normalize.pspline {TurboNorm} | R Documentation

## Functions for single-colour microarray data normalization using the P-splines

### Description

Modified version of normalize.loess and normalize.AffyBatch.pspline from the affy package uses the P-spline smoother in stead of the loess algorithm

### Usage

    normalize.pspline(mat, epsilon = 10^-2, maxit = 1, log.it = TRUE,
        verbose = TRUE, weights = rep(1, nrow(mat)), ...)
    normalize.AffyBatch.pspline(abatch,
        type=c("together","pmonly","mmonly","separate"), ...)

### Arguments

`mat` | a matrix with columns containing the values of the chips to normalize.
`abatch` | an `AffyBatch` object.
`epsilon` | a tolerance value (supposed to be a small value - used as a stopping criterion).
`maxit` | maximum number of iterations.
`log.it` | logical. If `TRUE` it takes the log2 of `mat`
`verbose` | logical. If `TRUE` displays current pair of chip being worked on.
`weights` | For weighted normalization. The default is NULL, so there are no weights used.
`type` | A string specifying how the normalization should be applied. See details for more.
`...` | Graphical parameters can be supplied.

### Details

This function is a modified version of the function `normalize.loess` from the affy package. In stead of the loess algorithm the function uses the P-spline algorithm. The type argument should be one of `"separate","pmonly","mmonly","together"` which indicates whether to normalize only one probe type(PM,MM) or both together or separately.

### Value

Normalized `AffyBatch`

### Author(s)

Maarten van Iterson and Chantal van Leeuwen

### References

Laurent Gautier, Leslie Cope, Benjamin M. Bolstad and Rafael A. Irizarry (2004). affy -analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, Vol. 20, no. 3, 307-315.

van Iterson M, Duijkers FA, Meijerink JP, Admiraal P, van Ommen GJ, Boer JM, van Noesel MM, Menezes RX (2012). A novel and fast normalization method for high-density arrays. SAGMB, 11(4).

Paul .H.C. Eilers and Brain D. Marx (1996). Flexible smoothing with B-splines and Penalties. Statistical Science, Vol 11, No. 2, 89-121.

### See Also

`normalize.loess`

### Examples

    library(affydata)
    data(Dilution)
    PM <- log2(pm(Dilution[,c(1,3)]))
    M <- PM[,1]-PM[,2]
    A <- 0.5*(PM[,1]+PM[,2])

    nPM <- log2(normalize.pspline(pm(Dilution[,c(1,3)])))
    nM <- nPM[,1]-nPM[,2]
    nA <- 0.5*(nPM[,1]+nPM[,2])

    par(mfcol=c(2,1))
    plot(M~A)
    plot(nM~nA)

    norm <- normalize.AffyBatch.pspline(Dilution, type="pmonly")

    weights <- rep(1, nrow(exprs(Dilution)))
    normw <- normalize.AffyBatch.pspline(Dilution, type="pmonly", weights=weights)

[Package TurboNorm version 1.38.0 Index]