Nothing
R/gmoExon.R
In puma: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0)
Defines functions
gmoExon
Documented in
gmoExon
gmoExon<-function(
object
,exontype = c("Human", "Mouse", "Rat")
,background=FALSE
,gsnorm=c("median", "none", "mean", "meanlog")
,savepar=FALSE
,eps=1.0e-6
,addConstant = 0
,cl=NULL
,BatchFold=10
)
{
if(!is.null(cl))
clusterEvalQ(cl, library(puma))
library(pumadata);
chipnum<-length(sampleNames(object));
Human_transcript_NO <- NULL
Human_transcript_name <- NULL
Human_probes_transcripts <- NULL
Human_Location <-NULL
rm(Human_transcript_NO);
rm(Human_probes_transcripts);
rm(Human_transcript_name);
rm(Human_Location);
Mouse_transcript_NO <- NULL
Mouse_transcript_name <- NULL
Mouse_probes_transcripts <- NULL
Mouse_Location <-NULL
rm(Mouse_transcript_NO);
rm(Mouse_probes_transcripts);
rm(Mouse_transcript_name);
rm(Mouse_Location);
Rat_transcript_NO <- NULL
Rat_transcript_name <- NULL
Rat_probes_transcripts <- NULL
Rat_Location <-NULL
rm(Rat_transcript_NO);
rm(Rat_probes_transcripts);
rm(Rat_transcript_name);
rm(Rat_Location);
###load corresponding between gene and transcirpt and probes and alpha num of every gene
if(exontype[1]=="Human")
{
cat('Exon type is Human');
data(Human_transcript_NO);
Gene_T_NO =Human_transcript_NO ;
data(Human_probes_transcripts);
Probes_A_NUM = Human_probes_transcripts;
data(Human_transcript_name)
transcript_name <- Human_transcript_name;
data(Human_Location)
location=Human_Location
total_isoform<-dim(Human_transcript_name)[1];
}else if(exontype[1]=="Mouse")
{
cat('Exon type is Mouse');
data(Mouse_transcript_NO);
Gene_T_NO =Mouse_transcript_NO ;
data(Mouse_probes_transcripts);
Probes_A_NUM = Mouse_probes_transcripts;
data(Mouse_transcript_name)
transcript_name <- Mouse_transcript_name;
data(Mouse_Location)
location=Mouse_Location
total_isoform<-dim(Mouse_transcript_name)[1];
} else if(exontype[1]=="Rat")
{
cat('Exon type is Rat');
data(Rat_transcript_NO);
Gene_T_NO =Rat_transcript_NO ;
data(Rat_probes_transcripts);
Probes_A_NUM = Rat_probes_transcripts;
data(Rat_transcript_name)
transcript_name <- Rat_transcript_name;
data(Rat_Location)
location=Rat_Location
total_isoform<-dim(Rat_transcript_name)[1];
}
cat('\n');
###find pm of every gene######
# All_index <- affy::xy2indices(Gene_T_NO[,1],Gene_T_NO[,2],abatch=object); ###pos_x and pos_y to indices
All_index<-Gene_T_NO[,2]*2560+Gene_T_NO[,1]+1
Gene_T_INdex = cbind( Gene_T_NO[,3],All_index); ###table of gene transcript index
unique_index <- unique(All_index);
if(background == TRUE)
{
for (i in c(1:chipnum)){
m<-min(pm(object)[,i])
pm(object)[,i]<-pm(object)[,i]-m+1
}
}
pm_g <- oligo::pm(object,target='probeset'); ##pm of cel
Length_unique_index <- length(unique_index);
if(chipnum==1)
{
pm <- pm_g[location] ##nofind pm use 1 to substitute for one chips
for (j in c(1:Length_unique_index))
{
if(is.na(pm[j]))
pm[j] <- 1;
}
}else
{
pm <- pm_g[location,] ##nofind pm use 1 to substitute for multi chips
for (j in c(1:Length_unique_index))
{
if(is.na(pm[j,2]))
{
pm[j,] <- 1;
}
}
}
rm(location)
rm(Gene_T_NO)
pm <- cbind(unique_index,pm); ##Table of PM
rm(unique_index)
total_probes_everygene <- Probes_A_NUM[,1]; ##total num of probes for everygene
rm(pm_g);
st <- 0;
ed <- 0;
Total_genes <- dim(Probes_A_NUM)[1]; ##total num of genes
unique_probes_everygene <- seq(1,Total_genes,by=1);
for(i in c(1:Total_genes ))
{
ed <- st + total_probes_everygene[i];
st <- st + 1;
unique_probes_everygene[i] <- length(unique(All_index[st:ed]));
st<-ed;
}
alpha_num_everygene <- Probes_A_NUM[,2]; ###alpha num of everygene
rm(All_index)
gc();
####calculate pm_index and gt_index##########
prctiles <- 0.01*c(5, 25, 50, 75, 95);
len_pc<-length(prctiles);
pm_index <- c();
gt_index <- c();
pmst=0;
gtst=0;
for(i in c(1:Total_genes ))
{
pm_index[i] <- pmst;
pmst <- pmst + unique_probes_everygene[i];
gt_index[i] <- gtst;
gtst <- gtst+ total_probes_everygene[i];
}
pm_index[Total_genes+1]<-pmst
gt_index[Total_genes+1]<-gtst
expectedCompletionTime <- system.time(
res <-
.Call(
"gme_c"
, pm
, Gene_T_INdex
, unique_probes_everygene
, total_probes_everygene
, alpha_num_everygene
, 10
, prctiles
, len_pc
, savepar
, eps
)
)[3];
if(is.null(cl))
{
expectedCompletionTime <- expectedCompletionTime*Total_genes/10;
}else
{
expectedCompletionTime <- expectedCompletionTime*Total_genes/10/length(cl);
}
if (expectedCompletionTime < 120){
expectedTimeString <- paste(round(expectedCompletionTime, 0), "seconds")
}else if (expectedCompletionTime < 7200){
expectedTimeString <- paste(round(expectedCompletionTime/60, 0), "minutes")
}else if (expectedCompletionTime < 172800){
expectedTimeString <- paste(round(expectedCompletionTime/3600, 0), "hours")
}else
{ expectedTimeString <- paste(round(expectedCompletionTime/172800, 0), "days")
}
cat(paste("optimise expected completion time is", expectedTimeString,"\n"))
if(is.null(cl))
{
####optimise#####
res <-
.Call(
"gme_c"
, pm
, Gene_T_INdex
, unique_probes_everygene
, total_probes_everygene
, alpha_num_everygene
, Total_genes
, prctiles
, len_pc
, savepar
, eps
)
}else
{
cl_genes_num<-matrix(0,1,ncol=BatchFold*length(cl))
cl_genes_num_temp = trunc(Total_genes/(BatchFold*length(cl)));
for (j in c(1:(BatchFold*length(cl)-1))){
cl_genes_num[1,j]<-cl_genes_num_temp
}
cl_genes_num[1,BatchFold*length(cl)]<-Total_genes-cl_genes_num_temp*(BatchFold*length(cl)-1)
paramsList<-list();
if(length(cl)>1)
{
for (i in 1:(BatchFold*length(cl)))
{
paramsList[[i]] <- list(
pm[(pm_index[sum(cl_genes_num[1,0:(i-1)])+1]+1):pm_index[sum(cl_genes_num[1,0:i])+1],] ###pm
, Gene_T_INdex[(gt_index[sum(cl_genes_num[1,0:(i-1)])+1]+1):gt_index[sum(cl_genes_num[1,0:i])+1],] ###Gene_T_INdex
, unique_probes_everygene[(sum(cl_genes_num[1,0:(i-1)])+1):(sum(cl_genes_num[1,0:i]))] ###unique_probes_everygene
, total_probes_everygene[(sum(cl_genes_num[1,0:(i-1)])+1):(sum(cl_genes_num[1,0:i]))] ###total_probes_everygene
, alpha_num_everygene[(sum(cl_genes_num[1,0:(i-1)])+1):(sum(cl_genes_num[1,0:i]))] ###alpha_num_everygene
, cl_genes_num[1,i] ###total_genes
);
#cat(i*cl_genes_num);
##cat('\n');
}
}
cat('start parallel compute...');
temp_res <- clusterApplyLB(
cl
,paramsList
,function(
params
, prctiles
, len_pc
, savepar
, eps
)
{
res <- .Call(
"gme_c"
, params[[1]] ###pm
, params[[2]] ###Gene_T_INdex
, params[[3]] ###unique_probes_everygene
, params[[4]] ###total_probes_everygene
, params[[5]] ###alpha_num_everygene
, params[[6]]
, prctiles
, len_pc ###Total_genes
, savepar ###savepar
, eps ###eps
)
}
, prctiles
, len_pc
, savepar
, eps
)
cl_isoform_num<-c()
for(i in 1:(BatchFold*length(cl))){
cl_isoform_num[i]<-(length(temp_res[[i]][,1])-cl_genes_num[1,i]*7)/7
}
expr_g<-c();
se_g<-c();
expr_t<-c();
se_t<-c();
prc5_g<-c();
prc25_g<-c();
prc50_g<-c();
prc75_g<-c();
prc95_g<-c();
prc5_t<-c();
prc25_t<-c();
prc50_t<-c();
prc75_t<-c();
prc95_t<-c();
res_t<-c();
for(i in 1:(BatchFold*length(cl)))
{
expr_g <- rbind(expr_g,temp_res[[i]][1:cl_genes_num[1,i],])
se_g <- rbind(se_g,temp_res[[i]][(cl_genes_num[1,i]+1):(2*cl_genes_num[1,i]),])
prc5_g <- rbind(prc5_g,temp_res[[i]][(2*cl_genes_num[1,i]+1):(3*cl_genes_num[1,i]),])
prc25_g <- rbind(prc25_g,temp_res[[i]][(3*cl_genes_num[1,i]+1):(4*cl_genes_num[1,i]),])
prc50_g <- rbind(prc50_g,temp_res[[i]][(4*cl_genes_num[1,i]+1):(5*cl_genes_num[1,i]),])
prc75_g <- rbind(prc75_g,temp_res[[i]][(5*cl_genes_num[1,i]+1):(6*cl_genes_num[1,i]),])
prc95_g <- rbind(prc95_g,temp_res[[i]][(6*cl_genes_num[1,i]+1):(7*cl_genes_num[1,i]),])
expr_t<-rbind(expr_t,temp_res[[i]][(cl_genes_num[1,i]*7+1):(cl_genes_num[1,i]*7+cl_isoform_num[i]),])
se_t<-rbind(se_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+cl_isoform_num[i]):(cl_genes_num[1,i]*7+2*cl_isoform_num[i]),])
prc5_t<-rbind(prc5_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+2*cl_isoform_num[i]):(cl_genes_num[1,i]*7+3*cl_isoform_num[i]),])
prc25_t<-rbind(prc25_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+3*cl_isoform_num[i]):(cl_genes_num[1,i]*7+4*cl_isoform_num[i]),])
prc50_t<-rbind(prc50_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+4*cl_isoform_num[i]):(cl_genes_num[1,i]*7+5*cl_isoform_num[i]),])
prc75_t<-rbind(prc75_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+5*cl_isoform_num[i]):(cl_genes_num[1,i]*7+6*cl_isoform_num[i]),])
prc95_t<-rbind(prc95_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+6*cl_isoform_num[i]):(cl_genes_num[1,i]*7+7*cl_isoform_num[i]),])
}
}
if(!is.null(cl))
{
stopCluster(cl);
}
if(is.null(cl)){
res_g<-res[1:(Total_genes*7),]
res_t<-res[(Total_genes*7+1):((Total_genes+total_isoform)*7),]
expr_g <- matrix(res_g[c(1:Total_genes),],Total_genes,chipnum);
se_g <- matrix(res_g[c((Total_genes+1):(2*Total_genes)),],Total_genes,chipnum);
prc5_g <- matrix(res_g[c((2*Total_genes+1):(3*Total_genes)),],Total_genes,chipnum)
prc25_g <- matrix(res_g[c((3*Total_genes+1):(4*Total_genes)),],Total_genes,chipnum)
prc50_g <- matrix(res_g[c((4*Total_genes+1):(5*Total_genes)),],Total_genes,chipnum)
prc75_g <- matrix(res_g[c((5*Total_genes+1):(6*Total_genes)),],Total_genes,chipnum)
prc95_g <- matrix(res_g[c((6*Total_genes+1):(7*Total_genes)),],Total_genes,chipnum)
expr_t <- matrix(res_t[c(1:total_isoform),],total_isoform,chipnum);
se_t <- matrix(res_t[c((total_isoform+1):(2*total_isoform)),],total_isoform,chipnum);
prc5_t <- matrix(res_t[c((2*total_isoform+1):(3*total_isoform)),],total_isoform,chipnum)
prc25_t <- matrix(res_t[c((3*total_isoform+1):(4*total_isoform)),],total_isoform,chipnum)
prc50_t <- matrix(res_t[c((4*total_isoform+1):(5*total_isoform)),],total_isoform,chipnum)
prc75_t <- matrix(res_t[c((5*total_isoform+1):(6*total_isoform)),],total_isoform,chipnum)
prc95_t <- matrix(res_t[c((6*total_isoform+1):(7*total_isoform)),],total_isoform,chipnum)
}
rm(pm)
rm(pm_index)
rm(gt_index)
rm(Gene_T_INdex);
rm(res);
gc();
###save result#####
###gene_names <- rownames(Probes_A_NUM);
cat("\n")
cat("Gene and transcirpt expression values are returned.\n");
if (gsnorm[1]=="mean")
{
expr_temp <- as.data.frame(2^expr_g)
expr_temp <-as.matrix(expr_temp)
expr_temp[apply(is.infinite(expr_temp), FUN = any, 1), ] = exp(700)
chipm <- apply(expr_temp,2,mean)
chipm <- chipm/chipm[1]
#expr_g <- as.matrix(log2(expr_g))
for (i in 1:chipnum)
{
expr_g[,i] <- expr_g[,i]-log2(chipm[i])
se_g[,i]<- se_g[,i]/chipm[i]
prc5_g[,i] <- prc5_g[,i]-log2(chipm[i])
prc25_g[,i] <- prc25_g[,i]-log2(chipm[i])
prc50_g[,i] <- prc50_g[,i]-log2(chipm[i])
prc75_g[,i] <- prc75_g[,i]-log2(chipm[i])
prc95_g[,i] <- prc95_g[,i]-log2(chipm[i])
}
rm(expr_temp);
}else if (gsnorm[1]=="median")
{
expr_g <- as.data.frame(2^expr_g)
chipm <- apply(expr_g,2,median)
chipm <- chipm/chipm[1]
expr_g <- as.matrix(log2(expr_g))
for (i in 1:chipnum)
{
expr_g[,i] <- expr_g[,i]-log2(chipm[i])
se_g[,i]<- se_g[,i]/chipm[i]
prc5_g[,i] <- prc5_g[,i]-log2(chipm[i])
prc25_g[,i] <- prc25_g[,i]-log2(chipm[i])
prc50_g[,i] <- prc50_g[,i]-log2(chipm[i])
prc75_g[,i] <- prc75_g[,i]-log2(chipm[i])
prc95_g[,i] <- prc95_g[,i]-log2(chipm[i])
}
}else if (gsnorm[1]=="meanlog")
{
chipm <- apply(expr_g,2,mean)
chipm <- chipm-chipm[1]
for (i in 1:chipnum)
{
if(i!=1)
{
expr_g[,i] <- expr_g[,i]-chipm[i]
se_g[,i]<- se_g[,i]/chipm[i]
prc5_g[,i] <- prc5_g[,i]-chipm[i]
prc25_g[,i] <- prc25_g[,i]-chipm[i]
prc50_g[,i] <- prc50_g[,i]-chipm[i]
prc75_g[,i] <- prc75_g[,i]-chipm[i]
prc95_g[,i] <- prc95_g[,i]-chipm[i]
}
}
}
if (gsnorm[1]=="mean")
{
expr_temp <- as.data.frame(2^expr_t)
expr_temp <-as.matrix(expr_temp)
expr_temp[apply(is.infinite(expr_temp), FUN = any, 1), ] = exp(700)
chipm <- apply(expr_temp,2,mean)
chipm <- chipm/chipm[1]
#expr_t <- as.matrix(log2(expr_t))
for (i in 1:chipnum)
{
expr_t[,i] <- expr_t[,i]-log2(chipm[i])
se_t[,i]<- se_t[,i]/chipm[i]
prc5_t[,i] <- prc5_t[,i]-log2(chipm[i])
prc25_t[,i] <- prc25_t[,i]-log2(chipm[i])
prc50_t[,i] <- prc50_t[,i]-log2(chipm[i])
prc75_t[,i] <- prc75_t[,i]-log2(chipm[i])
prc95_t[,i] <- prc95_t[,i]-log2(chipm[i])
}
rm(expr_temp);
} else if (gsnorm[1]=="median")
{
expr_t <- as.data.frame(2^expr_t)
chipm <- apply(expr_t,2,median)
chipm <- chipm/chipm[1]
expr_t <- as.matrix(log2(expr_t))
for (i in 1:chipnum)
{
expr_t[,i] <- expr_t[,i]-log2(chipm[i])
se_t[,i]<- se_t[,i]/chipm[i]
prc5_t[,i] <- prc5_t[,i]-log2(chipm[i])
prc25_t[,i] <- prc25_t[,i]-log2(chipm[i])
prc50_t[,i] <- prc50_t[,i]-log2(chipm[i])
prc75_t[,i] <- prc75_t[,i]-log2(chipm[i])
prc95_t[,i] <- prc95_t[,i]-log2(chipm[i])
}
}else if (gsnorm[1]=="meanlog")
{
chipm <- apply(expr_t,2,mean)
chipm <- chipm-chipm[1]
for (i in 1:chipnum)
{
if(i!=1)
{
expr_t[,i] <- expr_t[,i]-chipm[i]
se_t[,i]<- se_t[,i]/chipm[i]
prc5_g[,i] <- prc5_g[,i]-chipm[i]
prc25_g[,i] <- prc25_g[,i]-chipm[i]
prc50_g[,i] <- prc50_g[,i]-chipm[i]
prc75_g[,i] <- prc75_g[,i]-chipm[i]
prc95_g[,i] <- prc95_g[,i]-chipm[i]
}
}
}
rownames(expr_t) <- transcript_name[,1];
colnames(expr_t) <- sampleNames(object)
rownames(se_t) <- transcript_name[,1];
colnames(se_t) <- sampleNames(object)
rownames(prc5_t) <- transcript_name[,1];
colnames(prc5_t) <- sampleNames(object)
rownames(prc25_t) <- transcript_name[,1];
colnames(prc25_t) <- sampleNames(object)
rownames(prc50_t) <- transcript_name[,1];
colnames(prc50_t) <- sampleNames(object);
rownames(prc75_t) <- transcript_name[,1];
colnames(prc75_t) <- sampleNames(object)
rownames(prc95_t) <- transcript_name[,1];
colnames(prc95_t) <- sampleNames(object)
rm(transcript_name);
gc();
phenodata <- phenoData(object)
annotation <- annotation(object)
description <- description(object)
return_exprReslt_t <- new(
"exprReslt"
, exprs=log2((2^expr_t)+addConstant)
, se.exprs=se_t
, phenoData = new(
"AnnotatedDataFrame"
, data=pData(object)
, varMetadata=data.frame(labelDescription=varLabels(phenoData(object)))
)
# , notes=notes
)
prcfive(return_exprReslt_t) <- prc5_t
prctwfive(return_exprReslt_t) <- prc25_t
prcfifty(return_exprReslt_t) <- prc50_t
prcsevfive(return_exprReslt_t) <- prc75_t
prcninfive(return_exprReslt_t) <- prc95_t
annotation(return_exprReslt_t) <- annotation(object)
description(return_exprReslt_t) <- description(object)
notes(return_exprReslt_t) <- notes(object)
rownames(expr_g) <-rownames(Probes_A_NUM);
colnames(expr_g) <- sampleNames(object)
rownames(se_g) <- rownames(Probes_A_NUM);
colnames(se_g) <- sampleNames(object)
rownames(prc5_g) <- rownames(Probes_A_NUM);
colnames(prc5_g) <- sampleNames(object)
rownames(prc25_g) <- rownames(Probes_A_NUM);
colnames(prc25_g) <- sampleNames(object)
rownames(prc50_g) <- rownames(Probes_A_NUM);
colnames(prc50_g) <- sampleNames(object)
rownames(prc75_g) <- rownames(Probes_A_NUM);
colnames(prc75_g) <- sampleNames(object)
rownames(prc95_g) <- rownames(Probes_A_NUM);
colnames(prc95_g) <- sampleNames(object)
phenodata <- phenoData(object)
annotation <- annotation(object)
description <- description(object)
return_exprReslt_g <- new(
"exprReslt"
, exprs=log2((2^expr_g)+addConstant)
, se.exprs=se_g
, phenoData = new(
"AnnotatedDataFrame"
, data=pData(object)
, varMetadata=data.frame(labelDescription=varLabels(phenoData(object)))
)
# , notes=notes
)
prcfive(return_exprReslt_g) <- prc5_g
prctwfive(return_exprReslt_g) <- prc25_g
prcfifty(return_exprReslt_g) <- prc50_g
prcsevfive(return_exprReslt_g) <- prc75_g
prcninfive(return_exprReslt_g) <- prc95_g
annotation(return_exprReslt_g) <- annotation(object)
description(return_exprReslt_g) <- description(object)
notes(return_exprReslt_g) <- notes(object)
rm(object);
gc();
return_exprReslt<-list(gene=return_exprReslt_g,transcript=return_exprReslt_t)
return (return_exprReslt)
}
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Defines functions gmoExon
Documented in gmoExon
gmoExon<-function(
object
,exontype = c("Human", "Mouse", "Rat")
,background=FALSE
,gsnorm=c("median", "none", "mean", "meanlog")
,savepar=FALSE
,eps=1.0e-6
,addConstant = 0
,cl=NULL
,BatchFold=10
)
{
if(!is.null(cl))
clusterEvalQ(cl, library(puma))
library(pumadata);
chipnum<-length(sampleNames(object));
Human_transcript_NO <- NULL
Human_transcript_name <- NULL
Human_probes_transcripts <- NULL
Human_Location <-NULL
rm(Human_transcript_NO);
rm(Human_probes_transcripts);
rm(Human_transcript_name);
rm(Human_Location);
Mouse_transcript_NO <- NULL
Mouse_transcript_name <- NULL
Mouse_probes_transcripts <- NULL
Mouse_Location <-NULL
rm(Mouse_transcript_NO);
rm(Mouse_probes_transcripts);
rm(Mouse_transcript_name);
rm(Mouse_Location);
Rat_transcript_NO <- NULL
Rat_transcript_name <- NULL
Rat_probes_transcripts <- NULL
Rat_Location <-NULL
rm(Rat_transcript_NO);
rm(Rat_probes_transcripts);
rm(Rat_transcript_name);
rm(Rat_Location);
###load corresponding between gene and transcirpt and probes and alpha num of every gene
if(exontype[1]=="Human")
{
cat('Exon type is Human');
data(Human_transcript_NO);
Gene_T_NO =Human_transcript_NO ;
data(Human_probes_transcripts);
Probes_A_NUM = Human_probes_transcripts;
data(Human_transcript_name)
transcript_name <- Human_transcript_name;
data(Human_Location)
location=Human_Location
total_isoform<-dim(Human_transcript_name)[1];
}else if(exontype[1]=="Mouse")
{
cat('Exon type is Mouse');
data(Mouse_transcript_NO);
Gene_T_NO =Mouse_transcript_NO ;
data(Mouse_probes_transcripts);
Probes_A_NUM = Mouse_probes_transcripts;
data(Mouse_transcript_name)
transcript_name <- Mouse_transcript_name;
data(Mouse_Location)
location=Mouse_Location
total_isoform<-dim(Mouse_transcript_name)[1];
} else if(exontype[1]=="Rat")
{
cat('Exon type is Rat');
data(Rat_transcript_NO);
Gene_T_NO =Rat_transcript_NO ;
data(Rat_probes_transcripts);
Probes_A_NUM = Rat_probes_transcripts;
data(Rat_transcript_name)
transcript_name <- Rat_transcript_name;
data(Rat_Location)
location=Rat_Location
total_isoform<-dim(Rat_transcript_name)[1];
}
cat('\n');
###find pm of every gene######
# All_index <- affy::xy2indices(Gene_T_NO[,1],Gene_T_NO[,2],abatch=object); ###pos_x and pos_y to indices
All_index<-Gene_T_NO[,2]*2560+Gene_T_NO[,1]+1
Gene_T_INdex = cbind( Gene_T_NO[,3],All_index); ###table of gene transcript index
unique_index <- unique(All_index);
if(background == TRUE)
{
for (i in c(1:chipnum)){
m<-min(pm(object)[,i])
pm(object)[,i]<-pm(object)[,i]-m+1
}
}
pm_g <- oligo::pm(object,target='probeset'); ##pm of cel
Length_unique_index <- length(unique_index);
if(chipnum==1)
{
pm <- pm_g[location] ##nofind pm use 1 to substitute for one chips
for (j in c(1:Length_unique_index))
{
if(is.na(pm[j]))
pm[j] <- 1;
}
}else
{
pm <- pm_g[location,] ##nofind pm use 1 to substitute for multi chips
for (j in c(1:Length_unique_index))
{
if(is.na(pm[j,2]))
{
pm[j,] <- 1;
}
}
}
rm(location)
rm(Gene_T_NO)
pm <- cbind(unique_index,pm); ##Table of PM
rm(unique_index)
total_probes_everygene <- Probes_A_NUM[,1]; ##total num of probes for everygene
rm(pm_g);
st <- 0;
ed <- 0;
Total_genes <- dim(Probes_A_NUM)[1]; ##total num of genes
unique_probes_everygene <- seq(1,Total_genes,by=1);
for(i in c(1:Total_genes ))
{
ed <- st + total_probes_everygene[i];
st <- st + 1;
unique_probes_everygene[i] <- length(unique(All_index[st:ed]));
st<-ed;
}
alpha_num_everygene <- Probes_A_NUM[,2]; ###alpha num of everygene
rm(All_index)
gc();
####calculate pm_index and gt_index##########
prctiles <- 0.01*c(5, 25, 50, 75, 95);
len_pc<-length(prctiles);
pm_index <- c();
gt_index <- c();
pmst=0;
gtst=0;
for(i in c(1:Total_genes ))
{
pm_index[i] <- pmst;
pmst <- pmst + unique_probes_everygene[i];
gt_index[i] <- gtst;
gtst <- gtst+ total_probes_everygene[i];
}
pm_index[Total_genes+1]<-pmst
gt_index[Total_genes+1]<-gtst
expectedCompletionTime <- system.time(
res <-
.Call(
"gme_c"
, pm
, Gene_T_INdex
, unique_probes_everygene
, total_probes_everygene
, alpha_num_everygene
, 10
, prctiles
, len_pc
, savepar
, eps
)
)[3];
if(is.null(cl))
{
expectedCompletionTime <- expectedCompletionTime*Total_genes/10;
}else
{
expectedCompletionTime <- expectedCompletionTime*Total_genes/10/length(cl);
}
if (expectedCompletionTime < 120){
expectedTimeString <- paste(round(expectedCompletionTime, 0), "seconds")
}else if (expectedCompletionTime < 7200){
expectedTimeString <- paste(round(expectedCompletionTime/60, 0), "minutes")
}else if (expectedCompletionTime < 172800){
expectedTimeString <- paste(round(expectedCompletionTime/3600, 0), "hours")
}else
{ expectedTimeString <- paste(round(expectedCompletionTime/172800, 0), "days")
}
cat(paste("optimise expected completion time is", expectedTimeString,"\n"))
if(is.null(cl))
{
####optimise#####
res <-
.Call(
"gme_c"
, pm
, Gene_T_INdex
, unique_probes_everygene
, total_probes_everygene
, alpha_num_everygene
, Total_genes
, prctiles
, len_pc
, savepar
, eps
)
}else
{
cl_genes_num<-matrix(0,1,ncol=BatchFold*length(cl))
cl_genes_num_temp = trunc(Total_genes/(BatchFold*length(cl)));
for (j in c(1:(BatchFold*length(cl)-1))){
cl_genes_num[1,j]<-cl_genes_num_temp
}
cl_genes_num[1,BatchFold*length(cl)]<-Total_genes-cl_genes_num_temp*(BatchFold*length(cl)-1)
paramsList<-list();
if(length(cl)>1)
{
for (i in 1:(BatchFold*length(cl)))
{
paramsList[[i]] <- list(
pm[(pm_index[sum(cl_genes_num[1,0:(i-1)])+1]+1):pm_index[sum(cl_genes_num[1,0:i])+1],] ###pm
, Gene_T_INdex[(gt_index[sum(cl_genes_num[1,0:(i-1)])+1]+1):gt_index[sum(cl_genes_num[1,0:i])+1],] ###Gene_T_INdex
, unique_probes_everygene[(sum(cl_genes_num[1,0:(i-1)])+1):(sum(cl_genes_num[1,0:i]))] ###unique_probes_everygene
, total_probes_everygene[(sum(cl_genes_num[1,0:(i-1)])+1):(sum(cl_genes_num[1,0:i]))] ###total_probes_everygene
, alpha_num_everygene[(sum(cl_genes_num[1,0:(i-1)])+1):(sum(cl_genes_num[1,0:i]))] ###alpha_num_everygene
, cl_genes_num[1,i] ###total_genes
);
#cat(i*cl_genes_num);
##cat('\n');
}
}
cat('start parallel compute...');
temp_res <- clusterApplyLB(
cl
,paramsList
,function(
params
, prctiles
, len_pc
, savepar
, eps
)
{
res <- .Call(
"gme_c"
, params[[1]] ###pm
, params[[2]] ###Gene_T_INdex
, params[[3]] ###unique_probes_everygene
, params[[4]] ###total_probes_everygene
, params[[5]] ###alpha_num_everygene
, params[[6]]
, prctiles
, len_pc ###Total_genes
, savepar ###savepar
, eps ###eps
)
}
, prctiles
, len_pc
, savepar
, eps
)
cl_isoform_num<-c()
for(i in 1:(BatchFold*length(cl))){
cl_isoform_num[i]<-(length(temp_res[[i]][,1])-cl_genes_num[1,i]*7)/7
}
expr_g<-c();
se_g<-c();
expr_t<-c();
se_t<-c();
prc5_g<-c();
prc25_g<-c();
prc50_g<-c();
prc75_g<-c();
prc95_g<-c();
prc5_t<-c();
prc25_t<-c();
prc50_t<-c();
prc75_t<-c();
prc95_t<-c();
res_t<-c();
for(i in 1:(BatchFold*length(cl)))
{
expr_g <- rbind(expr_g,temp_res[[i]][1:cl_genes_num[1,i],])
se_g <- rbind(se_g,temp_res[[i]][(cl_genes_num[1,i]+1):(2*cl_genes_num[1,i]),])
prc5_g <- rbind(prc5_g,temp_res[[i]][(2*cl_genes_num[1,i]+1):(3*cl_genes_num[1,i]),])
prc25_g <- rbind(prc25_g,temp_res[[i]][(3*cl_genes_num[1,i]+1):(4*cl_genes_num[1,i]),])
prc50_g <- rbind(prc50_g,temp_res[[i]][(4*cl_genes_num[1,i]+1):(5*cl_genes_num[1,i]),])
prc75_g <- rbind(prc75_g,temp_res[[i]][(5*cl_genes_num[1,i]+1):(6*cl_genes_num[1,i]),])
prc95_g <- rbind(prc95_g,temp_res[[i]][(6*cl_genes_num[1,i]+1):(7*cl_genes_num[1,i]),])
expr_t<-rbind(expr_t,temp_res[[i]][(cl_genes_num[1,i]*7+1):(cl_genes_num[1,i]*7+cl_isoform_num[i]),])
se_t<-rbind(se_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+cl_isoform_num[i]):(cl_genes_num[1,i]*7+2*cl_isoform_num[i]),])
prc5_t<-rbind(prc5_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+2*cl_isoform_num[i]):(cl_genes_num[1,i]*7+3*cl_isoform_num[i]),])
prc25_t<-rbind(prc25_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+3*cl_isoform_num[i]):(cl_genes_num[1,i]*7+4*cl_isoform_num[i]),])
prc50_t<-rbind(prc50_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+4*cl_isoform_num[i]):(cl_genes_num[1,i]*7+5*cl_isoform_num[i]),])
prc75_t<-rbind(prc75_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+5*cl_isoform_num[i]):(cl_genes_num[1,i]*7+6*cl_isoform_num[i]),])
prc95_t<-rbind(prc95_t,temp_res[[i]][(1+ cl_genes_num[1,i]*7+6*cl_isoform_num[i]):(cl_genes_num[1,i]*7+7*cl_isoform_num[i]),])
}
}
if(!is.null(cl))
{
stopCluster(cl);
}
if(is.null(cl)){
res_g<-res[1:(Total_genes*7),]
res_t<-res[(Total_genes*7+1):((Total_genes+total_isoform)*7),]
expr_g <- matrix(res_g[c(1:Total_genes),],Total_genes,chipnum);
se_g <- matrix(res_g[c((Total_genes+1):(2*Total_genes)),],Total_genes,chipnum);
prc5_g <- matrix(res_g[c((2*Total_genes+1):(3*Total_genes)),],Total_genes,chipnum)
prc25_g <- matrix(res_g[c((3*Total_genes+1):(4*Total_genes)),],Total_genes,chipnum)
prc50_g <- matrix(res_g[c((4*Total_genes+1):(5*Total_genes)),],Total_genes,chipnum)
prc75_g <- matrix(res_g[c((5*Total_genes+1):(6*Total_genes)),],Total_genes,chipnum)
prc95_g <- matrix(res_g[c((6*Total_genes+1):(7*Total_genes)),],Total_genes,chipnum)
expr_t <- matrix(res_t[c(1:total_isoform),],total_isoform,chipnum);
se_t <- matrix(res_t[c((total_isoform+1):(2*total_isoform)),],total_isoform,chipnum);
prc5_t <- matrix(res_t[c((2*total_isoform+1):(3*total_isoform)),],total_isoform,chipnum)
prc25_t <- matrix(res_t[c((3*total_isoform+1):(4*total_isoform)),],total_isoform,chipnum)
prc50_t <- matrix(res_t[c((4*total_isoform+1):(5*total_isoform)),],total_isoform,chipnum)
prc75_t <- matrix(res_t[c((5*total_isoform+1):(6*total_isoform)),],total_isoform,chipnum)
prc95_t <- matrix(res_t[c((6*total_isoform+1):(7*total_isoform)),],total_isoform,chipnum)
}
rm(pm)
rm(pm_index)
rm(gt_index)
rm(Gene_T_INdex);
rm(res);
gc();
###save result#####
###gene_names <- rownames(Probes_A_NUM);
cat("\n")
cat("Gene and transcirpt expression values are returned.\n");
if (gsnorm[1]=="mean")
{
expr_temp <- as.data.frame(2^expr_g)
expr_temp <-as.matrix(expr_temp)
expr_temp[apply(is.infinite(expr_temp), FUN = any, 1), ] = exp(700)
chipm <- apply(expr_temp,2,mean)
chipm <- chipm/chipm[1]
#expr_g <- as.matrix(log2(expr_g))
for (i in 1:chipnum)
{
expr_g[,i] <- expr_g[,i]-log2(chipm[i])
se_g[,i]<- se_g[,i]/chipm[i]
prc5_g[,i] <- prc5_g[,i]-log2(chipm[i])
prc25_g[,i] <- prc25_g[,i]-log2(chipm[i])
prc50_g[,i] <- prc50_g[,i]-log2(chipm[i])
prc75_g[,i] <- prc75_g[,i]-log2(chipm[i])
prc95_g[,i] <- prc95_g[,i]-log2(chipm[i])
}
rm(expr_temp);
}else if (gsnorm[1]=="median")
{
expr_g <- as.data.frame(2^expr_g)
chipm <- apply(expr_g,2,median)
chipm <- chipm/chipm[1]
expr_g <- as.matrix(log2(expr_g))
for (i in 1:chipnum)
{
expr_g[,i] <- expr_g[,i]-log2(chipm[i])
se_g[,i]<- se_g[,i]/chipm[i]
prc5_g[,i] <- prc5_g[,i]-log2(chipm[i])
prc25_g[,i] <- prc25_g[,i]-log2(chipm[i])
prc50_g[,i] <- prc50_g[,i]-log2(chipm[i])
prc75_g[,i] <- prc75_g[,i]-log2(chipm[i])
prc95_g[,i] <- prc95_g[,i]-log2(chipm[i])
}
}else if (gsnorm[1]=="meanlog")
{
chipm <- apply(expr_g,2,mean)
chipm <- chipm-chipm[1]
for (i in 1:chipnum)
{
if(i!=1)
{
expr_g[,i] <- expr_g[,i]-chipm[i]
se_g[,i]<- se_g[,i]/chipm[i]
prc5_g[,i] <- prc5_g[,i]-chipm[i]
prc25_g[,i] <- prc25_g[,i]-chipm[i]
prc50_g[,i] <- prc50_g[,i]-chipm[i]
prc75_g[,i] <- prc75_g[,i]-chipm[i]
prc95_g[,i] <- prc95_g[,i]-chipm[i]
}
}
}
if (gsnorm[1]=="mean")
{
expr_temp <- as.data.frame(2^expr_t)
expr_temp <-as.matrix(expr_temp)
expr_temp[apply(is.infinite(expr_temp), FUN = any, 1), ] = exp(700)
chipm <- apply(expr_temp,2,mean)
chipm <- chipm/chipm[1]
#expr_t <- as.matrix(log2(expr_t))
for (i in 1:chipnum)
{
expr_t[,i] <- expr_t[,i]-log2(chipm[i])
se_t[,i]<- se_t[,i]/chipm[i]
prc5_t[,i] <- prc5_t[,i]-log2(chipm[i])
prc25_t[,i] <- prc25_t[,i]-log2(chipm[i])
prc50_t[,i] <- prc50_t[,i]-log2(chipm[i])
prc75_t[,i] <- prc75_t[,i]-log2(chipm[i])
prc95_t[,i] <- prc95_t[,i]-log2(chipm[i])
}
rm(expr_temp);
} else if (gsnorm[1]=="median")
{
expr_t <- as.data.frame(2^expr_t)
chipm <- apply(expr_t,2,median)
chipm <- chipm/chipm[1]
expr_t <- as.matrix(log2(expr_t))
for (i in 1:chipnum)
{
expr_t[,i] <- expr_t[,i]-log2(chipm[i])
se_t[,i]<- se_t[,i]/chipm[i]
prc5_t[,i] <- prc5_t[,i]-log2(chipm[i])
prc25_t[,i] <- prc25_t[,i]-log2(chipm[i])
prc50_t[,i] <- prc50_t[,i]-log2(chipm[i])
prc75_t[,i] <- prc75_t[,i]-log2(chipm[i])
prc95_t[,i] <- prc95_t[,i]-log2(chipm[i])
}
}else if (gsnorm[1]=="meanlog")
{
chipm <- apply(expr_t,2,mean)
chipm <- chipm-chipm[1]
for (i in 1:chipnum)
{
if(i!=1)
{
expr_t[,i] <- expr_t[,i]-chipm[i]
se_t[,i]<- se_t[,i]/chipm[i]
prc5_g[,i] <- prc5_g[,i]-chipm[i]
prc25_g[,i] <- prc25_g[,i]-chipm[i]
prc50_g[,i] <- prc50_g[,i]-chipm[i]
prc75_g[,i] <- prc75_g[,i]-chipm[i]
prc95_g[,i] <- prc95_g[,i]-chipm[i]
}
}
}
rownames(expr_t) <- transcript_name[,1];
colnames(expr_t) <- sampleNames(object)
rownames(se_t) <- transcript_name[,1];
colnames(se_t) <- sampleNames(object)
rownames(prc5_t) <- transcript_name[,1];
colnames(prc5_t) <- sampleNames(object)
rownames(prc25_t) <- transcript_name[,1];
colnames(prc25_t) <- sampleNames(object)
rownames(prc50_t) <- transcript_name[,1];
colnames(prc50_t) <- sampleNames(object);
rownames(prc75_t) <- transcript_name[,1];
colnames(prc75_t) <- sampleNames(object)
rownames(prc95_t) <- transcript_name[,1];
colnames(prc95_t) <- sampleNames(object)
rm(transcript_name);
gc();
phenodata <- phenoData(object)
annotation <- annotation(object)
description <- description(object)
return_exprReslt_t <- new(
"exprReslt"
, exprs=log2((2^expr_t)+addConstant)
, se.exprs=se_t
, phenoData = new(
"AnnotatedDataFrame"
, data=pData(object)
, varMetadata=data.frame(labelDescription=varLabels(phenoData(object)))
)
# , notes=notes
)
prcfive(return_exprReslt_t) <- prc5_t
prctwfive(return_exprReslt_t) <- prc25_t
prcfifty(return_exprReslt_t) <- prc50_t
prcsevfive(return_exprReslt_t) <- prc75_t
prcninfive(return_exprReslt_t) <- prc95_t
annotation(return_exprReslt_t) <- annotation(object)
description(return_exprReslt_t) <- description(object)
notes(return_exprReslt_t) <- notes(object)
rownames(expr_g) <-rownames(Probes_A_NUM);
colnames(expr_g) <- sampleNames(object)
rownames(se_g) <- rownames(Probes_A_NUM);
colnames(se_g) <- sampleNames(object)
rownames(prc5_g) <- rownames(Probes_A_NUM);
colnames(prc5_g) <- sampleNames(object)
rownames(prc25_g) <- rownames(Probes_A_NUM);
colnames(prc25_g) <- sampleNames(object)
rownames(prc50_g) <- rownames(Probes_A_NUM);
colnames(prc50_g) <- sampleNames(object)
rownames(prc75_g) <- rownames(Probes_A_NUM);
colnames(prc75_g) <- sampleNames(object)
rownames(prc95_g) <- rownames(Probes_A_NUM);
colnames(prc95_g) <- sampleNames(object)
phenodata <- phenoData(object)
annotation <- annotation(object)
description <- description(object)
return_exprReslt_g <- new(
"exprReslt"
, exprs=log2((2^expr_g)+addConstant)
, se.exprs=se_g
, phenoData = new(
"AnnotatedDataFrame"
, data=pData(object)
, varMetadata=data.frame(labelDescription=varLabels(phenoData(object)))
)
# , notes=notes
)
prcfive(return_exprReslt_g) <- prc5_g
prctwfive(return_exprReslt_g) <- prc25_g
prcfifty(return_exprReslt_g) <- prc50_g
prcsevfive(return_exprReslt_g) <- prc75_g
prcninfive(return_exprReslt_g) <- prc95_g
annotation(return_exprReslt_g) <- annotation(object)
description(return_exprReslt_g) <- description(object)
notes(return_exprReslt_g) <- notes(object)
rm(object);
gc();
return_exprReslt<-list(gene=return_exprReslt_g,transcript=return_exprReslt_t)
return (return_exprReslt)
}
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