Accessors for PM, MM or background probes indices.

Description

Extracts the indexes for PM, MM or background probes.

Usage

mmindex(object, ...)
pmindex(object, ...)
bgindex(object, ...)

Arguments

Details

The indices are ordered by 'fid', i.e. they follow the order that the probes appear in the CEL/XYS files.

Value

A vector of integers representing the rows of the intensity matrix that correspond to PM, MM or background probes.

Examples

## How pm() works
x <- read.celfiles(list.celfiles())
pms0 <- pm(x)
pmi <- pmindex(x)
pms1 <- exprs(x)[pmi,]
identical(pms0, pms1)

Accessors and replacement methods for the intensity/PM/MM/BG matrices.

Description

Accessors and replacement methods for the PM/MM/BG matrices.

Usage

intensity(object)
mm(object, subset = NULL, target='core')
pm(object, subset = NULL, target='core')
bg(object, subset = NULL)
mm(object, subset = NULL, target='core')<-value
pm(object, subset = NULL, target='core')<-value
bg(object)<-value

Arguments

Details

For all objects but TilingFeatureSet , these methods will return matrices. In case of TilingFeatureSet objects, the value is a 3-dimensional array (probes x samples x channels).

intensity will return the whole intensity matrix associated to the object. pm , mm , bg will return the respective PM/MM/BG matrix.

When applied to ExonFeatureSet or GeneFeatureSet objects, pm will return the PM matrix at the transcript level ('core' probes) by default. The user should set the target argument accordingly if something else is desired. The valid values are: 'probeset' (Exon and Gene arrays), 'core' (Exon and Gene arrays), 'full' (Exon arrays) and 'extended' (Exon arrays).

The target argument has no effects when used on designs other than Gene and Exon ST.

Examples

if (require(maqcExpression4plex) & require(pd.hg18.60mer.expr)){
xysPath <- system.file("extdata", package="maqcExpression4plex")
xysFiles <- list.xysfiles(xysPath, full.name=TRUE)
ngsExpressionFeatureSet <- read.xysfiles(xysFiles)
pm(ngsExpressionFeatureSet)[1:10,]
}

MA plots

Description

Create MA plots using a reference array (if one channel) or using channel2 as reference (if two channel).

Usage

MAplot(object, ...)
list(list("MAplot"), list("FeatureSet"))(object, what=pm, transfo=log2, groups,
       refSamples, which, pch=".", summaryFun=rowMedians,
       plotFun=smoothScatter, main="vs pseudo-median reference chip",
       pairs=FALSE, ...)
list(list("MAplot"), list("TilingFeatureSet"))(object, what=pm, transfo=log2, groups,
       refSamples, which, pch=".", summaryFun=rowMedians,
       plotFun=smoothScatter, main="vs pseudo-median reference chip",
       pairs=FALSE, ...)
list(list("MAplot"), list("PLMset"))(object, what=coefs, transfo=identity, groups,
       refSamples, which, pch=".", summaryFun=rowMedians,
       plotFun=smoothScatter, main="vs pseudo-median reference chip",
       pairs=FALSE, ...)
list(list("MAplot"), list("matrix"))(object, what=identity, transfo=identity,
       groups, refSamples, which, pch=".", summaryFun=rowMedians,
       plotFun=smoothScatter, main="vs pseudo-median reference chip",
       pairs=FALSE, ...)
list(list("MAplot"), list("ExpressionSet"))(object, what=exprs, transfo=identity,
       groups, refSamples, which, pch=".", summaryFun=rowMedians,
       plotFun=smoothScatter, main="vs pseudo-median reference chip",
       pairs=FALSE, ...)

Arguments

Details

MAplot will take the following extra arguments:

• subset : indices of elements to be plotted to reduce impact of plotting 100's thousands points (if pairs=FALSE only);

• span : see loess ;

• family.loess : see loess ;

• addLoess : logical flag (default TRUE) to add a loess estimate;

• parParams : list of params to be passed to par() (if pairs=TRUE only);

Value

Plot

Seealso

plot , smoothScatter

Author

Benilton Carvalho - based on Ben Bolstad's original MAplot function.

Examples

if(require(oligoData) & require(pd.hg18.60mer.expr)){
data(nimbleExpressionFS)
nimbleExpressionFS
groups <- factor(rep(c('brain', 'UnivRef'), each=3))
data.frame(sampleNames(nimbleExpressionFS), groups)
MAplot(nimbleExpressionFS, pairs=TRUE, ylim=c(-.5, .5), groups=groups)
}

Probe Sequeces

Description

Accessor to the (PM/MM/background) probe sequences.

Usage

mmSequence(object)
pmSequence(object, ...)
bgSequence(object, ...)

Arguments

Value

A DNAStringSet containing the PM/MM/background probe sequence associated to the array.

Sequence Base Contents

Description

Function to compute the amounts of each nucleotide in a sequence.

Usage

basecontent(seq)

Arguments

Value

matrix with n rows and 4 columns with the counts for each base.

Examples

sequences <- c("ATATATCCCCG", "TTTCCGAGC")
basecontent(sequences)

Simplified interface to PLM.

Description

Simplified interface to PLM.

Usage

basicPLM(pmMat, pnVec, normalize = TRUE, background = TRUE, transfo =
  log2, method = c('plm', 'plmr', 'plmrr', 'plmrc'), verbose = TRUE)

Arguments

Value

A list with the following components:

*

Seealso

rcModelPLM , rcModelPLMr , rcModelPLMrr , rcModelPLMrc , basicRMA

Note

Currently, only RMA-bg-correction and quantile normalization are allowed.

Author

Benilton Carvalho

Examples

set.seed(1)
pms <- 2^matrix(rnorm(1000), nc=20)
colnames(pms) <- paste("sample", 1:20, sep="")
pns <- rep(letters[1:10], each=5)
res <- basicPLM(pms, pns, TRUE, TRUE)
res[['Estimates']][1:4, 1:3]
res[['StdErrors']][1:4, 1:3]
res[['Residuals']][1:20, 1:3]

Simplified interface to RMA.

Description

Simple interface to RMA.

Usage

basicRMA(pmMat, pnVec, normalize = TRUE, background = TRUE, bgversion = 2, destructive = FALSE, verbose = TRUE, ...)

Arguments

Value

Matrix.

Examples

set.seed(1)
pms <- 2^matrix(rnorm(1000), nc=20)
colnames(pms) <- paste("sample", 1:20, sep="")
pns <- rep(letters[1:10], each=5)
res <- basicRMA(pms, pns, TRUE, TRUE)
res[, 1:3]

Boxplot

Description

Boxplot for observed (log-)intensities in a FeatureSet-like object (ExpressionFeatureSet, ExonFeatureSet, SnpFeatureSet, TilingFeatureSet) and ExpressionSet.

Usage

list(list("boxplot"), list("FeatureSet"))(x, which=c("pm", "mm", "bg", "both", "all"), transfo=log2, nsample=10000, list())
list(list("boxplot"), list("ExpressionSet"))(x, which, transfo=identity, nsample=10000, list())

Arguments

Details

The 'transfo' argument will set the transformation to be used. For raw data, 'transfo=log2' is a common practice. For summarized data (which are often in log2-scale), no transformation is needed (therefore 'transfo=identity').

Seealso

hist , image , sample , set.seed

Note

The boxplot methods for FeatureSet and Expression use a sample (via sample ) of the probes/probesets to produce the plot. Therefore, the user interested in reproducibility is advised to use set.seed .

Accessor for chromosome information

Description

Returns chromosome information.

Usage

%- chromosome(object)pmChr(object)

Arguments

Details

chromosome() returns the chromosomal information for all probes and pmChr() subsets the output to the PM probes only (if a TilingFeatureSet object).

Value

Vector with chromosome information.

Create set of colors, interpolating through a set of preferred colors.

Description

Create set of colors, interpolating through a set of preferred colors.

Usage

darkColors(n)
seqColors(n)
seqColors2(n)
divColors(n)

Arguments

Details

darkColors is based on the Dark2 palette in RColorBrewer, therefore useful to describe qualitative features of the data.

seqColors is based on Blues and generates a gradient of blues, therefore useful to describe quantitative features of the data. seqColors2 behaves similarly, but it is based on OrRd (white-orange-red).

divColors is based on the RdBu pallete in RColorBrewer, therefore useful to describe quantitative features ranging on two extremes.

Examples

x <- 1:10
y <- 1:10
cols1 <- darkColors(10)
cols2 <- seqColors(10)
cols3 <- divColors(10)
cols4 <- seqColors2(10)
plot(x, y, col=cols1, xlim=c(1, 13), pch=19, cex=3)
points(x+1, y, col=cols2, pch=19, cex=3)
points(x+2, y, col=cols3, pch=19, cex=3)
points(x+3, y, col=cols4, pch=19, cex=3)
abline(0, 1, lty=2)
abline(-1, 1, lty=2)
abline(-2, 1, lty=2)
abline(-3, 1, lty=2)

Accessors for physical array coordinates.

Description

Accessors for physical array coordinates.

Usage

getX(object, type)
getY(object, type)

Arguments

Value

A vector with the requested coordinates.

Examples

x <- read.celfiles(list.celfiles())
theXpm <- getX(x, "pm")
theYpm <- getY(x, "pm")

Genotype Calls

Description

Performs genotype calls via CRLMM (Corrected Robust Linear Model with Maximum-likelihood based distances).

Usage

crlmm(filenames, outdir, batch_size=40000, balance=1.5,
      minLLRforCalls=c(5, 1, 5), recalibrate=TRUE,
      verbose=TRUE, pkgname, reference=TRUE)
justCRLMM(filenames, batch_size = 40000, minLLRforCalls = c(5, 1, 5),
recalibrate = TRUE, balance = 1.5, phenoData = NULL, verbose = TRUE,
pkgname = NULL, tmpdir=tempdir())

Arguments

Value

SnpCallSetPlus object.

Tool to fit Probe Level Models.

Description

Fits robust Probe Level linear Models to all the (meta)probesets in an FeatureSet . This is carried out on a (meta)probeset by (meta)probeset basis.

Usage

fitProbeLevelModel(object, background=TRUE, normalize=TRUE, target="core", method="plm", verbose=TRUE, S4=TRUE, ...)

Arguments

Value

fitProbeLevelModel returns an oligoPLM object, if S4=TRUE ; otherwise, it will return a list.

Seealso

rma , summarizationMethods , subset

Note

This is the initial port of fitPLM to oligo. Some features found on the original work by Ben Bolstad (in the affyPLM package) may not be yet available. If you found one of this missing characteristics, please contact Benilton Carvalho.

Author

This is a simplified port from Ben Bolstad's work implemented in the affyPLM package. Problems with the implementation in oligo should be reported to Benilton Carvalho.

References

Bolstad, BM (2004) list("Low Level Analysis of High-density ", " Oligonucleotide Array Data: Background, Normalization and ", " Summarization") . PhD Dissertation. University of California, Berkeley.

Examples

if (require(oligoData)){
data(nimbleExpressionFS)
fit <- fitProbeLevelModel(nimbleExpressionFS)
image(fit)
NUSE(fit)
RLE(fit)
}

Estimate affinity coefficients.

Description

Estimate affinity coefficients using sequence information and splines.

Usage

getAffinitySplineCoefficients(intensities, sequences)

Arguments

Value

Matrix with estimated coefficients.

Seealso

getBaseProfile

Compute and plot nucleotide profile.

Description

Computes and, optionally, lots nucleotide profile, describing the sequence effect on intensities.

Usage

getBaseProfile(coefs, probeLength = 25, plot = FALSE, ...)

Arguments

Value

Invisibly returns a matrix with estimated effects.

Get container information for NimbleGen Tiling Arrays.

Description

Get container information for NimbleGen Tiling Arrays. This is useful for better identification of control probes.

Usage

getContainer(object, probeType)

Arguments

Value

'character' vector with container information.

Function to get CRLMM summaries saved to disk

Description

This will read the summaries written to disk and return them to the user as a SnpCallSetPlus or SnpCnvCallSetPlus object.

Usage

getCrlmmSummaries(tmpdir)

Arguments

Value

If the data were from SNP 5.0 or 6.0 arrays, the function will return a SnpCnvCallSetPlus object. It will return a SnpCallSetPlus object, otherwise.

NetAffx Biological Annotations

Description

Gets NetAffx Biological Annotations saved in the annotation package (Exon and Gene ST Affymetrix arrays).

Usage

getNetAffx(object, type = "probeset")

Arguments

Details

This retrieves NetAffx annotation saved in the (pd) annotation package

• annotation(object). It is only available for Exon ST and Gene ST arrays.

The 'type' argument should match the summarization target used to generate 'object'. The 'rma' method allows for two targets: 'probeset' (target='probeset') and 'transcript' (target='core', target='full', target='extended').

Value

'AnnotatedDataFrame' that can be used as featureData(object)

Author

Benilton Carvalho

Helper function to extract color information for filenames on NimbleGen arrays.

Description

This function will (try to) extract the color information for NimbleGen arrays. This is useful when using read.xysfiles2 to parse XYS files for Tiling applications.

Usage

getNgsColorsInfo(path = ".", pattern1 = "_532", pattern2 = "_635", ...)

Arguments

Details

Many NimbleGen samples are identified following the pattern sampleID_532.XYS / sampleID_635.XYS.

The function suggests sample names if all the filenames follow the standard above.

Value

A data.frame with, at least, two columns: 'channel1' and 'channel2'. A third column, 'sampleNames', is returned if the filenames follow the sampleID_532.XYS / sampleID_635.XYS standard.

Author

Benilton Carvalho bcarvalh@jhsph.edu

Retrieve Platform Design object

Description

Retrieve platform design object.

Usage

getPlatformDesign(object)
getPD(object)

Arguments

Details

Retrieve platform design object.

Value

platformDesign or PDInfo object.

Probe information selector.

Description

A tool to simplify the selection of probe information, so user does not need to use the SQL approaches.

Usage

getProbeInfo(object, field, probeType = "pm", target = "core", sortBy = c("fid", "man_fsetid", "none"), ...)

Arguments

Value

A data.frame with the probe level information.

Note

The code allows for querying info on MM probes, however it has been used mostly on PM probes.

Author

Benilton Carvalho

Examples

if (require(oligoData)){
data(affyGeneFS)
availProbeInfo(affyGeneFS)
probeInfo <- getProbeInfo(affyGeneFS, c('fid', 'x', 'y', 'chrom'))
head(probeInfo)
## Selecting antigenomic background probes
agenGene <- getProbeInfo(affyGeneFS, field=c('fid', 'fsetid', 'type'), target='probeset', subset= type == 'control->bgp->antigenomic')
head(agenGene)
}

Density estimate

Description

Plot the density estimates for each sample

Usage

list(list("hist"), list("FeatureSet"))(x, transfo=log2, which=c("pm", "mm", "bg", "both", "all"),
                   nsample=10000, ...)
list(list("hist"), list("ExpressionSet"))(x, transfo=identity, nsample=10000, ...)

Arguments

Details

The 'transfo' argument will set the transformation to be used. For raw data, 'transfo=log2' is a common practice. For summarized data (which are often in log2-scale), no transformation is needed (therefore 'transfo=identity').

Note

The hist methods for FeatureSet and Expression use a sample (via sample ) of the probes/probesets to produce the plot (unless nsample > nrow(x)). Therefore, the user interested in reproducibility is advised to use set.seed .

Display a pseudo-image of a microarray chip

Description

Produces a pseudo-image ( graphics::image ) for each sample.

Usage

list(list("image"), list("FeatureSet"))(x, which, transfo=log2, ...)
list(list("image"), list("PLMset"))(x, which=0,
                   type=c("weights","resids", "pos.resids","neg.resids","sign.resids"),
                   use.log=TRUE, add.legend=FALSE, standardize=FALSE,
                   col=NULL, main, ...)

Arguments

Examples

if(require(oligoData) & require(pd.hg18.60mer.expr)){
data(nimbleExpressionFS)
par(mfrow=c(1, 2))
image(nimbleExpressionFS, which=4)
##  fit <- fitPLM(nimbleExpressionFS)
##  image(fit, which=4)
plot(1) ## while fixing fitPLM TODO
}

Summarization of SNP data

Description

This function implements the SNPRMA method for summarization of SNP data. It works directly with the CEL files, saving memory.

Usage

justSNPRMA(filenames, verbose = TRUE, phenoData = NULL, normalizeToHapmap = TRUE)

Arguments

Value

SnpQSet or a SnpCnvQSet , depending on the array type.

Examples

## snprmaResults <- justSNPRMA(list.celfiles())

List XYS files

Description

Lists the XYS files.

Usage

list.xysfiles(...)

Arguments

Details

The functions interface list.files and the user is asked to check that function for further details.

Value

Character vector with the filenames.

Seealso

list.files

Examples

list.xysfiles()

Defunct Functions in Package 'oligo'

Description

The functions or variables listed here are no longer part of 'oligo'

Usage

fitPLM(...)
coefs(...)
resids(...)

Arguments

Details

fitPLM was replaced by fitProbeLevelModel , allowing faster execution and providing more specific models. fitPLM was based in the code written by Ben Bolstad in the affyPLM package. However, all the model-fitting functions are now in the package preprocessCore , on which fitProbeLevelModel depends.

coefs and resids , like fitPLM , were inherited from the affyPLM package. They were replaced respectively by coef and residuals , because this is how these statistics are called everywhere else in R .

Class "oligoPLM"

Description

A class to represent Probe Level Models.

Seealso

rma , summarize

Author

This is a port from Ben Bolstad's work implemented in the affyPLM package. Problems with the implementation in oligo should be reported to the package's maintainer.

References

Bolstad, BM (2004) list("Low Level Analysis of High-density ", " Oligonucleotide Array Data: Background, Normalization and ", " Summarization") . PhD Dissertation. University of California, Berkeley.

Examples

## TODO: review code and fix broken
if (require(oligoData)){
data(nimbleExpressionFS)
fit <- fitProbeLevelModel(nimbleExpressionFS)
image(fit)
NUSE(fit)
RLE(fit)
}

The oligo package: a tool for low-level analysis of oligonucleotide arrays

Description

The oligo package provides tools to preprocess different oligonucleotide arrays types: expression, tiling, SNP and exon chips. The supported manufacturers are Affymetrix and NimbleGen.

It offers support to large datasets (when the bigmemory is loaded) and can execute preprocessing tasks in parallel (if, in addition to bigmemory , the snow package is also loaded).

Details

The package will read the raw intensity files (CEL for Affymetrix; XYS for NimbleGen) and allow the user to perform analyses starting at the feature-level.

Reading in the intensity files require the existence of data packages that contain the chip specific information (X/Y coordinates; feature types; sequence). These data packages packages are built using the pdInfoBuilder package.

For Affymetrix SNP arrays, users are asked to download the already built annotation packages from BioConductor. This is because these packages contain metadata that are not automatically created. The following annotation packages are available:

50K Xba - pd.mapping50kxba.240 50K Hind - pd.mapping50khind.240 250K Sty - pd.mapping250k.sty 250K Nsp - pd.mapping250k.nsp GenomeWideSnp 5 (SNP 5.0) - pd.genomewidesnp.5 GenomeWideSnp 6 (SNP 6.0) - pd.genomewidesnp.6

For users interested in genotype calls for SNP 5.0 and 6.0 arrays, we strongly recommend the use use the crlmm package, which implements a more efficient version of CRLMM.

Author

Benilton Carvalho - carvalho@bclab.org

References

Carvalho, B.; Bengtsson, H.; Speed, T. P. & Irizarry, R. A. Exploration, Normalization, and Genotype Calls of High Density Oligonucleotide SNP Array Data. Biostatistics, 2006.

Methods for P/A Calls

Description

Methods for Present/Absent Calls are meant to provide means of assessing whether or not each of the (PM) intensities are compatible with observations generated by background probes.

Usage

paCalls(object, method, ..., verbose=TRUE)
list(list("paCalls"), list("ExonFeatureSet"))(object, method, verbose = TRUE)
list(list("paCalls"), list("GeneFeatureSet"))(object, method, verbose = TRUE)
list(list("paCalls"), list("ExpressionFeatureSet"))(object, method, ..., verbose = TRUE)

Arguments

Details

For Whole Transcript arrays (Exon/Gene) the valid options for method are 'DABG' (p-values for each probe) and 'PSDABG' (p-values for each probeset). For Expression arrays, the only option currently available for method is 'MAS5'.

ABOUT MAS5 CALLS:

The additional arguments that can be passed to MAS5 are:

• alpha1 : a significance threshold in (0, alpha2);

• alpha2 : a significance threshold in (alpha1, 0.5);

• tau : a small positive constant;

• ignore.saturated : if TRUE, do the saturation correction described in the paper, with a saturation level of 46000;

This function performs the hypothesis test:

H0: median(Ri) = tau, corresponding to absence of transcript H1: median(Ri) > tau, corresponding to presence of transcript

where Ri = (PMi - MMi) / (PMi + MMi) for each i a probe-pair in the probe-set represented by data.

The p-value that is returned estimates the usual quantity:

| Pr(observing a more "present looking" probe-set than data | data is absent)|

So that small p-values imply presence while large ones imply absence of transcript. The detection call is computed by thresholding the p-value as in:

call "P" if p-value < alpha1 call "M" if alpha1 <= p-value < alpha2 call "A" if alpha2 <= p-value

Value

A matrix (of dimension dim(PM) if method="DABG" or "MAS5"; of dimension length(unique(probeNames(object))) x ncol(object) if method="PSDABG") with p-values for P/A Calls.

Author

Benilton Carvalho

References

Clark et al. Discovery of tissue-specific exons using comprehensive human exon microarrays. Genome Biol (2007) vol. 8 (4) pp. R64

Liu, W. M. and Mei, R. and Di, X. and Ryder, T. B. and Hubbell, E. and Dee, S. and Webster, T. A. and Harrington, C. A. and Ho, M. H. and Baid, J. and Smeekens, S. P. (2002) Analysis of high density expression microarrays with signed-rank call algorithms, Bioinformatics, 18(12), pp. 1593--1599.

Liu, W. and Mei, R. and Bartell, D. M. and Di, X. and Webster, T. A. and Ryder, T. (2001) Rank-based algorithms for analysis of microarrays, Proceedings of SPIE, Microarrays: Optical Technologies and Informatics, 4266.

Affymetrix (2002) Statistical Algorithms Description Document, Affymetrix Inc., Santa Clara, CA, whitepaper. http://www.affymetrix.com/support/technical/whitepapers/sadd_whitepaper.pdf

Examples

if (require(oligoData) & require(pd.huex.1.0.st.v2)){
data(affyExonFS)
## Get only 2 samples for example
dabgP = paCalls(affyExonFS[, 1:2])
dabgPS = paCalls(affyExonFS[, 1:2], "PSDABG")
head(dabgP) ## for probe
head(dabgPS) ## for probeset
}

Methods for Log-Ratio plotting

Description

The plotM methods are meant to plot log-ratios for different classes of data.

Access the allele information for PM probes.

Description

Accessor to the allelic information for PM probes.

Usage

pmAllele(object)

Arguments

Access the fragment length for PM probes.

Description

Accessor to the fragment length for PM probes.

Usage

pmFragmentLength(object, enzyme, type=c('snp', 'cn'))

Arguments

Value

A list of length equal to the number of enzymes used for digestion. Each element of the list is a data.frame containing:

• row : the row used to link to the PM matrix;

• length : expected fragment length.

Note

There is not a 1:1 relationship between probes and expected fragment length. For one enzyme, a given probe may be associated to multiple fragment lengths. Therefore, the number of rows in the data.frame may not match the number of PM probes and the row column should be used to match the fragment length with the PM matrix.

Accessor to position information

Description

pmPosition will return the genomic position for the (PM) probes.

Usage

%position(object)pmPosition(object)
pmOffset(object)

Arguments

Details

pmPosition will return genomic position for PM probes on a tiling array.

pmOffset will return the offset information for PM probes on SNP arrays.

Accessor to the strand information

Description

Returns the strand information for PM probes (0 - sense / 1 - antisense).

Usage

pmStrand(object)

Arguments

Tools for microarray preprocessing.

Description

These are tools to preprocess microarray data. They include background correction, normalization and summarization methods.

Usage

backgroundCorrectionMethods()
normalizationMethods()
summarizationMethods()
backgroundCorrect(object, method=backgroundCorrectionMethods(), copy=TRUE, extra, subset=NULL, target='core', verbose=TRUE)
summarize(object, probes=rownames(object), method="medianpolish", verbose=TRUE, ...)
list(list("normalize"), list("FeatureSet"))(object, method=normalizationMethods(), copy=TRUE, subset=NULL,target='core', verbose=TRUE, ...)
list(list("normalize"), list("matrix"))(object, method=normalizationMethods(), copy=TRUE, verbose=TRUE, ...)
list(list("normalize"), list("ff_matrix"))(object, method=normalizationMethods(), copy=TRUE, verbose=TRUE, ...)
normalizeToTarget(object, targetDist, method="quantile", copy=TRUE, verbose=TRUE)

Arguments

Details

Number of rows of object must match the length of probes .

Value

backgroundCorrectionMethods and normalizationMethods will return a character vector with the methods implemented currently.

backgroundCorrect , normalize and normalizeToTarget will return a matrix with same dimensions as the input matrix. If they are applied to a FeatureSet object, the PM matrix will be used as input.

The summarize method will return a matrix with length(unique(probes)) rows and ncol(object) columns.

Examples

ns <- 100
nps <- 1000
np <- 10
intensities <- matrix(rnorm(ns*nps*np, 8000, 400), nc=ns)
ids <- rep(as.character(1:nps), each=np)
bgCorrected <- backgroundCorrect(intensities)
normalized <- normalize(bgCorrected)
summarizationMethods()
expression <- summarize(normalized, probes=ids)
intensities[1:20, 1:3]
expression[1:20, 1:3]
target <- rnorm(np*nps)
normalizedToTarget <- normalizeToTarget(intensities, target)

if (require(oligoData) & require(pd.hg18.60mer.expr)){
## Example of normalization with real data
data(nimbleExpressionFS)
boxplot(nimbleExpressionFS, main='Original')
for (mtd in normalizationMethods()){
message('Normalizing with ', mtd)
res <- normalize(nimbleExpressionFS, method=mtd, verbose=FALSE)
boxplot(res, main=mtd)
}
}

Accessor to feature names

Description

Accessors to featureset names.

Usage

probeNames(object, subset = NULL, ...)
probesetNames(object, ...)

Arguments

Value

probeNames returns a string with the probeset names for each probe on the array. probesetNames , on the other hand, returns the unique probeset names.

Read summaries generated by crlmm

Description

This function read the different summaries generated by crlmm.

Usage

readSummaries(type, tmpdir)

Arguments

Details

On the 50K and 250K arrays, given a SNP, there are probes on both strands (sense and antisense). For this reason, the options 'alleleA-sense', 'alleleA-antisense', 'alleleB-sense' and 'alleleB-antisense' should be used only with such arrays (XBA, HIND, NSP or STY).

On the SNP 5.0 and SNP 6.0 platforms, this distinction does not exist in terms of algorithm (note that the actual strand could be queried from the annotation package). For these arrays, options 'alleleA', 'alleleB' are the ones to be used.

The options calls , llr and conf will return, respectivelly, the CRLMM calls, log-likelihood ratios (for devel purpose only) and CRLMM confidence calls matrices.

Value

Matrix with values of summaries.

Parser to CEL files

Description

Reads CEL files.

Usage

read.celfiles(..., filenames, pkgname, phenoData, featureData,
experimentData, protocolData, notes, verbose=TRUE, sampleNames,
rm.mask=FALSE, rm.outliers=FALSE, rm.extra=FALSE, checkType=TRUE)
read.celfiles2(channel1, channel2, pkgname, phenoData, featureData,
experimentData, protocolData, notes, verbose=TRUE, sampleNames,
rm.mask=FALSE, rm.outliers=FALSE, rm.extra=FALSE, checkType=TRUE)

Arguments

Details

When using 'affyio' to read in CEL files, the user can read compressed CEL files (CEL.gz). Additionally, 'affyio' is much faster than 'affxparser'.

The function guesses which annotation package to use from the header of the CEL file. The user can also provide the name of the annotaion package to be used (via the pkgname argument). If the annotation package cannot be loaded, the function returns an error. If the annotation package is not available from BioConductor, one can use the pdInfoBuilder package to build one.

Value

*

Seealso

list.celfiles , read.xysfiles

Examples

if(require(pd.mapping50k.xba240) & require(hapmap100kxba)){
celPath <- system.file("celFiles", package="hapmap100kxba")
celFiles <- list.celfiles(celPath, full.name=TRUE)
affySnpFeatureSet <- read.celfiles(celFiles)
}

Parser to XYS files

Description

NimbleGen provides XYS files which are read by this function.

Usage

read.xysfiles(..., filenames, pkgname, phenoData, featureData,
experimentData, protocolData, notes, verbose=TRUE, sampleNames,
checkType=TRUE)
read.xysfiles2(channel1, channel2, pkgname, phenoData, featureData,
experimentData, protocolData, notes, verbose=TRUE, sampleNames,
checkType=TRUE)

Arguments

Details

The function will read the XYS files provided by NimbleGen Systems and return an object of class FeatureSet.

The function guesses which annotation package to use from the header of the XYS file. The user can also provide the name of the annotaion package to be used (via the pkgname argument). If the annotation package cannot be loaded, the function returns an error. If the annotation package is not available from BioConductor, one can use the pdInfoBuilder package to build one.

Value

*

Seealso

list.xysfiles , read.celfiles

Examples

if (require(maqcExpression4plex) & require(pd.hg18.60mer.expr)){
xysPath <- system.file("extdata", package="maqcExpression4plex")
xysFiles <- list.xysfiles(xysPath, full.name=TRUE)
ngsExpressionFeatureSet <- read.xysfiles(xysFiles)
}

RMA - Robust Multichip Average algorithm

Description

Robust Multichip Average preprocessing methodology. This strategy allows background subtraction, quantile normalization and summarization (via median-polish).

Usage

list(list("rma"), list("ExonFeatureSet"))(object, background=TRUE, normalize=TRUE, subset=NULL, target="core")
  list(list("rma"), list("HTAFeatureSet"))(object, background=TRUE, normalize=TRUE, subset=NULL, target="core")
  list(list("rma"), list("ExpressionFeatureSet"))(object, background=TRUE, normalize=TRUE, subset=NULL)
  list(list("rma"), list("GeneFeatureSet"))(object, background=TRUE, normalize=TRUE, subset=NULL, target="core")
  list(list("rma"), list("SnpCnvFeatureSet"))(object, background=TRUE, normalize=TRUE, subset=NULL)

Arguments

Seealso

snprma

References

Rafael. A. Irizarry, Benjamin M. Bolstad, Francois Collin, Leslie M. Cope, Bridget Hobbs and Terence P. Speed (2003), Summaries of Affymetrix GeneChip probe level data Nucleic Acids Research 31(4):e15

Bolstad, B.M., Irizarry R. A., Astrand M., and Speed, T.P. (2003), A Comparison of Normalization Methods for High Density O ligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2):185-193

Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2003) Exploration, Normalizati on, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics. Vol. 4, Number 2: 249-264

Examples

if (require(maqcExpression4plex) & require(pd.hg18.60mer.expr)){
xysPath <- system.file("extdata", package="maqcExpression4plex")
xysFiles <- list.xysfiles(xysPath, full.name=TRUE)
ngsExpressionFeatureSet <- read.xysfiles(xysFiles)
summarized <- rma(ngsExpressionFeatureSet)
show(summarized)
}

Date of scan

Description

Retrieves date information in CEL/XYS files.

Usage

runDate(object)

Arguments

Create design matrix for sequences

Description

Creates design matrix for sequences.

Usage

sequenceDesignMatrix(seqs)

Arguments

Details

This assumes all sequences are 25bp long.

The design matrix is often used when the objecive is to adjust intensities by sequence.

Value

Matrix with length(seqs) rows and 75 columns.

Examples

genSequence <- function(x)
paste(sample(c("A", "T", "C", "G"), 25, rep=TRUE), collapse="", sep="")
seqs <- sapply(1:10, genSequence)
X <- sequenceDesignMatrix(seqs)
Y <- rnorm(10, mean=12, sd=2)
Ydemean <- Y-mean(Y)
X[1:10, 1:3]
fit <- lm(Ydemean~X)
coef(fit)

Preprocessing SNP Arrays

Description

This function preprocess SNP arrays.

Usage

snprma(object, verbose = TRUE, normalizeToHapmap = TRUE)

Arguments

Value

A SnpQSet object.