## ----style-knitr, eval=TRUE, echo=FALSE, results="asis"-------------------- BiocStyle::latex() ## -------------------------------------------------------------------------- library(blima) library(blimaTestingData) data(blimatesting) library(Biobase) library(xtable) ## ----results='asis'-------------------------------------------------------- array1stats = chipArrayStatistics(blimatesting[[1]], includeBeadStatistic=TRUE, excludedOnSDMultiple=3) array1pheno = pData(blimatesting[[1]]@experimentData$phenoData) array1stats = data.frame(array1pheno$Name, array1stats) colnames(array1stats)[1] <- "Array"; table = xtable(array1stats, align="|c|c|c|c|c|c|c|c|c|c|", caption="Array 1 statistic.") digits(table)[c(2,3)]<-0 digits(table)[c(4:9)]<-1 print(table, include.rownames=FALSE) array2stats = chipArrayStatistics(blimatesting[[2]], includeBeadStatistic=TRUE, excludedOnSDMultiple=3) array2pheno = pData(blimatesting[[2]]@experimentData$phenoData) array2stats = data.frame(array2pheno$Name, array2stats) colnames(array2stats)[1] <- "Array"; table = xtable(array2stats, align="|c|c|c|c|c|c|c|c|c|c|", caption="Array 2 statistic.") digits(table)[c(2,3)]<-0 digits(table)[c(4:9)]<-1 print(table, include.rownames=FALSE) ## -------------------------------------------------------------------------- library(illuminaHumanv4.db) adrToIllumina = toTable(illuminaHumanv4ARRAYADDRESS) adrToIllumina = adrToIllumina[, c("ArrayAddress", "IlluminaID")] colnames(adrToIllumina) = c("Array_Address_Id", "Probe_Id") illuminaToSymbol = toTable(illuminaHumanv4SYMBOLREANNOTATED) adrToSymbol = merge(adrToIllumina, illuminaToSymbol, by.x="Probe_Id", by.y="IlluminaID") adrToSymbol = adrToSymbol[,c("Array_Address_Id", "SymbolReannotated")] colnames(adrToSymbol) = c("Array_Address_Id", "Symbol") negIl = mappedLkeys(revmap(illuminaHumanv4REPORTERGROUPNAME)["negative"]) negAdr = mappedRkeys(illuminaHumanv4ARRAYADDRESS[negIl]) ## -------------------------------------------------------------------------- if(exists("annotationHumanHT12V4")) { adrToIllumina = annotationHumanHT12V4$Probes[, c("Array_Address_Id", "Probe_Id")] adrToSymbol = annotationHumanHT12V4$Probes[, c("Array_Address_Id", "Symbol")] negAdr = unique(annotationHumanHT12V4$Controls[ annotationHumanHT12V4$Controls$Reporter_Group_Name=="negative", "Array_Address_Id"]) } ## ----eval=F---------------------------------------------------------------- # blimatestingall = bacgroundCorrect(blimatesting, channelBackground = "GrnB", # channelBackgroundFilter="bgf") # blimatestingall = nonPositiveCorrect(blimatestingall, normalizationMod=NULL, # channelCorrect="GrnF", channelBackgroundFilter="bgf", channelAndVector="bgf") ## ----eval=F---------------------------------------------------------------- # blimatestingall = backgroundChannelSubtract(blimatestingall, normalizationMod = NULL, # channelSubtractFrom = "GrnF", channelSubtractWhat = "GrnB", channelResult = "BGS") ## ----eval=F---------------------------------------------------------------- # blimatestingall = xieBacgroundCorrect(blimatestingall, normalizationMod = NULL, # negativeArrayAddresses=negAdr, channelCorrect="GrnF", channelResult="XIE", # channelInclude="bgf") ## ----eval=F---------------------------------------------------------------- # blimatestingall = varianceBeadStabilise(blimatestingall, quality="GrnF", # channelInclude="bgf", channelOutput="vst") ## ----eval=F---------------------------------------------------------------- # blimatestingall = selectedChannelTransform(blimatestingall, normalizationMod=NULL, # channelTransformFrom="GrnF", channelResult="LOG", # transformation=log2TransformPositive) ## ----eval=F---------------------------------------------------------------- # blimatestingall = quantileNormalize(blimatestingall, normalizationMod=NULL, # channelNormalize="vst", channelOutput="qua", channelInclude="bgf") ## -------------------------------------------------------------------------- data("blimatesting") groups1 = "A"; groups2 = "E"; sampleNames = list() groups1Mod = list() groups2Mod = list() processingMod = list() for(i in 1:length(blimatesting)) { p = pData(blimatesting[[i]]@experimentData$phenoData) groups1Mod[[i]] = p$Group %in% groups1; groups2Mod[[i]] = p$Group %in% groups2; processingMod[[i]] = p$Group %in% c(groups1, groups2); sampleNames[[i]] = p$Name } ## -------------------------------------------------------------------------- blimatesting = bacgroundCorrect(blimatesting, normalizationMod = processingMod, channelBackgroundFilter="bgf") blimatesting = nonPositiveCorrect(blimatesting, normalizationMod = processingMod, channelCorrect="GrnF", channelBackgroundFilter="bgf", channelAndVector="bgf") blimatesting = varianceBeadStabilise(blimatesting, normalizationMod = processingMod, quality="GrnF", channelInclude="bgf", channelOutput="vst") blimatesting = quantileNormalize(blimatesting, normalizationMod = processingMod, channelNormalize="vst", channelOutput="qua", channelInclude="bgf") ## ----results='asis'-------------------------------------------------------- probeTest <- doProbeTTests(blimatesting, groups1Mod, groups2Mod, transformation=NULL, quality="qua", channelInclude="bgf") beadTest <- doTTests(blimatesting, groups1Mod, groups2Mod, transformation=NULL, quality="qua", channelInclude="bgf") probeTestID = probeTest[,"ProbeID"] beadTestID = beadTest[,"ProbeID"] probeTestFC = abs(probeTest[,"mean1"]-probeTest[,"mean2"]) beadTestFC = abs(beadTest[,"mean1"]-beadTest[,"mean2"]) probeTestP = probeTest[,"adjustedp"] beadTestP = beadTest[,"adjustedp"] probeTestMeasure = (1-probeTestP)*probeTestFC beadTestMeasure = (1-beadTestP)*beadTestFC probeTest = cbind(probeTestID, probeTestMeasure) beadTest = cbind(beadTestID, beadTestMeasure) colnames(probeTest) <- c("ArrayAddressID", "difexPL") colnames(beadTest) <- c("ArrayAddressID", "difexBL") tocmp <- merge(probeTest, beadTest) tocmp = merge(tocmp, adrToSymbol, by.x="ArrayAddressID", by.y="Array_Address_Id") tocmp = tocmp[, c("ArrayAddressID", "Symbol", "difexPL", "difexBL")] sortPL = sort(-tocmp[,"difexPL"], index.return=TRUE)$ix sortBL = sort(-tocmp[,"difexBL"], index.return=TRUE)$ix beadTop10 = tocmp[sortBL[1:10],] probeTop10 = tocmp[sortPL[1:10],] beadTop10 = xtable(beadTop10, align="|c|c|c|c|c|", caption="Top 10 probes on bead level.") probeTop10 = xtable(probeTop10, align="|c|c|c|c|c|", caption="Top 10 probes on probe level.") digits(beadTop10)[2] = 0 digits(probeTop10)[2] = 0 print(beadTop10, include.rownames=FALSE) print(probeTop10, include.rownames=FALSE) ## ----results='asis'-------------------------------------------------------- nonnormalized = createSummarizedMatrix(blimatesting, spotsToProcess=processingMod, quality="GrnF", channelInclude="bgf", annotationTag="Name") nonnormalized = merge(nonnormalized, adrToIllumina, by.x="ProbeID", by.y="Array_Address_Id") nonnormalized = nonnormalized[, c(10, 2:9)] colnames(nonnormalized)[1] = "ID_REF" for(i in 2:9) { colnames(nonnormalized)[i] = sprintf("%s", colnames(nonnormalized)[i]) } table = head(nonnormalized) table = xtable(table, align="|c|c|c|c|c|c|c|c|c|c|", caption="Head of nonnormalized data.") digits(table)[c(2:9)]<-1 print(table, include.rownames=FALSE) normalized = createSummarizedMatrix(blimatesting, spotsToProcess=processingMod, quality="qua", channelInclude="bgf", annotationTag="Name") normalized = merge(normalized, adrToIllumina, by.x="ProbeID", by.y="Array_Address_Id") normalized = normalized[, c(10, 2:9)] colnames(normalized)[1] = "ID_REF" for(i in 2:9) { colnames(normalized)[i] = sprintf("%s", colnames(normalized)[i]) } table = head(normalized) table = xtable(table, align="|c|c|c|c|c|c|c|c|c|c|", caption="Head of normalized data.") digits(table)[c(2:10)]<-3 print(table, include.rownames=FALSE)