data.frame(apply(meanall,1,min))
meancombined3 <-meancombined1-meancombined2
meandi<- mean2-mean1
sd1 <- data.frame(apply(selected1,1,FUN = sd))
sd2 <- data.frame(apply(selected2,1,FUN = sd))
sdall <- cbind(sd1,sd2)
sdcombined1 <-data.frame(apply(sdall,1,max))
sdcombined2 <-data.frame(apply(sdall,1,min))
sdcombined3 <-sdcombined1-sdcombined2
sddi<- sd2-sd1
select <- cbind(selected1,selected2)
sumall<- data.frame(apply(select,1,FUN = sum))
selected1zero<-rowSums(selected1==0)/(dim(selected1)[2])
selected2zero<-rowSums(selected2==0)/(dim(selected2)[2])
}
results<-list(meanmean=mean(unlist(meanall)),sdsd=mean(unlist(sdall)),sumall=sumall,meancombined3=meancombined3,meandi=meandi,sdcombined3=sdcombined3,sddi=sddi,selected1zero=selected1zero,selected2zero=selected2zero)
return(results)
}
results<-findmoments(selected1=selected1_resampled,selected2=selected2_resampled,paired=paired)
meanmean=results$meanmean
sdsd=results$sdsd
sumall=results$sumall
meancombined3=results$meancombined3
meandi=results$meandi
sdcombined3=results$sdcombined3
sddi=results$sddi
selected1zero=results$selected1zero
selected2zero=results$selected2zero
mean_dissimilarity <- wilcox.test(x=unlist(meancombined3),conf.int = TRUE)$estimate
mean_dissimilarity_direct <- wilcox.test(x=unlist(meandi),conf.int = TRUE)$estimate
standardized_mean_dissimilarity<-mean_dissimilarity/meanmean
standardized_mean_dissimilarity_direct<-mean_dissimilarity_direct/meanmean
sd_dissimilarity <- wilcox.test(x=unlist(sdcombined3),conf.int = TRUE)$estimate
sd_dissimilarity_direct <- wilcox.test(x=unlist(sddi),conf.int = TRUE)$estimate
standardized_sd_dissimilarity<-sd_dissimilarity/sdsd
standardized_sd_dissimilarity_direct<-sd_dissimilarity_direct/sdsd
sparsity_dissimilarity<-sum(meancombined3*abs(selected1zero-selected2zero))#*2/(sparsity_all[2]+sparsity_all[1])
standardized_sparsity_dissimilarity<-sum(meancombined3*abs(selected1zero-selected2zero))*2/(sum(unlist(sumall)))
sparsity_dissimilarity_direct<-sum(meandi*abs(selected1zero-selected2zero))#*2/(sparsity_all[2]+sparsity_all[1])
standardized_sparsity_dissimilarity_direct<-sum(meandi*abs(selected1zero-selected2zero))*2/(sum(unlist(sumall)))
results2<-c(mean_dissimilarity=mean_dissimilarity,
mean_dissimilarity_direct=mean_dissimilarity_direct,standardized_mean_dissimilarity=standardized_mean_dissimilarity,
standardized_mean_dissimilarity_direct=standardized_mean_dissimilarity_direct,
sd_dissimilarity=sd_dissimilarity,
sd_dissimilarity_direct=sd_dissimilarity_direct,standardized_sd_dissimilarity=standardized_sd_dissimilarity,
standardized_sd_dissimilarity_direct=standardized_sd_dissimilarity_direct,
sparsity_dissimilarity=sparsity_dissimilarity,
standardized_sparsity_dissimilarity=standardized_sparsity_dissimilarity,sparsity_dissimilarity_direct=sparsity_dissimilarity_direct,
standardized_sparsity_dissimilarity_direct=standardized_sparsity_dissimilarity_direct)
results2
}
stopCluster(cl)
registerDoSEQ()
bootresults2 <- data.frame(bootresults1)
ci0<-c()
for(i in 1:12){
ci1<-quantile(unlist(bootresults2[i,]), probs = c(0.05, 0.95))
ci0<-rbind(ci0,ci1)
}
results2<-list(mean_dissimilarity=mean_dissimilarity,mean_dissimilarityci1=(unlist(ci0[1,1])),mean_dissimilarityci2=(unlist(ci0[1,2])),
mean_dissimilarity_direct=mean_dissimilarity_direct,mean_dissimilarity_directci1=unlist(ci0[2,1]),mean_dissimilarity_directci2=unlist(ci0[2,2]),
standardized_mean_dissimilarity=standardized_mean_dissimilarity,standardized_mean_dissimilarityci1=unlist(ci0[3,1]),standardized_mean_dissimilarityci2=unlist(ci0[3,2]),
standardized_mean_dissimilarity_direct=standardized_mean_dissimilarity_direct,standardized_mean_dissimilarity_directci1=unlist(ci0[4,1]),standardized_mean_dissimilarity_directci2=unlist(ci0[4,2]),
sd_dissimilarity=sd_dissimilarity,sd_dissimilarityci1=unlist(ci0[5,1]),sd_dissimilarityci2=unlist(ci0[5,2]),
sd_dissimilarity_direct=sd_dissimilarity_direct,sd_dissimilarity_directci1=unlist(ci0[6,1]),sd_dissimilarity_directci2=unlist(ci0[6,2]),
standardized_sd_dissimilarity=standardized_sd_dissimilarity,standardized_sd_dissimilarityci1=unlist(ci0[7,1]),standardized_sd_dissimilarityci2=unlist(ci0[7,2]),
standardized_sd_dissimilarity_direct=standardized_sd_dissimilarity_direct,standardized_sd_dissimilarity_directci1=unlist(ci0[8,1]),standardized_sd_dissimilarity_directci2=unlist(ci0[8,2]),
sparsity_dissimilarity=sparsity_dissimilarity,sparsity_dissimilarityci1=unlist(ci0[9,1]),sparsity_dissimilarityci2=unlist(ci0[9,2]),
standardized_sparsity_dissimilarity=standardized_sparsity_dissimilarity,standardized_sparsity_dissimilarityci1=unlist(ci0[10,1]),standardized_sparsity_dissimilarityci2=unlist(ci0[10,2]),
sparsity_dissimilarity_direct=sparsity_dissimilarity_direct,sparsity_dissimilarity_directci1=unlist(ci0[11,1]),sparsity_dissimilarity_directci2=unlist(ci0[11,2]),
standardized_sparsity_dissimilarity_direct=standardized_sparsity_dissimilarity_direct,standardized_sparsity_dissimilarity_directci1=unlist(ci0[12,1]),standardized_sparsity_dissimilarity_directci2=unlist(ci0[12,2])
)
rawdata<-list(meanmean=results$meanmean,
sdsd=results$sdsd,
sumall=as.data.frame(results$sumall),
meancombined3=as.data.frame(results$meancombined3),
meandi=as.data.frame(results$meandi),
sdcombined3=as.data.frame(results$sdcombined3),
sddi=as.data.frame(results$sddi),
selected1zero=as.data.frame(results$selected1zero),
selected2zero=as.data.frame(results$selected2zero)
)
Results<-list(results=(results2),rawdata=rawdata)
return(Results)
}