Description Usage Arguments Details Value Examples
These are tools to preprocess microarray data. They include background correction, normalization and summarization methods.
1 2 3 4 5 6 7 8 9 10 11 12 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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)
}
}
|
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