library(gplots)
flyWorm <- read.table("../data/flyWorm.txt", header = TRUE)
flyWorm <- as.matrix(flyWorm)
fwBC <- bcSubSamplePar(flyWorm, 100, 27, 0.6)
save(fwBC, file = "fwBC.RData")
fwBC30 <- bcSubSamplePar(flyWorm, 100, 30, 0.6)
fwBC33 <- bcSubSamplePar(flyWorm, 100, 33, 0.6)
fwBC35 <- bcSubSamplePar(flyWorm, 100, 35, 0.6)
fwBC27.all <- biclusteringPar(flyWorm, 100, 27)
fwBC30.all <- biclusteringPar(flyWorm, 100, 30)
fwBC33.all <- biclusteringPar(flyWorm, 100, 33)
fwBC35.all <- biclusteringPar(flyWorm, 100, 35)
save(fwBC30, fwBC33, fwBC35, fwBC27.all, fwBC30.all, fwBC33.all,
fwBC35.all, file = "fwBCExtra.RData")
load("fwBCExtra.RData", verbose = T)
tmpAllNames <- rownames(flyWorm)
rownames(flyWorm) <- flybaseNames
pss.fwBC30 <- postSubSample.percent(fwBC30, 0.95, 0.5)
pss.fwBC30 <- postSubSample.percent(fwBC30, 0.9, 0.9)
write((flybaseNames[pss.fwBC30$rowIdx]), file = "../results/flybaseNames_30_90_90.FG.txt") # this is most interesting
write((wormNames[pss.fwBC30$rowIdx]), file = "../results/wormNames_30_90_90.FG.txt") # this is most interesting
write.table(flyWorm[pss.fwBC30$rowIdx, pss.fwBC30$colIdx],
file = "../results/flybaseNames_30_90_90_reactome.txt",
sep = "\t", col.names = F, quote = F)
rownames(flyWorm) <- wormNames
write.table(flyWorm[pss.fwBC30$rowIdx, pss.fwBC30$colIdx],
file = "../results/wormNames_30_90_90_reactome.txt",
sep = "\t", col.names = F, quote = F)
nrow(flyWorm[pss.fwBC30$rowIdx, pss.fwBC30$colIdx])
pss.fwBC30 <- postSubSample.percent(fwBC30, 0.95, 0.95)
write((flybaseNames[pss.fwBC30$rowIdx]), file = "../results/flybaseNames_30_95_95.FG.txt") # this is most interesting
write((flybaseNames[pss.fwBC30$rowIdx]), file = "../results/flybaseNames_30_95_95.FG.txt") # this is most interesting
colnames(flyWorm)[pss.fwBC30$colIdx]
pss.fwBC30$colIdx
maskData <- function(geneDf, clusterIdx)
{
# getting next BC
get1DIdx.ext <- function(geneDf, rows, cols)
{
allPairs <- expand.grid(rows, cols)
nrow(geneDf) * (allPairs[, 2] - 1) + allPairs[, 1]
}
idxToMask <- get1DIdx.ext(geneDf, clusterIdx$rowIdx, clusterIdx$colIdx)
geneDf[idxToMask] <- sample(geneDf[-idxToMask], length(idxToMask))
return(geneDf)
}
# 0.9, 0.9 looks good... lets get next bicluster
pss.fwBC30 <- postSubSample.percent(fwBC30, 0.9, 0.9)
flyWorm.minus1 <- maskData(flyWorm, pss.fwBC30)
fw30.2 <- bcSubSamplePar(flyWorm.minus1, 100, 30, 0.6)
save(fw30.2, file = "fw30.2.RData")
fw25.2 <- bcSubSamplePar(flyWorm.minus1, 100, 25, 0.6)
save(fw25.2, file = "fw25.2.RData")
load("../bcSol/fw30.2.RData")
pssTmp <- postSubSample.percent(fw30.2, 0.75, 0.9)
write((flybaseNames[pssTmp$rowIdx]), file = "../results/flybaseNames_30_75_90_2.FG.txt") # this is most interesting
write((wormNames[pssTmp$rowIdx]), file = "../results/wormNames_30_75_90_2.FG.txt") # this is most interesting
pssTmp <- postSubSample.percent(fw30.2, 0.90, 0.9)
write((flybaseNames[pssTmp$rowIdx]), file = "../results/flybaseNames_30_90_90_2.FG.txt") # this is most interesting
write((wormNames[pssTmp$rowIdx]), file = "../results/wormNames_30_90_90_2.FG.txt") # this is most interesting
ggPlotExpression(flyWorm[pssTmp$rowIdx, pssTmp$colIdx])
ggPlotParSolution(fw30.2)
set.seed(42)
ranRows <- sample(setdiff(1:nrow(flyWorm), pss.fwBC30$rowIdx), 200)
fwBC30.trim <- lapply(fwBC30, function(x) {
x$ab <- x$ab[c(pss.fwBC30$rowIdx, ranRows)]
x
})
plotParSolution(fwBC30.trim, "../img/flyWorm")
A <- abs(sapply(fwBC30.trim, function(x) x$ab))
ggPlotParSolution(fwBC30.trim, "flyWorm", colNames = colnames(flyWorm))
plotParSolution(fwBC30)
levelplot(flyWorm[pss.fwBC30$rowIdx, sort(c(pss.fwBC30$colIdx))],
scales=list(x=list(rot=90)))
dev.print(pdf, file = "flyWorm30.pdf")
colnames(flyWorm[pss.fwBC30$rowIdx, sort(c(pss.fwBC30$colIdx))])
ggPlotExpression(flyWorm[pss.fwBC30$rowIdx, sort(c(pss.fwBC30$colIdx))])
ggPlotExpression(flyWorm[pss.fwBC30$rowIdx, sort(c(pss.fwBC30$colIdx))])
# TODO: fix this figure
# Fly/worm Fig. to show Prof. Huang
ggPlotExpression(flyWorm[pss.fwBC30$rowIdx, sort(c(pss.fwBC30$colIdx))], clustCols = F)
ggsave("../img/flyWorm_90_90.pdf", width = 21.6, height = 9.91 )
# Saving 21.6 x 9.91 in image
levelplot(flyWorm[pss.fwBC30$rowIdx, sort(c(pss.fwBC30$colIdx, 32))],
scales=list(x=list(rot=90)))
dev.print(pdf, file = "flyWorm30PlusLate.pdf")
pss.fwBC33 <- postSubSample.percent(fwBC33, 0.95, 0.5)
colnames(flyWorm)[pss.fwBC33$colIdx]
pss.fwBC33$colIdx
levelplot(flyWorm[pss.fwBC33$rowIdx, sort(c(pss.fwBC33$colIdx))],
scales=list(x=list(rot=90)))
pss.fwBC27.all <- postSubSample.percent(fwBC27.all, 0.95, 0.5)
colnames(flyWorm)[pss.fwBC27.all$colIdx]
pss.fwBC27.all$colIdx
levelplot(flyWorm[pss.fwBC27.all$rowIdx, sort(c(pss.fwBC27.all$colIdx))],
scales=list(x=list(rot=90)))
pss.fwBC30.all <- postSubSample.percent(fwBC30.all, 0.85, 0.5)
colnames(flyWorm)[pss.fwBC30.all$colIdx]
pss.fwBC30.all$colIdx
levelplot(flyWorm[pss.fwBC30.all$rowIdx, sort(c(pss.fwBC27.all$colIdx))],
scales=list(x=list(rot=90)))
post.fwBC <- postSubSample(fwBC)
post.fwBC.hclust <- post.hclust(post.fwBC, 3, 2)
post.fwBC.hclust
post.iid.subSample.per <- postSubSample.percent(fwBC, 0.95, .5)
post.iid.subSample.per
hi <- sapply(fwBC, function(x) x$d)
test2 <- postSubSample.percent2(fwBC, 0.75, .3)
order.fw <- flyWorm[post.iid.subSample.per$rowIdx, post.fwBC.hclust[[1]]$colIdx]
levelplot(order.fw, scales=list(x=list(rot=90)))
flybaseNames <- sapply(strsplit(rownames(flyWorm), "/"), function(x) x[1])
wormNames <- sapply(strsplit(rownames(flyWorm), "/"), function(x) x[2])
write(wormNames, "../results/wormNames_bg.txt")
write(flybaseNames, "../results/flybaseNames_BG.txt")
write(flybaseNames[post.iid.subSample.per$rowIdx], "../results/flybaseNames_FG.txt")
write(flybaseNames[pss.fwBC30$rowIdx], "../results/flybaseNames_30_FG.txt")
write(flybaseNames[pss.fwBC30$rowIdx], "../results/flybaseNames_30_85p_FG.txt")
dev.print(pdf, "../img/flyWorm.pdf")
levelplot(order.fw, col.regions = redgreen(75),
scales=list(x=list(rot=90)))
geneNames <- simplify2array(strsplit(rownames(flyWorm)[post.iid.subSample.per$rowIdx], "/"))[2,]
levelplot(flyWorm[post.fwBC.hclust[[1]]$rowIdx, post.fwBC.hclust[[1]]$colIdx],
scales=list(x=list(rot=90)), )
levelplot(flyWorm[sample(nrow(flyWorm), 148), (sample(ncol(flyWorm), 27))],
scales=list(x=list(rot=90)))
dev.print(pdf, "../img/flyWormRandom.pdf")
levelplot(flyWorm[sample(nrow(flyWorm), 148), post.fwBC.hclust[[1]]$colIdx],
scales=list(x=list(rot=90)))
load("fwBC.RData")
plotParSolution(fwBC)
post.fwBC.kmeans <- post.hclust(post.fwBC, 3, 2)
post.fwBC.kmeans
# go terms for worm
sexDiff<-c("C04H5.6","F56G4.4","W03F9.10","B0286.4","W09C5.2","R07E5.3","K07A1.12","C14B9.4","F41E6.4","C01G8.9","T10F2.4","Y116A8C.32","Y41D4B.19","R05D3.4","F56D2.6","W07E6.4","W04A8.7","ZK507.6","R53.6","Y110A7A.8","F26E4.10","C43E11.10","T13H5.4","T05G5.3","C06A8.2","C08B11.3","JC8.6","Y92H12A.1","Y57A10A.19","C36B1.5","F55F8.4","T23G7.1","W02D3.9","F59E10.2","F10B5.6","F12F6.3","Y111B2A.22","C55A6.9","T12A2.7","W02A11.4","R08D7.1","B0035.11","Y54E5B.3","T11G6.8","ZK616.4","F58A4.4","C15C6.4","F25B3.6","Y113G7B.23","F19F10.9","K08E4.1","F09G2.4","T08A11.2","Y106G6E.5","M04B2.1","C50C3.6")
herm<-c("C04H5.6","F56G4.4","W03F9.10","B0286.4","W09C5.2","R07E5.3","K07A1.12","C14B9.4","F41E6.4","C01G8.9","T10F2.4","Y116A8C.32","Y41D4B.19","R05D3.4","F56D2.6","W07E6.4","W04A8.7","ZK507.6","R53.6","Y110A7A.8","C43E11.10","T13H5.4","T05G5.3","C06A8.2","JC8.6","C08B11.3","Y57A10A.19","C36B1.5","F55F8.4","T23G7.1","W02D3.9","F10B5.6","F12F6.3","Y111B2A.22","C55A6.9","W02A11.4","T12A2.7","R08D7.1","B0035.11","Y54E5B.3","T11G6.8","ZK616.4","F58A4.4","C15C6.4","F25B3.6","Y113G7B.23","F19F10.9","K08E4.1","F09G2.4","T08A11.2","M04B2.1","C50C3.6")
gen<-c("C04H5.6","F56G4.4","W03F9.10","B0286.4","W09C5.2","R07E5.3","K07A1.12","C14B9.4","F41E6.4","C01G8.9","T10F2.4","Y116A8C.32","Y41D4B.19","R05D3.4","F56D2.6","W07E6.4","W04A8.7","ZK507.6","R53.6","Y110A7A.8","C43E11.10","T13H5.4","T05G5.3","C06A8.2","JC8.6","C08B11.3","Y57A10A.19","C36B1.5","F55F8.4","T23G7.1","W02D3.9","F10B5.6","F12F6.3","Y111B2A.22","C55A6.9","W02A11.4","T12A2.7","R08D7.1","B0035.11","Y54E5B.3","T11G6.8","ZK616.4","F58A4.4","C15C6.4","F25B3.6","Y113G7B.23","F19F10.9","K08E4.1","F09G2.4","T08A11.2","M04B2.1","C50C3.6")
clust2<-c("F28C6.3","C04H5.6","C50F2.3","E01A2.4","F49D11.1","D1081.8","Y54E5B.3","C36B1.3","Y102A5C.18","C36B1.5","F55F8.4","T05H4.14","F33A8.1","K01G5.6","R10E4.4","W04A8.7","Y54E10BR.6","K07A1.12","F12F6.3","C01G8.9","Y110A7A.8")
clust2.2 <- c("F28C6.3","C50F2.3","E01A2.4","F49D11.1","D1081.8","Y54E5B.3","C36B1.3","C36B1.5","F55F8.4","T05H4.14","F33A8.1","R10E4.4","W04A8.7","Y54E10BR.6","C01G8.9","Y110A7A.8")
# out of curiosity, look at smaller BC with 13 conditions
fwBC13 <- bcSubSamplePar(flyWorm, 100, 13, 0.6)
save(fwBC13, file = "fwBC13.RData")
fwBC10 <- bcSubSamplePar(flyWorm, 100, 10, 0.6)
pssTmp <- postSubSample.percent(fwBC10, 0.9, 0.9)
load("../bcSol/fwBC13.RData")
pssTmp <- postSubSample.percent(fwBC13, 0.9, 0.9)
ggPlotExpression(flyWorm[pssTmp$rowIdx, pssTmp$colIdx])
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