summarize {oligo} | R Documentation

## 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, ...)
    ## 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)

### Arguments

`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 `object`.
`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.

### 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)
      }
    }

[Package oligo version 1.58.0 Index]