Package 'blima'

Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level
Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing.
Authors: Vojtěch Kulvait
Maintainer: Vojtěch Kulvait <[email protected]>
License: GPL-3
Version: 1.41.0
Built: 2024-11-18 03:16:06 UTC
Source: https://github.com/bioc/blima

Help Index

Package for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level.

Description

Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing.

Details

The DESCRIPTION file:

Encoding: UTF-8
Package: blima
Type: Package
Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level
Version: 1.41.0
Date: 2017-09-23
Author: Vojtěch Kulvait
Maintainer: Vojtěch Kulvait <[email protected]>
Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing.
License: GPL-3
LazyLoad: yes
Depends: R(>= 3.3)
Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8), BiocGenerics, grDevices, stats, graphics
LinkingTo: Rcpp
Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi, knitr
URL: https://bitbucket.org/kulvait/blima
biocViews: Microarray, Preprocessing, Normalization, DifferentialExpression, GeneRegulation, GeneExpression
VignetteBuilder: knitr
Config/pak/sysreqs: libicu-dev libpng-dev libssl-dev
Repository: https://bioc.r-universe.dev
RemoteUrl: https://github.com/bioc/blima
RemoteRef: HEAD
RemoteSha: c59eaff0b1196f71ec0ab38c08153ae372d450cd

Index of help topics: 

aggregateAndPreprocess
                        Aggregate data
bacgroundCorrect        Data background correction.
bacgroundCorrectSingleArray
                        Data background correction.
backgroundChannelSubtract
                        Background channel subtraction
backgroundChannelSubtractSingleArray
                        Background channel subtraction
blima-package           Package for the preprocessing and analysis of
                        the Illumina microarrays on the detector (bead)
                        level.
channelExistsIntegrityWithLogicalVectorList
                        Internal function
checkIntegrity          Internal function
checkIntegrityLogical   Internal function
checkIntegrityOfListOfBeadLevelDataObjects
                        Internal function
checkIntegrityOfSingleBeadLevelDataObject
                        Internal function
chipArrayStatistics     Statistics of beadLevelData
createSummarizedMatrix
                        Summarized value matrix.
doAction                Internal function
doProbeTTests           T-test for probe level data.
doTTests                T-test for bead (detector) level data.
filterBg                Bg correct vector
getNextVector           Support probe and beadl level testing.
initMeanDistribution    initMeanDistribution
insertColumn            Internal function to support
                        chipArrayStatistics
interpolateSortedVector
                        Interpolate sorted vector
interpolateSortedVectorRcpp_
                        interpolateSortedVectorRcpp
log2TransformPositive   Log2 transform of numbers >1.
meanDistribution        Produce sorted double vector with mean
                        distribution.
nonParametricEstimator
                        INTERNAL FUNCTION Xie background correct.
nonPositiveCorrect      Correct non positive
nonPositiveCorrectSingleArray
                        Correct non positive
numberOfDistributionElements
                        Internal
performXieCorrection    INTERNAL FUNCTION Xie background correct.
plotBackgroundImageAfterCorrection
                        Plot background image after correction
plotBackgroundImageBeforeCorrection
                        Plot background image before correction
quantileNormalize       Bead level quantile normalization.
readToVector            Support doTTests function.
selectedChannelTransform
                        Channel transformation
selectedChannelTransformSingleArray
                        Channel transformation
singleArrayNormalize    Bead level quantile normalization.
singleChannelExistsIntegrityWithLogicalVector
                        Internal function
singleCheckIntegrityLogicalVector
                        Internal function
singleNumberOfDistributionElements
                        Internal
updateMeanDistribution
                        updateMeanDistribution
varianceBeadStabilise   Bead level VST.
varianceBeadStabiliseSingleArray
                        Bead level VST.
vstFromLumi             Function from LGPL lumi package 2.16.0
writeBackgroundImages   Write Background Images
xieBacgroundCorrect     Xie background correct.
xieBacgroundCorrectSingleArray
                        INTERNAL FUNCTION Xie background correct.

Author(s)

Vojtěch Kulvait Maintainer: Vojtěch Kulvait <[email protected]>

Aggregate data

Description

This function is not intended to direct use. It helps perform work of doProbeTTests function. For each probe it prints mean and sd of an quality.

Usage

aggregateAndPreprocess(x, quality = "qua", transformation = NULL)

Arguments

x

Two column matrix to agregate with columns "ProbeID" and quality.
quality

Quality to analyze, default is "qua".
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.

Value

Some return value

Author(s)

Vojtěch Kulvait

Data background correction.

Description

Background correction procedure selecting beads with background Intensity I_b |mean - I_b | > k*SD(I_bs) for exclusion.

Usage

bacgroundCorrect(b, normalizationMod = NULL, channelBackground = "GrnB", 
    k = 3, channelBackgroundFilter = "bgf", channelAndVector = NULL)

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelBackground

Name of channel to normalize.
k

Parameter of method stringency (default is 3).
channelBackgroundFilter

Filtered beads will have weight 0 and non filtered weight 1.
channelAndVector

Represents vector to bitvise multiple to the channelBackgroundFilter vector.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #To perform background correction on blimatesting object for two groups. Background correction is followed by correction for non positive data. Array spots out of selected groups will not be processed.
    data(blimatesting)
    #Prepare logical vectors corresponding to conditions A and E.
    groups1 = "A";
    groups2 = "E";
    sampleNames = list()
    c = list()
    for(i in 1:length(blimatesting))
    {
        p = pData(blimatesting[[i]]@experimentData$phenoData)
        c[[i]] = p$Group %in% c(groups1, groups2);
        sampleNames[[i]] = p$Name
    }
    #Background correction and quantile normalization followed by testing including log2TransformPositive transformation.
    blimatesting = bacgroundCorrect(blimatesting, normalizationMod=c, channelBackgroundFilter="bgf")
    blimatesting = nonPositiveCorrect(blimatesting, normalizationMod=c, channelCorrect="GrnF",  channelBackgroundFilter="bgf", channelAndVector="bgf")
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Data background correction.

Description

Background correction procedure selecting beads with background Intensity I_b |mean - I_b | > k*SD(I_bs) for exclusion, internal.

Usage

bacgroundCorrectSingleArray(b, normalizationMod = NULL, channelBackground = "GrnB", 
    k = 3, channelBackgroundFilter = "bgf", channelAndVector = NULL)

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelBackground

Name of channel to normalize.
k

Parameter of method stringency (default is 3).
channelBackgroundFilter

Filtered beads will have weight 0 and non filtered weight 1.
channelAndVector

Represents vector to bitvise multiple to the channelBackgroundFilter vector.

Author(s)

Vojtěch Kulvait

Background channel subtraction

Description

Function to subtract one channel from another producing new channel. Standard graphic subtraction.

Usage

backgroundChannelSubtract(b, normalizationMod = NULL, channelSubtractFrom = "GrnF", 
    channelSubtractWhat = "GrnB", channelResult = "Grn")

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for performing on all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelSubtractFrom

Name of channel to subtract from.
channelSubtractWhat

Name of channel to subtract.
channelResult

Result channel, if this channel exists it will be overwritten.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #To perform background correction on blimatesting object for two groups. Background correction is followed by correction for non positive data. Array spots out of selected groups will not be processed.
    data(blimatesting)
    #Prepare logical vectors corresponding to conditions A and E.
    groups1 = "A";
    groups2 = "E";
    sampleNames = list()
    c = list()
    for(i in 1:length(blimatesting))
    {
        p = pData(blimatesting[[i]]@experimentData$phenoData)
        c[[i]] = p$Group %in% c(groups1, groups2);
        sampleNames[[i]] = p$Name
    }
    #Background correction and quantile normalization followed by testing including log2TransformPositive transformation.
    blimatesting = bacgroundCorrect(blimatesting, normalizationMod=c, channelBackgroundFilter="bgf")
    blimatesting = nonPositiveCorrect(blimatesting, normalizationMod=c, channelCorrect="GrnF",  channelBackgroundFilter="bgf", channelAndVector="bgf")
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Background channel subtraction

Description

INTERNAL FUNCTION Correction for positive values only

Usage

backgroundChannelSubtractSingleArray(b, normalizationMod = NULL, 
    channelSubtractFrom = "GrnF", channelSubtractWhat = "GrnB", 
    channelResult = "Grn")

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelSubtractFrom

Name of channel to subtract from.
channelSubtractWhat

Name of channel to subtract.
channelResult

Result channel, if this channel exists it will be overwritten.

Author(s)

Vojtěch Kulvait

Internal function

Description

Test existence of channel slot based on vector list

Usage

channelExistsIntegrityWithLogicalVectorList(b, spotsToCheck = NULL, 
    slotToCheck, action = c("returnText", "warn", "error"))

Arguments

b

List of beadLevelData objects.
spotsToCheck

NULL for check all spots from b. Otherwise specifies logical vector of the length equals to the number of arrays in b with TRUE for checking.
slotToCheck

Slot name to check
action

What type of action is required in case of invalid object structure. Either return text different from TRUE, warn or error.

Author(s)

Vojtěch Kulvait

Internal function

Description

Check integrity of the list of beadLevelData objects or single beadLevelData object returns waslist.

Usage

checkIntegrity(b, action = c("warn", "error"))

Arguments

b

List of beadLevelData objects or single.
action

What type of action is required in case of invalid object structure. Either return text different from TRUE, warn or error.

Value

Returns value if the object was list or not before calling this function.

Author(s)

Vojtěch Kulvait

Internal function

Description

Check integrity of the list of logical objects, internal.

Usage

checkIntegrityLogical(xx, b, action = c("returnText", "warn", 
    "error"))

Arguments

xx

List of logical objects compatible with a list b.
b

List of beadLevelData objects.
action

What type of action is required in case of invalid object structure. Either return text different from TRUE, warn or error.

Author(s)

Vojtěch Kulvait

Internal function

Description

Check integrity of the list of beadLevelData objects, internal.

Usage

checkIntegrityOfListOfBeadLevelDataObjects(listb, action = c("returnText", 
    "warn", "error"))

Arguments

listb

List of beadLevelData objects.
action

What type of action is required in case of invalid object structure. Either return text different from TRUE, warn or error.

Author(s)

Vojtěch Kulvait

Internal function

Description

Check integrity of single beadLevelData object, internal.

Usage

checkIntegrityOfSingleBeadLevelDataObject(b, action = c("returnText", 
    "warn", "error"))

Arguments

b

beadLevelData object.
action

What type of action is required in case of invalid object structure. Either return text different from TRUE, warn or error.

Author(s)

Vojtěch Kulvait

Statistics of beadLevelData

Description

This function returns table with statistics of single beadLevelData object indexed by order of spots. It prints number of beads on each array spot mean foreground intensity and optionally mean background intensity, mean number of beads in probe set and unbiased estimate of standard deviations of these parameters. Optionaly you can also obtain percentage of removed beads within excludedOnSDMultiple multiple of standard deviations from the background value.

Usage

chipArrayStatistics(b, includeBeadStatistic = TRUE, channelForeground = "GrnF", 
    channelBackground = "GrnB", includeBackground = TRUE, excludedOnSDMultiple = NA)

Arguments

b

Single beadLevelData object.
includeBeadStatistic

Include number of beads per probe in output.
channelForeground

Name of channel of foreground.
channelBackground

Name of channel of background.
includeBackground

Whether to output background data.
excludedOnSDMultiple

If positive number, print how much percents of the background lies more than excludedOnSDMultiple multipliers of standard deviation estimate away from background mean.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #To print basic statistic data about blimatesting[[1]] object.
    data(blimatesting)
    array1stats = chipArrayStatistics(blimatesting[[1]], includeBeadStatistic=TRUE,
            excludedOnSDMultiple=3)
    array1pheno = pData(blimatesting[[1]]@experimentData$phenoData)
    array1stats = data.frame(array1pheno$Name, array1stats)
    colnames(array1stats)[1] <- "Array";
    print(array1stats);
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Summarized value matrix.

Description

This function creates summarized matrix of values of certain type.

Usage

createSummarizedMatrix(b, spotsToProcess = NULL, quality = "qua", 
    channelInclude = "bgf", annotationTag = NULL)

Arguments

b

List of beadLevelData objects (or single object).
spotsToProcess

NULL for processing all spots in b. Otherwise specifies logical vector of the length equals to the number of arrays in b.
quality

Quality to matrize.
channelInclude

This field allows user to set channel with weights which have to be from 0,1. All zero weighted items are excluded from summarization. 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").
annotationTag

Tag from annotation file which to use in resulting matrix as colname.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && require("illuminaHumanv4.db") && interactive())
{
    #Create summarization of nonnormalized data from GrnF column.
    data(blimatesting)
    blimatesting = bacgroundCorrect(blimatesting, channelBackgroundFilter="bgf")
    blimatesting = nonPositiveCorrect(blimatesting, channelCorrect="GrnF",  channelBackgroundFilter="bgf", channelAndVector="bgf")
    #Prepare logical vectors corresponding to conditions A(groups1Mod), E(groups2Mod) and both(processingMod).
    nonnormalized = createSummarizedMatrix(blimatesting, quality="GrnF", channelInclude="bgf",
            annotationTag="Name")
    head(nonnormalized)
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Internal function

Description

Performs action of certain type

Usage

doAction(message, action = c("returnText", "warn", "error"))

Arguments

message

Text message.
action

What type of action is required in case of invalid object structure. Either return text different from TRUE, warn or error.

Author(s)

Vojtěch Kulvait

T-test for probe level data.

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)

Vojtěch 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').");
}

T-test for bead (detector) level data.

Description

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

Usage

doTTests(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 log2TransformPositive.

Author(s)

Vojtěch 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').");
}

Bg correct vector

Description

Background correction procedure selecting beads with background Intensity I_b |mean - I_b | > k*SD(I_bs) for exclusion, internal.

Usage

filterBg(x, k = 3)

Arguments

x

Vector to correct
k

Parameter of method stringency (default is 3).

Author(s)

Vojtěch Kulvait

Support probe and beadl level testing.

Description

Internal function supporting probe and beadl level testing.

Usage

getNextVector(what, from, length)

Arguments

what

Two column sorted matrix with probe values.
from

Index to start on
length

nrow(what)

Author(s)

Vojtěch Kulvait

initMeanDistribution

Description

This is internal function not intended to direct use which initializes mean distribution.

Usage

initMeanDistribution(srt, prvku)

Arguments

srt

vector of sorted values
prvku

number of items in meanDistribution

Author(s)

Vojtěch Kulvait

Internal function to support chipArrayStatistics

Description

Internal

Usage

insertColumn(matrix, column, name)

Arguments

matrix

Object to insert column to
column

Column to insert
name

Name of column to assign.

Author(s)

Vojtěch Kulvait

Interpolate sorted vector

Description

Interpolates given sorted vector to the vector of different length. It does not sort input vector thus for unsorted vectors do not guarantee functionality. Internal function.

Usage

interpolateSortedVector(vector, newSize)

Arguments

vector

Sorted vector to interpolate.
newSize

Size of the vector to produce.

Author(s)

Vojtěch Kulvait

interpolateSortedVectorRcpp

Usage

interpolateSortedVectorRcpp_(vector, newSize)

Arguments

vector
newSize

Author(s)

Vojtěch Kulvait

Log2 transform of numbers >1.

Description

Transformation function are popular in beadarray package. Here this is similar concept. This function allow user to perform log transformation before doing t-tests.

Usage

log2TransformPositive(x)

Arguments

x

Number to transform.

Value

This function returns logarithm of base 2 for numbers >=1 and zero for numbers <1.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && require("illuminaHumanv4.db") && interactive())
{
    #To perform background correction, quantile normalization and then bead level t-test on log data run. Vst is not performed in this scheme. Top 10 probes is then printed according to certain measure.
    data(blimatesting)
    #Prepare logical vectors corresponding to conditions A(groups1Mod), E(groups2Mod) and both(c).
    groups1 = "A";
    groups2 = "E";
    sampleNames = list()
    groups1Mod = list()
    groups2Mod = list()
    c = list()
    for(i in 1:length(blimatesting))
    {
        p = pData(blimatesting[[i]]@experimentData$phenoData)
        groups1Mod[[i]] = p$Group %in% groups1;
        groups2Mod[[i]] = p$Group %in% groups2;
        c[[i]] = p$Group %in% c(groups1, groups2);
        sampleNames[[i]] = p$Name
    }
    #Background correction and quantile normalization followed by testing including log2TransformPositive transformation.
    blimatesting = bacgroundCorrect(blimatesting, normalizationMod =c, channelBackgroundFilter="bgf")
    blimatesting = nonPositiveCorrect(blimatesting, normalizationMod=c, channelCorrect="GrnF",  channelBackgroundFilter="bgf", channelAndVector="bgf")
    blimatesting = quantileNormalize(blimatesting, normalizationMod=c, channelNormalize="GrnF", channelOutput="qua", channelInclude="bgf")
    beadTest <- doTTests(blimatesting, groups1Mod, groups2Mod,
            transformation=log2TransformPositive, quality="qua", channelInclude="bgf")
    symbol2address <- merge(toTable(illuminaHumanv4ARRAYADDRESS), toTable(illuminaHumanv4SYMBOLREANNOTATED))
    symbol2address <- symbol2address[,c("SymbolReannotated", "ArrayAddress") ]
    colnames(symbol2address) <- c("Symbol", "ArrayAddressID")
    beadTest = merge(beadTest, symbol2address, by.x="ProbeID", by.y="ArrayAddressID")
    beadTestID = beadTest[,c("ProbeID", "Symbol")]
    beadTestFC = abs(beadTest[,"mean1"]-beadTest[,"mean2"])
    beadTestP = beadTest[,"adjustedp"]
    beadTestMeasure = (1-beadTestP)*beadTestFC
    beadTest = cbind(beadTestID, beadTestMeasure)
    colnames(beadTest) <- c("ArrayAddressID", "Symbol", "difexBL")
    sortBL = sort(-beadTest[,"difexBL"], index.return=TRUE)$ix
    beadTop10 = beadTest[sortBL[1:10],]
    print(beadTop10)
}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').");
}

Produce sorted double vector with mean distribution.

Description

This function processes arrays in the object beadLevelData from package beadarray and returns sorted double vector. The vector has length prvku. And the distribution of this vector is a "mean" of all distributions of distributionChannel quantity in arrays. In case that probe numbers are different from prvku it does some averaging.

Usage

meanDistribution(b, normalizationMod = NULL, distributionChannel = "Grn", 
    channelInclude = NULL, prvku)

Arguments

b

Object beadLevelData from package beadarray or list of these objects
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes (defaults to NULL).

distributionChannel

Channel to do mean distribution from (defaults to "Grn").
channelInclude

This field allows user to set channel with weights which have to be in 0,1. All zero weighted items are excluded from quantile normalization and the value asigned to such probes is a close to value which would be assigned to them if not being excluded. 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 NULL).
prvku

Number of items in a resulting double vector. Prvku must not be more than minimal number of indluded items in any distributionChannel.

Author(s)

Vojtěch Kulvait

INTERNAL FUNCTION Xie background correct.

Description

INTERNAL This function is not intended for direct use. Background correction according to non parametric estimator in Xie, Yang, Xinlei Wang, and Michael Story. "Statistical Methods of Background Correction for Illumina BeadArray Data." Bioinformatics 25, no. 6 (March 15, 2009): 751-57. doi:10.1093/bioinformatics/btp040. The method is applied on the bead level.

Usage

nonParametricEstimator(toCorrectAll, toCorrectNeg)

Arguments

toCorrectAll
toCorrectNeg

Author(s)

Vojtěch Kulvait

Correct non positive

Description

Correction for positive values only

Usage

nonPositiveCorrect(b, normalizationMod = NULL, channelCorrect = "GrnF", 
    channelBackgroundFilter = "bgf", channelAndVector = NULL)

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelCorrect

Name of channel to correct.
channelBackgroundFilter

Filtered beads will have weight 0 and non filtered weight 1.
channelAndVector

Represents vector to bitvise multiple to the channelBackgroundFilter vector.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #To perform background correction on blimatesting object for two groups. Background correction is followed by correction for non positive data. Array spots out of selected groups will not be processed.
    data(blimatesting)
    #Prepare logical vectors corresponding to conditions A and E.
    groups1 = "A";
    groups2 = "E";
    sampleNames = list()
    c = list()
    for(i in 1:length(blimatesting))
    {
        p = pData(blimatesting[[i]]@experimentData$phenoData)
        c[[i]] = p$Group %in% c(groups1, groups2);
        sampleNames[[i]] = p$Name
    }
    #Background correction and quantile normalization followed by testing including log2TransformPositive transformation.
    blimatesting = bacgroundCorrect(blimatesting, normalizationMod=c, channelBackgroundFilter="bgf")
    blimatesting = nonPositiveCorrect(blimatesting, normalizationMod=c, channelCorrect="GrnF",  channelBackgroundFilter="bgf", channelAndVector="bgf")
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Correct non positive

Description

INTERNAL FUNCTION Correction for positive values only

Usage

nonPositiveCorrectSingleArray(b, normalizationMod = NULL, channelCorrect = "GrnF", 
    channelBackgroundFilter = "bgf", channelAndVector = NULL)

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelCorrect

Name of channel to correct.
channelBackgroundFilter

Filtered beads will have weight 0 and non filtered weight 1.
channelAndVector

Represents vector to bitvise multiple to the channelBackgroundFilter vector.

Author(s)

Vojtěch Kulvait

Internal

Description

Internal function

Usage

numberOfDistributionElements(b, normalizationMod = NULL, channelInclude = NULL)

Arguments

b

Object beadLevelData from package beadarray or list of these objects
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelInclude

Author(s)

Vojtěch Kulvait

INTERNAL FUNCTION Xie background correct.

Description

INTERNAL This function is not intended for direct use. Background correction according to non parametric estimator in Xie, Yang, Xinlei Wang, and Michael Story. "Statistical Methods of Background Correction for Illumina BeadArray Data." Bioinformatics 25, no. 6 (March 15, 2009): 751-57. doi:10.1093/bioinformatics/btp040. ###The method is applied on the bead level.

Usage

performXieCorrection(value, alpha, mu, sigma)

Arguments

value
alpha
mu
sigma

Author(s)

Vojtěch Kulvait

Plot background image after correction

Description

This function plots image of background distribution versus to foreground after background subtraction.

Usage

plotBackgroundImageAfterCorrection(b, index, channelForeground = "GrnF", 
    channelBackground = "GrnB", SDMultiple = 3, includePearson = FALSE)

Arguments

b

Single beadLevelData object.
index

Index of spot to generate.
channelForeground

Name of channel of foreground.
channelBackground

Name of channel of background.
SDMultiple

Correct on this level.
includePearson

Include Pearson corelation.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #Write background images after correction. This function prints graph for condition D4. Call dev.off() to close.
    data(blimatesting)
    p = pData(blimatesting[[2]]@experimentData$phenoData)
    index = base::match("D4", p$Name)
    plotBackgroundImageAfterCorrection(blimatesting[[2]], index)
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Plot background image before correction

Description

This function plots image of background distribution versus to foreground before background subtraction.

Usage

plotBackgroundImageBeforeCorrection(b, index, channelForeground = "GrnF", 
    channelBackground = "GrnB", includePearson = FALSE)

Arguments

b

Single beadLevelData object.
index

Index of spot to generate.
channelForeground

Name of channel of foreground.
channelBackground

Name of channel of background.
includePearson

Include Pearson corelation.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #Write background images before correction. This function prints graph for condition D4. Call dev.off() to close.
    data(blimatesting)
    p = pData(blimatesting[[2]]@experimentData$phenoData)
    index = base::match("D4", p$Name)
    plotBackgroundImageBeforeCorrection(blimatesting[[2]], index)
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Bead level quantile normalization.

Description

This function does quantile normalization of object beadLevelData from package beadarray.

Usage

quantileNormalize(b, normalizationMod = NULL, channelNormalize = "Grn", 
    channelOutput = "qua", channelInclude = NULL, dst)

Arguments

b

Object beadLevelData from package beadarray or list of these objects
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelNormalize

Name of channel to normalize.
channelOutput

Name of output normalized channel.
channelInclude

This field allows user to set channel with weights which have to be in 0,1. All zero weighted items are excluded from quantile normalization and the value asigned to such probes is a close to value which would be assigned to them if not being excluded. 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 NULL).
dst

User can specify sorted vector which represents distribution that should be assigned to items.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #To perform background correction, variance stabilization and quantile normalization.
    data(blimatesting)
    #Prepare logical vectors corresponding to conditions A(groups1Mod), E(groups2Mod) and both(c).
    groups1 = "A";
    groups2 = "E";
    sampleNames = list()
    processingMod = list()
    for(i in 1:length(blimatesting))
    {
        p = pData(blimatesting[[i]]@experimentData$phenoData)
        processingMod[[i]] = p$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")
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Support doTTests function.

Description

Internal function supporting doTTests function.

Usage

readToVector(what, from, length, quality)

Arguments

what

Item to read.
from

From index.
length

Length of vector.
quality

Column.

Author(s)

Vojtěch Kulvait

Channel transformation

Description

Function to transform channel data.

Usage

selectedChannelTransform(b, normalizationMod = NULL, channelTransformFrom, 
    channelResult, transformation = NULL)

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for performing on all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelTransformFrom

Name of channel to transform.
channelResult

Result channel, if this channel exists it will be overwritten.
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)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #To perform background correction on blimatesting object for two groups. Background correction is followed by correction for non positive data. Array spots out of selected groups will not be processed.
    data(blimatesting)
    #Prepare logical vectors corresponding to conditions A and E.
    groups1 = "A";
    groups2 = "E";
    sampleNames = list()
    c = list()
    for(i in 1:length(blimatesting))
    {
        p = pData(blimatesting[[i]]@experimentData$phenoData)
        c[[i]] = p$Group %in% c(groups1, groups2);
        sampleNames[[i]] = p$Name
    }
    #Background correction and quantile normalization followed by testing including log2TransformPositive transformation.
    blimatesting = bacgroundCorrect(blimatesting, normalizationMod=c, channelBackgroundFilter="bgf")
    blimatesting = nonPositiveCorrect(blimatesting, normalizationMod=c, channelCorrect="GrnF",  channelBackgroundFilter="bgf", channelAndVector="bgf")
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Channel transformation

Description

Function to transform channel data.

Usage

selectedChannelTransformSingleArray(b, normalizationMod = NULL, 
    channelTransformFrom, channelResult, transformation)

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for performing on all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelTransformFrom

Name of channel to transform.
channelResult

Result channel, if this channel exists it will be overwritten.
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)

Vojtěch Kulvait

Bead level quantile normalization.

Description

This function does quantile normalization of object beadLevelData from package beadarray. Internal function not intended to direct use. Please use quantileNormalize.

Usage

singleArrayNormalize(b, normalizationMod = NULL, channelNormalize = "Grn", 
    channelOutput = "qua", channelInclude = NULL, dst)

Arguments

b

Object beadLevelData from package beadarray
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b.
channelNormalize

Name of channel to normalize.
channelOutput

Name of output normalized channel.
channelInclude

This field allows user to set channel with weights which have to be in 0,1. All zero weighted items are excluded from quantile normalization and the value asigned to such probes is a close to value which would be assigned to them if not being excluded. 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 NULL).
dst

This field must be sorted. It is a distribution of values to assign to ports. By default this distribution is computed using meanDistribution function.

Author(s)

Vojtěch Kulvait

Internal function

Description

Test existence of channel slot based on logical list

Usage

singleChannelExistsIntegrityWithLogicalVector(b, spotsToCheck = NULL, 
    slotToCheck, action = c("returnText", "warn", "error"))

Arguments

b

single beadLevelData object
spotsToCheck

NULL for check all spots from b. Otherwise specifies logical vector of the length equals to the number of arrays in b with TRUE for checking.
slotToCheck

Slot name to check
action

What type of action is required in case of invalid object structure. Either return text different from TRUE, warn or error.

Author(s)

Vojtěch Kulvait

Internal function

Description

Check integrity of the logical object, internal.

Usage

singleCheckIntegrityLogicalVector(xx, b, action = c("returnText", 
    "warn", "error"))

Arguments

xx

Logical object compatible with b.
b

Single beadLevelData object.
action

What type of action is required in case of invalid object structure. Either return text different from TRUE, warn or error.

Author(s)

Vojtěch Kulvait

Internal

Description

Internal function

Usage

singleNumberOfDistributionElements(b, normalizationMod = NULL, 
    channelInclude = NULL)

Arguments

b

Object beadLevelData from package beadarray
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
channelInclude

Author(s)

Vojtěch Kulvait

updateMeanDistribution

Description

This is internal function not intended to direct use. Updates mean distribution.

Usage

updateMeanDistribution(meanDistribution, srt, arraysUsed)

Arguments

meanDistribution
srt

vector of sorted values
arraysUsed

number of arrays allready used to create distribution

Author(s)

Vojtěch Kulvait

Bead level VST.

Description

This function does variance stabilising step on bead level.

Usage

varianceBeadStabilise(b, normalizationMod = NULL, quality = "qua", 
    channelInclude = "bgf", channelOutput = "vst")

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equal to the number of arrays in b or list of such vectors if b is a list of beadLevelData classes.
quality

Quality to analyze, default is "qua".
channelInclude

This field allows user to set channel with weights which have to be in 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").
channelOutput

Output from VST.

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #To perform background correction, variance stabilization and quantile normalization.
    data(blimatesting)
    #Prepare logical vectors corresponding to conditions A(groups1Mod), E(groups2Mod) and both(c).
    groups1 = "A";
    groups2 = "E";
    sampleNames = list()
    processingMod = list()
    for(i in 1:length(blimatesting))
    {
        p = pData(blimatesting[[i]]@experimentData$phenoData)
        processingMod[[i]] = p$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")
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Bead level VST.

Description

This function is not intended to direct use it takes single beadLevelData object and do bead level variance stabilisation.

Usage

varianceBeadStabiliseSingleArray(b, normalizationMod = NULL, 
    quality = "qua", channelInclude = "bgf", channelOutput = "vst")

Arguments

b

Object beadLevelData.
normalizationMod

NULL for normalization of all input b. Otherwise specifies logical vector of the length equals to the number of arrays in b.
quality

Quality to analyze, default is "qua".
channelInclude

This field allows user to set channel with weights which have to be in 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").
channelOutput

Output from VST.

Author(s)

Vojtěch Kulvait

Function from LGPL lumi package 2.16.0

Description

This function is derived from copy and paste of lumi::vst function. Since lumi package has extensive imports I decided to hardcode this function to the blima instead of importing lumi package.

Usage

vstFromLumi(u, std, nSupport = min(length(u), 500), backgroundStd = NULL, 
    lowCutoff = 1/3)

Arguments

u

The mean of probe beads
std

The standard deviation of the probe beads
nSupport

Something for c3 guess.
backgroundStd

Estimate the background variance c3. Input should be variance according to article, not SD.
lowCutoff

Something for c3 guess.

Author(s)

authors are Pan Du, Simon Lin, the function was edited by Vojtěch Kulvait

References

http://www.bioconductor.org/packages/release/bioc/html/lumi.html

Write Background Images

Description

This function writes images with background distribution according to foreground before and after background subtraction.

Usage

writeBackgroundImages(b, spotsToGenerate = NULL, imageType = c("jpg", 
    "png", "eps"), channelForeground = "GrnF", channelBackground = "GrnB", 
    SDMultiple = 3, includePearson = FALSE, outputDir = getwd(), 
    width = 505, height = 505)

Arguments

b

Single beadLevelData object.
spotsToGenerate

NULL for generate images for all spots from b. Otherwise specifies logical vector of the length equals to the number of arrays in b with TRUE for images to generate.
imageType

Type of images produced, either jpg, png or eps
channelForeground

Name of channel of foreground.
channelBackground

Name of channel of background.
SDMultiple

Correct on this level.
includePearson

Include Pearson corelation.
outputDir

Directory where to output images.
width

Width of image (default 505 fits well for 86mm 150dpi illustration in Bioinformatics journal:)
height

Height of image

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && interactive())
{
    #Write background images before and after correction for background into /tmp directory. This function creates two jpg images for condition D. Output files are /tmp/6898481102_D_CORRECTED.jpg and /tmp/6898481102_D.jpg.
    data(blimatesting)
    p = pData(blimatesting[[2]]@experimentData$phenoData)
    spotsToGenerate = p$Group %in% "D";
    writeBackgroundImages(blimatesting[[2]], imageType="jpg", spotsToGenerate=spotsToGenerate, includePearson=FALSE, outputDir="/tmp", width=505, height=505)
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData').");
}

Xie background correct.

Description

Background correction according to non parametric estimator in Xie, Yang, Xinlei Wang, and Michael Story. "Statistical Methods of Background Correction for Illumina BeadArray Data." Bioinformatics 25, no. 6 (March 15, 2009): 751-57. doi:10.1093/bioinformatics/btp040.###The method is applied on the bead level.

Usage

xieBacgroundCorrect(b, normalizationMod = NULL, negativeArrayAddresses, 
    channelCorrect, channelResult, channelInclude = NULL)

Arguments

b

List of beadLevelData objects (or single object).
normalizationMod

NULL for processing all spots in b. Otherwise specifies logical vector of the length equals to the number of arrays in b.
negativeArrayAddresses

Vector of addresses of negative control probes on array
channelCorrect

Slot to perform convolution correction.
channelResult

Result channel, if this channel exists it will be overwritten.
channelInclude

This field allows user to set channel with weights which have to be from 0,1. All zero weighted items are excluded from summarization. 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 NULL).

Author(s)

Vojtěch Kulvait

Examples

if(require("blimaTestingData") && exists("annotationHumanHT12V4") && interactive())
{
    #Create vector of negative array addresses.
    negAdr = unique(annotationHumanHT12V4$Controls[annotationHumanHT12V4$Controls$Reporter_Group_Name=="negative", "Array_Address_Id"])
    #Create summarization of nonnormalized data from GrnF column.
    data(blimatesting)
    blimatesting = bacgroundCorrect(blimatesting, channelBackgroundFilter="bgf")
    blimatesting = nonPositiveCorrect(blimatesting, channelCorrect="GrnF",  channelBackgroundFilter="bgf", channelAndVector="bgf")
    blimatesting = xieBacgroundCorrect(blimatesting, negativeArrayAddresses=negAdr, channelCorrect="GrnF", channelResult="GrnFXIE", channelInclude="bgf")
    #Prepare logical vectors corresponding to conditions A(groups1Mod), E(groups2Mod) and both(processingMod).
    xiecorrected = createSummarizedMatrix(blimatesting, quality="GrnFXIE", channelInclude="bgf",
            annotationTag="Name")
    head(xiecorrected)
}else
{
    print("To run this example, please install blimaTestingData package from bioconductor by running BiocManager::install('blimaTestingData') and prepare annotationHumanHT12V4 object according to blimaTestingData manual.");
}

INTERNAL FUNCTION Xie background correct.

Description

INTERNAL This function is not intended for direct use. Background correction according to non parametric estimator in Xie, Yang, Xinlei Wang, and Michael Story. "Statistical Methods of Background Correction for Illumina BeadArray Data." Bioinformatics 25, no. 6 (March 15, 2009): 751-57. doi:10.1093/bioinformatics/btp040. The method is applied on the bead level.

Usage

xieBacgroundCorrectSingleArray(b, normalizationMod = NULL, negativeArrayAddresses, 
    channelCorrect, channelResult, channelInclude = NULL)

Arguments

b

Single beadLevelData object.
normalizationMod

NULL for processing all spots in b. Otherwise specifies logical vector of the length equals to the number of arrays in b.
negativeArrayAddresses

Vector of addresses of negative control probes on array
channelCorrect

Slot to perform convolution correction.
channelResult

Result channel, if this channel exists it will be overwritten.
channelInclude

This field allows user to set channel with weights which have to be from 0,1. All zero weighted items are excluded from summarization. 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 NULL).

Author(s)

Vojtěch Kulvait