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down_miRNA.v2.R
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down_miRNA.v2.R
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# LGMB Laboratory of Genetics and Molecular Biology
# BiT Bioinformatics Team
#
# Project...: down
# Date......: 22/05/2012
# Author(s).: matheus
###############################################################################
## Carregar bibliotecas
library('dataframes2xls')
library('HTqPCR')
library('genefilter')
library("AgiMicroRna")
library("miRNApath")
library("gplots")
library('reshape')
#library("clusterProfiler")
#library("ReactomePA")
library('RmiR.Hs.miRNA')
library('org.Hs.eg.db')
annpkg <- 'hgug4112a.db'
library(package=annpkg,character.only = TRUE)
source("/work/projects/manalysis/myArray/myArrayFunctions.R")
## Definir Diretorio base
dir_base = "/work/projects/bit_data"
projeto = "down"
analises = "htqpcr/miRNA"
mywd <- paste(dir_base,projeto,analises,sep="/")
setwd(mywd)
getwd()
## Ler arquivos
# Grupo A
filesA <- read.delim(file.path(mywd, "targetsA384.txt"))
rawA <- readCtData(files = filesA$File, path = mywd, n.features=384, feature=2, type=3, Ct=4, header=TRUE)
sampleNames(rawA) <- gsub('raw/A/', '', sampleNames(rawA))
filesA$Names <- gsub('raw/A/|.txt', '', filesA$File)
featureClass(rawA) <- filesA$Treatment
show(rawA)
# Grupo B
filesB <- read.delim(file.path(mywd, "targetsB384.txt"))
rawB <- readCtData(files = filesB$File, path = mywd, n.features=384, feature=2, type=3, Ct=4, header=TRUE)
sampleNames(rawB) <- gsub('raw/B/', '', sampleNames(rawB))
filesB$Names <- gsub('raw/B/|.txt', '', filesB$File)
featureClass(rawB) <- filesB$Treatment
show(rawB)
## Plots densidade
png(paste("quality/groupA","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(rawA)
dev.off()
png(paste("quality/groupB","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(rawB)
dev.off()
## Definir categorias - Unreliable, Undetermined, Ok
## Grupo A
# quantile entre replicas
par(ask=T)
sink(paste("results/setCategoryA_groups_Rep_Ctmax_32_quantile_0.75.txt",sep=""))
rawA.cat <- setCategory( rawA,
Ct.max = 32,
groups = filesA$Rep,
quantile = 0.75,
replicates=TRUE,
verbose=TRUE,
flag=FALSE,
plot=F)
sink()
## Grupo B
# quantile entre replicas
sink(paste("results/setCategoryB_groups_Rep_Ctmax_32_quantile_0.75.txt",sep=""))
rawB.cat <- setCategory( rawB,
Ct.max = 32,
groups = filesB$Rep,
quantile = 0.75,
replicates=TRUE,
verbose=TRUE,
flag=FALSE,
plot=F)
sink()
## Grupo A
# quantile entre condicoes
sink(paste("results/setCategoryA_groups_Rep_Treatment_Ctmax_32_quantile_0.90.txt",sep=""))
rawA.cat.2 <- setCategory( rawA.cat,
Ct.max = 32,
groups = filesA$Treatment,
quantile = 0.90,
replicates=TRUE,
verbose=TRUE,
flag=FALSE,
plot=TRUE)
sink()
## Grupo B
# quantile entre condicoes
sink(paste("results/setCategoryB_groups_Rep_Treatment_Ctmax_32_quantile_0.90.txt",sep=""))
rawB.cat.2 <- setCategory( rawB.cat,
Ct.max = 32,
groups = filesB$Treatment,
quantile = 0.90,
replicates=TRUE,
verbose=TRUE,
flag=FALSE,
plot=F)
sink()
par(ask=F, mfrow=c(1,1))
## Plot Categories
system(paste("mkdir -p results/plots/ 2>&1",sep=""), intern=TRUE)
png(filename=paste("results/plots/plotCtCategoryA_groups_Rep.png",sep=""),
bg="white", res=300, width=2000, height=2000)
plotCtCategory(rawA.cat)
dev.off()
png(filename=paste("results/plots/plotCtCategoryB_groups_Rep.png",sep=""),
bg="white", res=300, width=2000, height=2000)
plotCtCategory(rawB.cat)
dev.off()
png(filename=paste("results/plots/plotCtCategoryA_groups_Rep_Treatment.png",sep=""),
bg="white", res=300, width=2000, height=2000)
plotCtCategory(rawA.cat.2)
dev.off()
png(filename=paste("results/plots/plotCtCategoryB_groups_Rep_Treatment.png",sep=""),
bg="white", res=300, width=2000, height=2000)
plotCtCategory(rawB.cat.2)
dev.off()
## Filtro - Undetermined
rawA.filt <- filterCategory(rawA.cat.2, na.categories = c("Undetermined")) ## Inclusao de NAs
names(which(apply(exprs(rawA.filt),1,function(x){ is.element(NA,x) })==TRUE))
names(which(apply(featureCategory(rawA.cat.2),1,function(x){ is.element("Undetermined",x) })==TRUE))
write.table(names(which(apply(featureCategory(rawA.cat.2),1,function(x){ is.element("Undetermined",x) })==TRUE)),
"/tmp/verify/mymicrosA.txt", row.names=F, col.names=F, quote=F)
rawB.filt <- filterCategory(rawB.cat.2, na.categories = c("Undetermined")) ## Inclusao de NAs
write.table(names(which(apply(exprs(rawB.filt),1,function(x){ is.element(NA,x) })==TRUE)), "/tmp/microsUndeterminedB", row.names=F, quote=F)
## Plots densidade
png(paste("quality/groupA","filterUndetermined","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(rawA.filt)
dev.off()
png(paste("quality/groupB","filterUndetermined","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(rawB.filt)
dev.off()
## Cluster Categories
png(filename=paste("results/plots/Clustering_CtCategoryA.png",sep=""),
bg="white", res=300, width=2000, height=2000)
plotCtCategory(rawA.filt, by.feature = TRUE, cexRow = 0.1)
dev.off()
png(filename=paste("results/plots/Clustering_CtCategoryB.png",sep=""),
bg="white", res=300, width=2000, height=2000)
plotCtCategory(rawB.filt, by.feature = TRUE, cexRow = 0.1)
dev.off()
## Normalização
sink(paste("results/normalizeCtDataA_deltaCt.txt",sep=""))
dA.norm <- normalizeCtData( rawA.filt,
norm = "deltaCt",
deltaCt.genes = c("MammU6-4395470", "RNU44-4373384", "RNU48-4373383"),
#deltaCt.genes = c("MammU6-4395470")
)
sink()
sink(paste("results/normalizeCtDataB_deltaCt.txt",sep=""))
dB.norm <- normalizeCtData( rawB.filt,
norm = "deltaCt",
deltaCt.genes = c("U6 snRNA-001973", "RNU44-001094", "RNU48-001006")
#deltaCt.genes = c("U6 snRNA-001973")
)
sink()
## Plots densidade
png(paste("quality/groupA","filterUndetermined","norm","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(dA.norm)
dev.off()
png(paste("quality/groupB","filterUndetermined","norm","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(dB.norm)
dev.off()
## Filtro
#sink(paste("results/filterCtDataA_ncat_",as.integer(0.75*length(filesA$Name)),".txt",sep=""))
sink(paste("results/filterCtDataA",".txt",sep=""))
qFiltA <- filterCtData( dA.norm,
remove.type = "Endogenous Control",
remove.name = c("MammU6-4395470", "RNU44-4373384", "RNU48-4373383"),
#remove.category = c("Undetermined","Unreliable"),
#remove.category = c("Unreliable"),
#remove.category = c("Undetermined"),
n.category = 0
#n.category = as.integer(0.75*length(filesA$Name))
)
sink()
#sink(paste("results/filterCtDataB_ncat_",as.integer(0.75*length(filesB$Name)),".txt",sep=""))
sink(paste("results/filterCtDataB",".txt",sep=""))
qFiltB <- filterCtData( dB.norm,
remove.type = "Endogenous Control",
remove.name = c("MammU6-4395470", "RNU44-4373384", "RNU48-4373383"),
#remove.category = c("Undetermined","Unreliable"),
#remove.category = c("Unreliable"),
#remove.category = c("Undetermined"),
n.category = 0
#n.category = as.integer(0.75*length(filesB$Name))
)
sink()
## Plots densidade
png(paste("quality/groupA","filterUndetermined","norm","filterEndoControl","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(qFiltA)
dev.off()
png(paste("quality/groupB","filterUndetermined","norm","filterEndoControl","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(qFiltB)
dev.off()
## Obtem valores de expressao
eqFiltA <- exprs(qFiltA)
eqFiltB <- exprs(qFiltB)
## Designa 40 para NAs "Saturação"
#eqFiltA[eqFiltA %in% NA] <- 40
#eqFiltB[eqFiltB %in% NA] <- 40
## Plots densidade
png(paste("quality/groupA","filterUndetermined","norm","saturate","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(dA.norm)
dev.off()
png(paste("quality/groupB","filterUndetermined","norm","saturate","png", sep="."), res =300, height=2000, width=2000)
plotCtDensity(dB.norm)
dev.off()
######
rownames(eqFiltB) %in% rownames(eqFiltA)
write(rownames(eqFiltB),"/tmp/groupB")
write(rownames(eqFiltA),"/tmp/groupA")
## Avaliação do Interquartile Range
png(filename=paste("results/plots/IQRA.png",sep=""),
bg="white", res=300, width=2000, height=2000)
iqrA.values <- apply(eqFiltA, 1, IQR, na.rm=TRUE)
hist(iqrA.values, n = 20, main = "", xlab = "IQR across samples - A")
abline(v = median(iqrA.values, na.rm=TRUE), col = 2)
dev.off()
png(filename=paste("results/plots/IQRB.png",sep=""),
bg="white", res=300, width=2000, height=2000)
iqrB.values <- apply(eqFiltB, 1, IQR, na.rm=TRUE)
hist(iqrB.values, n = 20, main = "", xlab = "IQR across samples - B")
abline(v = median(iqrB.values, na.rm=TRUE), col = 2)
dev.off()
# Nenhum corte sobre o IQR
eqFiltA.2 <- eqFiltA
eqFiltB.2 <- eqFiltB
## Heatmap
library('gplots')
library('cluster')
library(marray)
rbg = maPalette(low = "green", high = "red", mid = "black", k = 5000)
hclust.ave <- function (d,...) {
return(hclust(d=d,method="average",...))
}
hclust.diana <- function (x,...) {
return(as.hclust(diana(x=x,diss=TRUE,...)))
}
#dist.euclidean <- function(x, ...){
# dist(x, method = "euclidean", diag = FALSE, upper = FALSE, p = 2)
#}
na.reduce <- function(x, max.NA){
ifelse(sum(is.na(x))>= max.NA,FALSE, TRUE)
}
genesA <- apply(eqFiltA.2, 1, na.reduce, max.NA=ncol(eqFiltA.2)/2)
length(which(genesA))
x11();
hv1<-heatmap.2(
as.matrix(eqFiltA.2[which(genesA),]),
na.rm=TRUE,
scale="none",
col=rbg,
hclustfun=hclust.ave,
key=TRUE,
symkey=FALSE,
density.info="none",
trace="none",
Rowv=T,
Colv=T,
cexRow=0.45,
cexCol=1,
keysize=1,
margins = c(10,10),
dendrogram=c("both"),
main = "Group A",
na.color="gray"
)
genesB <- apply(eqFiltB.2, 1, na.reduce, max.NA=ncol(eqFiltB.2)/2)
length(which(genesB))
hv1<-heatmap.2(
as.matrix(eqFiltB.2[which(genesB),]),
na.rm=TRUE,
scale="none",
col=greenred,
hclustfun=hclust.diana,
key=TRUE,
symkey=FALSE,
density.info="none",
trace="none",
breaks=100,
Rowv=T,
Colv=T,
cexRow=0.45,
cexCol=1,
keysize=1,
margins = c(10,10),
dendrogram=c("both"),
main = "Group B"
)
## Save
source("/work/projects/manalysis/myArray/myArrayFunctions.R")
saveArray('saves/')
write.table(eqFiltA.2, file="saves/tables/groupA.txt", col.names=T, row.names=T, sep="\t")
write.table(eqFiltB.2, file="saves/tables/groupB.txt", col.names=T, row.names=T, sep="\t")
# Junção Grupos A e B
eqFilt <- matrix(ncol=dim(eqFiltA.2)[2],nrow=dim(eqFiltA.2)[1]+dim(eqFiltB.2)[1])
colnames(eqFilt) <- gsub('A-([12])$','-\\1',colnames(eqFiltA.2))
rownames(eqFilt) <- c(rownames(eqFiltA.2),rownames(eqFiltB.2))
for (r in levels(as.factor(filesA$Rep))) {
print(r)
for (i in c(1,2)) {
eqFilt[,paste(r,i,sep="-")] <- c(as.numeric(eqFiltA.2[,paste(r,'A-',i,sep="")]),as.numeric(eqFiltB.2[,paste(r,'B-',i,sep="")]))
}
}
dim(eqFilt)
## Teste estatistico
## Teste T
#
#qFiltA.2 <- qFiltA
#exprs(qFiltA.2) <- eqFiltA.2
#qDE.ttest.A <- ttestCtData(qFiltA.2, groups = filesA$Treatment, calibrator = "Control", p.adjust="fdr", na.rm=TRUE, stringent=TRUE)
##dim(subset(qDE.ttest.A, adj.p.value <= 0.08))
##head(subset(qDE.ttest.A, adj.p.value <= 0.08))
#write.table(qDE.ttest.A, paste("results","tables","ttest.A.cal_control.padj_fdr.stringent_F.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
#
#qFiltB.2 <- qFiltB
#exprs(qFiltB.2) <- eqFiltB.2
#qDE.ttest.B <- ttestCtData(qFiltB.2, groups = filesA$Treatment, calibrator = "Control", p.adjust="fdr" , stringent=FALSE)
#write.table(qDE.ttest.B, paste("results","tables","ttest.B.cal_control.padj_fdr.stringent_F.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
#
#
### Teste Mann-Whitney
#
#qDE.mwtest.A <- mannwhitneyCtData(qFiltA.2, groups = filesA$Treatment, calibrator = "Control", p.adjust="fdr", stringent=TRUE)
#write.table(qDE.mwtest.A, paste("results","tables","mwtest.A.cal_control.padj_fdr.stringent_F.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
#head(qDE.mwtest.A)
#
#qDE.mwtest.B <- mannwhitneyCtData(qFiltB.2, groups = filesA$Treatment, calibrator = "Control", p.adjust="fdr", stringent=TRUE)
#write.table(qDE.mwtest.B, paste("results","tables","mwtest.B.cal_control.padj_fdr.stringent_F.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
#head(qDE.mwtest.A)
## Limma
designA <- model.matrix(~0 + filesA$Treatment)
colnames(designA) <- as.character(unique(filesA$Treatment))
contrastsA <- makeContrasts(Down - Control, levels = designA)
colnames(contrastsA) <- c("D-C")
qDE.limma.A <- limmaCtData(qFiltA.2, design = designA, contrasts = contrastsA, ndups = 1, spacing = NULL, stringent=FALSE)
head(qDE.limma.A[['D-C']])
write.table(qDE.limma.A[['D-C']], paste("results","tables","limma.A.cal_control.padj_fdr.stringent_F.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
designB <- model.matrix(~0 + filesB$Treatment)
colnames(designB) <- as.character(unique(filesB$Treatment))
contrastsB <- makeContrasts(Down - Control, levels = designB)
colnames(contrastsB) <- c("D-C")
qDE.limma.B <- limmaCtData(qFiltB.2, design = designB, contrasts = contrastsB, ndups = 1, spacing = NULL, stringent=FALSE)
write.table(qDE.limma.B[['D-C']], paste("results","tables","limma.B.cal_control.padj_fdr.stringent_F.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
# x11();func.list$volcano.plus( qDE.limma.B[["D-C"]], "FC", "p.value", paste("Volcano Plot (limma) \n "), 0.01, 2, -2,
# 'adj.p.value' ,ncolors=5)
## Limma modified
qDE.my.limma.A <- my.limmaCtData(qFiltA.2, design = designA, contrasts = contrastsA, ndups = 1, spacing = NULL, NA.threshold = 0.6)
write.table(qDE.my.limma.A[['D-C']], paste("results","tables","my.limma.A.cal_control.padj_fdr.stringent_F.NAThreshold_0.6.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
my.DE.A <- subset(qDE.my.limma.A[['D-C']], categoryCalibrator=="OK" & categoryTarget=="OK" )
write.table(my.DE.A, paste("results","tables","my.limma.A.cal_control.padj_fdr.stringent_F.NAThreshold_0.6.filtered.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
qDE.my.limma.B <- my.limmaCtData(qFiltB.2, design = designB, contrasts = contrastsB, ndups = 1, spacing = NULL, NA.threshold = 0.6)
write.table(qDE.my.limma.B[['D-C']], paste("results","tables","my.limma.B.cal_control.padj_fdr.stringent_F.NAThreshold_0.6.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
my.DE.B <- subset(qDE.my.limma.B[['D-C']], categoryCalibrator=="OK" & categoryTarget=="OK" )
write.table(my.DE.B, paste("results","tables","my.limma.B.cal_control.padj_fdr.stringent_F.NAThreshold_0.6.filtered.txt",sep="/"), col.names=T, row.names=F, sep="\t", quote=F)
## Avaliação
hist(my.DE.A$p.value, breaks=c(seq(0,1,0.01)))
abline(v = 0.05, col = 2)
hist(my.DE.A$adj.p.value, breaks=c(seq(0,1,0.01)))
abline(v = 0.05, col = 2)
hist(my.DE.B$p.value, breaks=c(seq(0,1,0.01)))
abline(v = 0.05, col = 2)
hist(my.DE.B$adj.p.value, breaks=c(seq(0,1,0.01)))
abline(v = 0.05, col = 2)
pv.cut <- 0.05
fc.cut <- 1.5
miR.DE.A <- subset(my.DE.A, p.value<pv.cut & ((FC >= fc.cut) | (FC <= 1/fc.cut)) )
head(miR.DE.A)
write.table(miR.DE.A$genes, file=paste("results","tables",paste("sig.miR.A.p_",pv.cut,".txt",sep=""),sep="/"), row.names=F, col.names=F, quote=F)
write.table(miR.DE.A, file=paste("results","tables",paste("sig.miR.values.A.p_",pv.cut,".txt",sep=""),sep="/"), row.names=F, col.names=T, quote=F, sep="\t")
miR.DE.B <- subset(my.DE.B, p.value<pv.cut & ((FC >= fc.cut) | (FC <= 1/fc.cut)) )
write.table(miR.DE.B$genes, file=paste("results","tables",paste("sig.miR.B.p_",pv.cut,".txt",sep=""),sep="/"), row.names=F, col.names=F, quote=F)
write.table(miR.DE.B, file=paste("results","tables",paste("sig.miR.values.B.p_",pv.cut,".txt",sep=""),sep="/"), row.names=F, col.names=T, quote=F, sep="\t")
dim(miR.DE.A)
dim(miR.DE.B)
#aux <- miR.DE.B[,c("genes","FC","p.value")]
#aux[which(aux$FC<1),'FC'] <- -1/aux[which(aux$FC<1),'FC']
#aux$FC <- round(aux$FC,2)
#aux$p.value <- round(aux$p.value,4)
#write.table(aux, "/tmp/myauxB.tmp", row.names=F, col.names=T, quote=F, sep="|",dec=",")
#
#aux <- miR.DE.A[,c("genes","FC","p.value")]
#aux[which(aux$FC<1),'FC'] <- -1/aux[which(aux$FC<1),'FC']
#aux$FC <- round(aux$FC,2)
#aux$p.value <- round(aux$p.value,4)
#write.table(aux, "/tmp/myauxA.tmp", row.names=F, col.names=T, quote=F, sep="|",dec=",")
## Heatmaps
x11()
aux.miRsA <- levels(miR.DE.A$genes)[miR.DE.A$genes]
hv1<-heatmap.2(
as.matrix(exprs(qFiltA.2)[aux.miRsA,]),
na.rm=TRUE,
scale="none",
col=rbg,
hclustfun=hclust.diana,
key=TRUE,
symkey=FALSE,
density.info="none",
trace="none",
Rowv=T,
Colv=T,
cexRow=0.45,
cexCol=1,
keysize=1,
margins = c(10,10),
dendrogram=c("both"),
main = "Group A",
na.color="gray"
)
rm(aux.miRsA)
x11()
aux.miRsB <- levels(miR.DE.B$genes)[miR.DE.B$genes]
hv1<-heatmap.2(
as.matrix(exprs(qFiltB.2)[aux.miRsB,]),
na.rm=TRUE,
scale="none",
col=greenred,
hclustfun=hclust.diana,
key=TRUE,
symkey=FALSE,
density.info="none",
trace="none",
breaks=100,
Rowv=T,
Colv=T,
cexRow=0.45,
cexCol=1,
keysize=1,
margins = c(10,10),
dendrogram=c("both"),
main = "Group B",
na.color="grey"
)
rm(aux.miRsB)
source("/work/projects/manalysis/myArray/myArrayFunctions.R")
saveArray('saves/')
#loadArray('saves/')
#########
loadArray('saves/')
## miRNApath
#dataA <- (my.DE.A)
#dataB <- (my.DE.B)
#
#head(dataA)
#
#colnames(dataA)
#
### Converter ids (coluna genes) / remover ultima coluna
#
#aux.ids <- levels(dataA$genes)[dataA$genes]
#head(aux.ids)
#aux.ids <- unlist(strsplit(aux.ids, "\\-\\d+$",perl=TRUE))
#sustenido <- grep("#$", aux.ids)
#aux.ids[sustenido] <- gsub(".$","",aux.ids[sustenido])
#dataA$Id <- aux.ids
#
#rm(aux.ids)
#
#aux.ids <- levels(dataB$genes)[dataB$genes]
#head(aux.ids)
#aux.ids <- unlist(strsplit(aux.ids, "\\-\\d+$",perl=TRUE))
#sustenido <- grep("#$", aux.ids)
#aux.ids[sustenido] <- gsub(".$","",aux.ids[sustenido])
#dataB$Id <- aux.ids
#
### Escrever arquivo
#
#dataA$Assay <- dataA$Id
#
#dataA$Group <- "DOWN.vs.CONTROL"
#
#aux.fc <- dataA$FC; aux.fc[which(aux.fc < 1)] <- -1/aux.fc[which(aux.fc < 1)]; dataA$FC <- aux.fc
#
#file.dataA <- paste("/tmp",paste("mirnaA",format(Sys.time(),"%H%M%S"),"txt",sep="."),sep="/")
#write.table(dataA,file=file.dataA, quote=FALSE, row.names=FALSE, col.names=TRUE, na="", sep="\t")
#
#dataB$Assay <- dataB$Id
#
#dataB$Group <- "DOWN.vs.CONTROL"
#
#aux.fc <- dataB$FC; aux.fc[which(aux.fc < 1)] <- -1/aux.fc[which(aux.fc < 1)]; dataB$FC <- aux.fc
#
#file.dataB <- paste("/tmp",paste("mirnaB",format(Sys.time(),"%H%M%S"),"txt",sep="."),sep="/")
#write.table(dataB,file=file.dataB, quote=FALSE, row.names=FALSE, col.names=TRUE, na="", sep="\t")
#
### Criar objeto
#
#mirnaobjA <- loadmirnapath( mirnafile=file.dataA, pvaluecol="p.value", groupcol="Group", mirnacol="Id", assayidcol= "Assay", foldchangecol = "FC")
#
#mirnaobjA
#mirnaobjA@columns["pvaluecol"] <- "p.value";
#mirnaobjA@columns["foldchangecol"] <- "FC";
#
#mirnaobjA <- filtermirnapath( mirnaobjA, pvalue = pv.cut, expression = NA, foldchange = fc.cut );
#
#mirnaobjA
#head(mirnaobjA@mirnaTable)
#tail(mirnaobjA@mirnaTable)
#
#
#mirnaobjB <- loadmirnapath( mirnafile=file.dataB, pvaluecol="p.value", groupcol="Group", mirnacol="Id", assayidcol= "Assay", foldchangecol = "FC")
#
#mirnaobjB
#mirnaobjB@columns["pvaluecol"] <- "p.value";
#mirnaobjB@columns["foldchangecol"] <- "FC";
#
#mirnaobjB <- filtermirnapath( mirnaobjB, pvalue = pv.cut, expression = NA, foldchange = fc.cut );
#
#mirnaobjB
#head(mirnaobjB@mirnaTable)
#tail(mirnaobjB@mirnaTable)
#
#
#### Obter Alvos
### Utilizando script em perl para gerar uma tabela contendo as interações miR-mR
###
#
#
#miRs <- c(dataA$Id, dataB$Id)
#length(miRs) # 374
#write.table(as.character(miRs), file="results/tables/miRs.txt", row.names=F, col.names=F, quote=F)
#
#aux.ids <-as.character(c(levels(miR.DE.A$genes)[miR.DE.A$genes],levels(miR.DE.B$genes)[miR.DE.B$genes]))
#head(aux.ids)
#aux.ids <- unlist(strsplit(aux.ids, "\\-\\d+$",perl=TRUE))
#sustenido <- grep("#$", aux.ids)
#aux.ids[sustenido] <- gsub(".$","",aux.ids[sustenido])
#write.table(as.character(aux.ids), file="results/tables/miRs.selected.txt", row.names=F, col.names=F, quote=F)
#
#
##library("RmiR.Hs.miRNA")
##library("RmiR")
##
##
##con <- RmiR.Hs.miRNA_dbconn()
##
##tables <- dbListTables(con)
##
##ls()
##
##
##
##my.micros <- unlist(strsplit(c(levels(miR.DE.A$genes),levels(miR.DE.B$genes)), "\\-\\d+$",perl=TRUE))
##my.genes <- c()
##for(t in tables){
## assign(t,unique(dbReadTable(con, t)[,c("mature_miRNA","gene_id")]))
## my.genes <- unique(c(my.genes,get(t)[which(get(t)[,'mature_miRNA'] %in% my.micros), "gene_id"]))
##
##}
##miRmR <- matrix(0, nrow=length(my.micros), ncol=length(my.genes), dimnames = list(my.micros, as.character(my.genes) ))
##for(t in tables){
## pos.in.table <- which(get(t)[,'mature_miRNA'] %in% my.micros )
## in.matrix <- get(t)[pos.in.table, c("mature_miRNA","gene_id")]
##
### miRmR[in.matrix[,1], in.matrix[,2]] <-
### miRmR[in.matrix[,1], in.matrix[,2]] + 1
##
## for(i in rownames(in.matrix) ){
## miRmR[in.matrix[i,1], in.matrix[i,2]] <- miRmR[in.matrix[i,1], in.matrix[i,2]] + 1
##
## }
##}
##miRmR[relation.mir,]
##relation.mir[1]
##get(t)[3,]
##miRmR[3, as.character(get(t)[3,'gene_id'])]
##
##
##summary(miRmR)
##head(miRmR)
##
##max(miRmR)
##
##
##which(my.micros %in% miranda$mature_miRNA)
##head(miranda)
##
##
##head(my.micros)
#
#miRmR <- read.table("/work/matheus/getmiRmR/out/miRmR.txt")
#dim(miRmR)
#
#miRmR[1:10,1:10]
#
#max(miRmR)
#min(miRmR)
#
#aux.sum <- apply(miRmR, 2, sum)
#min(aux.sum)
#
#
#entrez <- colnames(miRmR); substr(entrez, 1,1) <- ""
#class(entrez)
#
#
#
### Data structure with Symbol names for each entrez id
#x <- org.Hs.egSYMBOL
## Get the gene names that are mapped to an entrez gene identifier
#mapped_genes <- mappedkeys(x)
## Convert to a list
#SYMBOL <- as.list(x[mapped_genes])
#my.genes <- unlist(SYMBOL[as.character(entrez)])
#
#table(my.genes)
#
#### Subset miRmR
#### MicroRNAs no set A presentes no banco de dados de interação miRmR
##relations.micro <- which(rownames(miRmR) %in% dataA$Id)
##length(relations.micro)
##length(dataA$Id)
##dataA$Id[which(!dataA$Id %in% rownames(miRmR))] #"has-miR-155" => has != hsa
##
##miRmR.sub <- miRmR[relations.micro,]
##
##
##
##miRmR.sub.atl2 <- miRmR.sub;
##miRmR.sub.atl2[miRmR.sub.atl2<2] <- 0 # at least 2 databases
##
##
##write.table(as.character(dataA$Id), file="results/tables/miRs.setA.txt", row.names=F, col.names=F, quote=F)
##
#
#
### Format Table
#
#dim(miRmR)
#
### Load the miRNA to gene associations
#mirnaobj <- loadmirnatogene( mirnafile="mirnaGene.txt", mirnaobj=mirnaobj, mirnacol="miRNA Name",
# genecol="Entrez Gene ID",columns=c(assayidcol="ASSAYID") );
#mirnaobj;
#
#############
### New Analysis
## Obter microRNAs exclusivos
control <- grep("CC",colnames(featureCategory(qFiltA.2)))
down <- grep("CD",colnames(featureCategory(qFiltA.2)))
head(featureCategory(qFiltA.2))
## Verifica se todas as amostras ->> down <<- são categorizadas como Unreliable ou Undetermined
## então provavelmente esse microRNA eh exclusivo do ->> controle <<-
aux.control.exclusive.A <- names(which(apply(featureCategory(qFiltA.2), 1,function(x){
is.na(table(as.character(x[down]))["OK"])
})))
length( aux.control.exclusive.A )
aux.down.exclusive.A <- names(which(apply(featureCategory(qFiltA.2), 1,function(x){
is.na(table(as.character(x[control]))["OK"]) ## Não existe OK ??
})))
length( aux.down.exclusive.A )
control.exclusive.A <- (setdiff(aux.control.exclusive.A, aux.down.exclusive.A))
## Verificação
featureCategory(qFiltA.2)[control.exclusive.A, control]
apply(featureCategory(qFiltA.2)[control.exclusive.A,control],1,function(x){table(x)["OK"]}) ## Não deve possuir NA
apply(featureCategory(qFiltA.2)[control.exclusive.A,down],1,function(x){table(x)["OK"]}) ## NA
exprs(qFiltA.2)[control.exclusive.A,control]
exprs(qFiltA.2)[control.exclusive.A,down]
mean(exprs(rawA)[control.exclusive.A,control]) ## Media dados BRUTOS
sd(as.numeric(exprs(rawA)[control.exclusive.A,control]))
mean(exprs(rawA)[control.exclusive.A,down]) ## Media dados BRUTOS
sd(as.numeric(exprs(rawA)[control.exclusive.A,down]))
down.exclusive.A <- (setdiff(aux.down.exclusive.A, aux.control.exclusive.A))
## Verificação
featureCategory(qFiltA.2)[down.exclusive.A, down]
apply(featureCategory(qFiltA.2)[down.exclusive.A,down],1,function(x){table(x)["OK"]}) ## Não deve possuir NA
apply(featureCategory(qFiltA.2)[down.exclusive.A,control],1,function(x){table(x)["OK"]}) ## NA
exprs(qFiltA.2)[control.exclusive.A,control]
exprs(qFiltA.2)[control.exclusive.A,down]
mean(exprs(rawA)[down.exclusive.A,control]) ## Media dados BRUTOS
sd(as.numeric(exprs(rawA)[down.exclusive.A,control]))
mean(exprs(rawA)[down.exclusive.A,down]) ## Media dados BRUTOS
sd(as.numeric(exprs(rawA)[down.exclusive.A,down]))
#######
colnames(qDE.my.limma.A[["D-C"]])
control.exclusive.A <- subset(qDE.my.limma.A[["D-C"]],categoryTarget=="Undetermined" & categoryCalibrator=="OK")
control.exclusive.A$LFC <- log2(control.exclusive.A$FC)
control.exclusive.A.sig <- subset(control.exclusive.A, p.value <= 0.05 & (LFC>1 | LFC< -1))
featureCategory(qFiltA.2)[levels(control.exclusive.A.sig$genes)[control.exclusive.A.sig$genes], down]
exprs(qFiltA.2)[levels(control.exclusive.A.sig$genes)[control.exclusive.A.sig$genes], down]
exprs(qFiltA.2)[levels(control.exclusive.A.sig$genes)[control.exclusive.A.sig$genes], control]
control.exclusive.B <- subset(qDE.my.limma.B[["D-C"]],categoryTarget=="Undetermined" & categoryCalibrator=="OK")
control.exclusive.B$LFC <- log2(control.exclusive.B$FC)
control.exclusive.B.sig <- subset(control.exclusive.B, p.value <= 0.05 & (LFC>1 | LFC< -1))
down.exclusive.A <- subset(qDE.my.limma.A[["D-C"]],categoryTarget=="OK" & categoryCalibrator=="Undetermined")
down.exclusive.A$LFC <- log2(down.exclusive.A$FC)
down.exclusive.A.sig <- subset(down.exclusive.A, p.value <= 0.05 & (LFC>1 | LFC< -1))
down.exclusive.B <- subset(qDE.my.limma.B[["D-C"]],categoryTarget=="OK" & categoryCalibrator=="Undetermined")
down.exclusive.B$LFC <- log2(down.exclusive.B$FC)
down.exclusive.A.sig <- subset(down.exclusive.A, p.value <= 0.05 & (LFC>1 | LFC< -1))
####################
## pegar miRs com NA em mais do que 20 % das amostras em um grupo
## pegar a expressão no outro grupo e verificar se esta acima da mediana do array
median.A <- median(as.numeric(exprs(qFiltA.2)), na.rm=T)
median.B<- median(as.numeric(exprs(qFiltB.2)), na.rm=T)
x <- down.exclusive.A
qPCRset <- qFiltA.2
expr.cut <- median.A
group.idx=control
group.names=sampleNames(qFiltA)[control]
#'qPCRset
#'group.names = nome das amostras do grupo com exclusividade de expressão
#'down.cut = valor de 0 a 1 que indica a parcela minima com valores categorizados como OK no grupo com exclusividade
#'up.cut = valor de 0 a 1 que indica a parcela minima com valores categorizados como Undetermined no outro grupo
verify.exclusive <- function(qPCRset, group.names=sampleNames(qFiltA)[control], expr.cut=10, up.cut=0.8, down.cut=0.6){
if(as.character(class(qPCRset))!="qPCRset")
stop("Please, think a little ...")
## Verificar microRNAs com pelo menos down.cut% de expressão no grupo exclusivo
min.ok <- ceiling(0.6*length(group.names)) # quantidade minima de ok no grupo exlusivo
miR.ok <- names(which(apply(featureCategory(qPCRset)[group.names], 1, function(x){
table(x)["OK"]>= min.ok
})))
other.grp <- setdiff(sampleNames(qPCRset),group.names)
min.undet <- ceiling(0.8*length(other.grp)) ## quantidade minima de valores indeterminados para grupo ser considerado sem expressao
miR.pos <- names(which(apply(featureCategory(qPCRset)[miR.ok,other.grp],1,
function(x){
table(x)["Undetermined"]>= min.undet
}
)))
print(group.names)
print(other.grp)
print(miR.pos)
if(length(miR.pos)>0){
if(length(miR.pos)==1){
if(mean(exprs(qPCRset)[miR.pos, group.names], na.rm=TRUE) <= expr.cut )
return(miR.pos)
else
return(NA)
}
return(names(which(apply(exprs(qPCRset)[miR.pos, group.names],1,function(x){
mean(x, na.rm=T) <= expr.cut
}))))
}else{
return(NA)
}
}
median(as.numeric(exprs(qFiltA.2)), na.rm=T)
median(as.numeric(exprs(qFiltB.2)), na.rm=T)
my.par <- list( list(qPCRset=qFiltA.2, group.names=sampleNames(qFiltA.2)[control], expr.cut=median(as.numeric(exprs(qFiltA.2)), na.rm=T)),
list(qPCRset=qFiltA.2, group.names=sampleNames(qFiltA.2)[down], expr.cut=median(as.numeric(exprs(qFiltA.2)), na.rm=T)),
list(qPCRset=qFiltB.2, group.names=sampleNames(qFiltB.2)[control], expr.cut=median(as.numeric(exprs(qFiltB.2)), na.rm=T)),
list(qPCRset=qFiltB.2, group.names=sampleNames(qFiltB.2)[control], expr.cut=medialibrary('HTqPCR')
i <- my.par[[1]]
for(i in my.par){
print(i[[1]])
print(verify.exclusive(i[['qPCRset']], i[['group.names']], i[['expr.cut']], 0.5, 0.6))
}
## Obter relação miRmR
# para todos os miRNAs presentes na análise (setA e setB)
## qFiltA.2 ## dados utilizados para o teste estatistico
## qFiltB.2 ## dados utilizados para o teste estatistico
## todos os microRNAs
all.miRs <- c(featureNames(qFiltA.2), featureNames(qFiltB.2))
all.miRs <- unlist(strsplit(all.miRs, "\\-\\d+$",perl=TRUE))
sustenido <- grep("#$", all.miRs)
all.miRs[sustenido] <- gsub(".$","",all.miRs[sustenido])
length(all.miRs)
all.miRs <- unique(all.miRs)
length(all.miRs)
write.table(all.miRs,"/data/matheus/getmiRmR/in/all.miRs.txt", col.names=F, quote=F, row.names=F)
## Rodar script em perl (process.pl) para obter tabela miRmR
miRmR <- read.table("/work/matheus/getmiRmR/out/miRmR.txt")
dim(miRmR)
length(all.miRs) - nrow(miRmR) # miRs not found
miRs.not <- setdiff(all.miRs, rownames(miRmR))
miRmR[1:10,1:10]
## todos os genes
entrez <- colnames(miRmR); substr(entrez, 1,1) <- ""
class(entrez)
universe <- entrez
length(universe)
miR.DE <- c(levels(miR.DE.A$genes)[(miR.DE.A$genes)],levels(miR.DE.B$genes)[(miR.DE.B$genes)])
substr(miR.DE[grep("has", miR.DE)],1,3) <- "hsa"
length(miR.DE)
miR.DE <- unlist(strsplit(miR.DE, "\\-\\d+$",perl=TRUE))
sustenido <- grep("#$", miR.DE)
miR.DE[sustenido] <- gsub(".$","",miR.DE[sustenido])
miRmR.sel <- subset(miRmR, rownames(miRmR) %in% miR.DE)
dim(miRmR.sel)
setdiff(miR.DE, rownames(miRmR.sel)) ## microRNAs DE não presentes no banco ou com nomes incompativeis
miRmR.sel[1:10, 1:10]
length(which(apply(miRmR.sel, 2, sum) == 0))
miRmR.sel <- miRmR.sel[, -which(apply(miRmR.sel, 2, sum) == 0)]
dim(miRmR.sel)
sig.genes <- colnames(miRmR.sel); substr(sig.genes, 1,1) <- ""
length(sig.genes)
miRmR.sel.bin <- miRmR.sel
position <- which(miRmR.sel.bin>0, arr.ind = TRUE)
for(i in 1:nrow(position)){
miRmR.sel.bin[position[i,1], position[i,2]] <- 1
}
# Obter miRs ups e downs
dim(miR.DE.A)
miR.up <- c(levels(miR.DE.A[which(miR.DE.A$FC>1), "genes"])[miR.DE.A[which(miR.DE.A$FC>1), "genes"]],
levels(miR.DE.B[which(miR.DE.B$FC>1), "genes"])[miR.DE.B[which(miR.DE.B$FC>1), "genes"]])
substr(miR.up[grep("has", miR.up)],1,3) <- "hsa"
miR.down <- c(levels(miR.DE.A[which(miR.DE.A$FC<1), "genes"])[miR.DE.A[which(miR.DE.A$FC<1), "genes"]],
levels(miR.DE.B[which(miR.DE.B$FC<1), "genes"])[miR.DE.B[which(miR.DE.B$FC<1), "genes"]])
substr(miR.down[grep("has", miR.down)],1,3) <- "hsa"
correctNames <- function(x){
x <- unlist(strsplit(x, "\\-\\d+$",perl=TRUE))
sustenido <- grep("#$", x)
x[sustenido] <- gsub(".$","",x[sustenido])
x
}
miR.up <- correctNames(miR.up)
miR.down <- correctNames(miR.down)
length(miR.down)
length(which((row.names(miRmR.sel.bin) %in% miR.down)))
miR.down[which((miR.down %in% row.names(miRmR.sel.bin)) == FALSE)] ## MicroRNAs não encontrados
length(miR.up)
length(which((row.names(miRmR.sel.bin) %in% miR.up)))
miR.up[which((miR.up %in% row.names(miRmR.sel.bin)) == FALSE)] ## MicroRNAs não encontrados
miRmR.sel.bin.up <- miRmR.sel.bin[which(row.names(miRmR.sel.bin) %in% miR.up),]
miRmR.sel.bin.up <- miRmR.sel.bin.up[, -which(apply(miRmR.sel.bin.up, 2, sum) == 0)]
miRmR.sel.bin.down <- miRmR.sel.bin[which(row.names(miRmR.sel.bin) %in% miR.down),]
miRmR.sel.bin.down <- miRmR.sel.bin.down[, -which(apply(miRmR.sel.bin.down, 2, sum) == 0)]
dim(miRmR.sel.bin.up)
dim(miRmR.sel.bin.down)
miRmR.sel.bin.neg <- miRmR.sel.bin
miRmR.sel.bin.neg[which((row.names(miRmR.sel.bin) %in% miR.down)),] <- -1*miRmR.sel.bin.neg[which((row.names(miRmR.sel.bin) %in% miR.down)),]
genes.sum <- apply(miRmR.sel.bin, 2, sum)
genes.sum <- genes.sum[order(genes.sum, decreasing=T)]
genes.sum.neg <- apply(miRmR.sel.bin.neg, 2, sum)