ino {OLIN} | R Documentation

## Intensity-dependent normalisation of two-colour microarrays

### Description

This functions performs intensity-dependent normalisation based on local regression by locfit.

### Usage

    ino(object,alpha=0.3,weights=NA,bg.corr="subtract",...)

### Arguments

`object` | object of class “marrayRaw” or “marrayNorm”
`alpha` | smoothing parameter
`weights` | matrix of weights for local regression. Rows correspond to the spotted probe sequences, columns to arrays in the batch. These may be derived from the matrix of spot quality weights as defined for “maRaw” objects.
`bg.corr` | backcorrection method (for “marrayRaw” objects) : “none” or “subtract”(default).
`...` | Further arguments for `locfit` function.

### Details

The function `ino` regresses the average logged fold changes (M) with respect to the average logged spot intensity (A). The residuals of this fit are the normalised logged fold changes. The parameter `alpha` specifies the fraction of points that are included in the neighbourhood and thus has a value between 0 and 1. Larger `alpha` values lead to smoother fits.

### Value

Object of class “marrayNorm” with normalised logged ratios

### Author(s)

Matthias E. Futschik (http://itb.biologie.hu-berlin.de/~futschik)

### See Also

`maNorm`, `locfit.raw`,`olin`, `oin`, `lin`

### Examples

    # LOADING DATA
      data(sw)

    # INTENSITY-DEPENDENT NORMALISATION
      norm.ino <- ino(sw)

    # MA-PLOT OF NORMALISATION RESULTS OF FIRST ARRAY
      plot(maA(norm.ino)[,1],maM(norm.ino)[,1],main="INO")

    # CORRESPONDING MXY-PLOT
       mxy.plot(maM(norm.ino)[,1],Ngc=maNgc(norm.ino),Ngr=maNgr(norm.ino),
                    Nsc=maNsc(norm.ino),Nsr=maNsr(norm.ino),main="INO")

[Package OLIN version 1.70.0 Index]