summarize {oligo} | R Documentation |
These are tools to preprocess microarray data. They include background correction, normalization and summarization methods.
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, ...) ## S4 method for signature 'FeatureSet' normalize(object, method=normalizationMethods(), copy=TRUE, subset=NULL,target='core', verbose=TRUE, ...) ## S4 method for signature 'matrix' normalize(object, method=normalizationMethods(), copy=TRUE, verbose=TRUE, ...) ## S4 method for signature 'ff_matrix' normalize(object, method=normalizationMethods(), copy=TRUE, verbose=TRUE, ...) normalizeToTarget(object, targetDist, method="quantile", copy=TRUE, verbose=TRUE)
object |
Object containing probe intensities to be preprocessed. |
method |
String determining which method to use at that preprocessing step. |
targetDist |
Vector with the target distribution |
probes |
Character vector that identifies the name of the probes represented
by the rows of |
copy |
Logical flag determining if data must be copied before processing (TRUE), or if data can be overwritten (FALSE). |
subset |
Not yet implemented. |
target |
One of the following values: 'core', 'full', 'extended', 'probeset'. Used only with Gene ST and Exon ST designs. |
extra |
Extra arguments to be passed to other methods. |
verbose |
Logical flag for verbosity. |
... |
Arguments to be passed to methods. |
Number of rows of object
must match the length of
probes
.
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.
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) } }