ino: Intensity-dependent normalisation of two-colour microarrays

In OLIN: Optimized local intensity-dependent normalisation of two-color 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")

OLIN documentation built on Nov. 8, 2020, 7:44 p.m.