doProbeTTests: T-test for probe level data.

In blima: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level

Description

This function does aggregated probe level t-tests on the data provided by the object beadLevelData from package beadarray.

Usage

doProbeTTests(b, c1, c2, quality = "qua", channelInclude = "bgf", 
    correction = "BY", transformation = NULL)

Arguments

b	List of beadLevelData objects (or single object).
c1	List of logical vectors of data to assign to the first group (or single vector).
c2	List of logical vectors of data to assign to the second group (or single vector).
quality	Quality to analyze, default is "qua".
channelInclude	This field allows user to set channel with weights which have to be 0,1. All zero weighted items are excluded from t-test. You can turn this off by setting this NULL. This option may be used together with bacgroundCorrect method or/and with beadarray QC (defaults to "bgf").
correction	Multiple testing adjustment method as defined by p.adjust function, default is "BY".
transformation	Function of input data trasformation, default is NULL. Any function which for input value returns transformed value may be supplied. T-test then will be evaluated on transformed data, consider use log2TranformPositive.

Author(s)

Vojt<c4><9b>ch Kulvait

Examples

if(require("blimaTestingData") && require("illuminaHumanv4.db") && interactive())
{
    #To perform background correction, variance stabilization and  quantile normalization then test on probe level, bead level and print top 10 results.
    data(blimatesting)
    #Prepare logical vectors corresponding to conditions A(groups1Mod), E(groups2Mod) and both(processingMod).
    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$Name
    }
    #Background correction and quantile normalization followed by testing including log2TransformPositive transformation.
    blimatesting = bacgroundCorrect(blimatesting, normalizationMod =processingMod, channelBackgroundFilter="bgf")
    blimatesting = nonPositiveCorrect(blimatesting, normalizationMod=processingMod, channelCorrect="GrnF",  channelBackgroundFilter="bgf", channelAndVector="bgf")
    blimatesting = varianceBeadStabilise(blimatesting, normalizationMod = processingMod, 
            quality="GrnF", channelInclude="bgf", channelOutput="vst")
    blimatesting = quantileNormalize(blimatesting, normalizationMod = processingMod, 
            channelNormalize="vst", channelOutput="qua", channelInclude="bgf")
    beadTest = doTTests(blimatesting, groups1Mod, groups2Mod, "qua", "bgf")
    probeTest = doProbeTTests(blimatesting, groups1Mod, groups2Mod, "qua", "bgf")
    adrToSymbol <- merge(toTable(illuminaHumanv4ARRAYADDRESS), toTable(illuminaHumanv4SYMBOLREANNOTATED))
    adrToSymbol <- adrToSymbol[,c("ArrayAddress", "SymbolReannotated") ]
    colnames(adrToSymbol) <- c("Array_Address_Id", "Symbol")
    probeTestID = probeTest[,"ProbeID"]
    beadTestID = beadTest[,"ProbeID"]
    probeTestFC = abs(probeTest[,"mean1"]-probeTest[,"mean2"])
    beadTestFC = abs(beadTest[,"mean1"]-beadTest[,"mean2"])
    probeTestP = probeTest[,"adjustedp"]
    beadTestP = beadTest[,"adjustedp"]
    probeTestMeasure = (1-probeTestP)*probeTestFC
    beadTestMeasure = (1-beadTestP)*beadTestFC
    probeTest = cbind(probeTestID, probeTestMeasure)
    beadTest = cbind(beadTestID, beadTestMeasure)
    colnames(probeTest) <- c("ArrayAddressID", "difexPL")
    colnames(beadTest) <- c("ArrayAddressID", "difexBL")
    tocmp <- merge(probeTest, beadTest)
    tocmp = merge(tocmp, adrToSymbol, by.x="ArrayAddressID", by.y="Array_Address_Id")
    tocmp = tocmp[, c("ArrayAddressID", "Symbol", "difexPL", "difexBL")]
    sortPL = sort(-tocmp[,"difexPL"], index.return=TRUE)$ix
    sortBL = sort(-tocmp[,"difexBL"], index.return=TRUE)$ix
    beadTop10 = tocmp[sortBL[1:10],]
    probeTop10 = tocmp[sortPL[1:10],]
    print(beadTop10)
    print(probeTop10)
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData') and illuminaHumanv4.db by running BiocManager::install('illuminaHumanv4.db').");
}

blima documentation built on Nov. 8, 2020, 8:15 p.m.