OLIN (version 1.50.0)

oin: Optimised intensity-dependent normalisation of two-colour microarrays

Description

This functions performs optimised intensity-dependent normalisation (OLIN).

Usage

oin(object,alpha=seq(0.1,1,0.1),weights=NA,bg.corr="subtract",...)

Arguments

object

object of class “marrayRaw” or “marrayNorm”

alpha

vector of alpha parameters that are tested in the GCV procedure

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 “marrayRaw” objects.

bg.corr

backcorrection method (for “marrayRaw” objects) : “none” or “subtract”(default).

...

Further arguments for locfit function.

Value

“marrayNorm” with normalised logged ratios

Details

The function oin is based on iterative local regression of logged fold changes in respect to average logged spot intensities. It incorporates optimisation of the smoothing parameter alpha that controls the neighbourhood size h of local fitting. 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.

If the normalisation should be based on set of genes assumed to be not differentially expressed (house-keeping genes), weights can be used for local regression. In this case, all weights should be set to zero except for the house-keeping genes for which weights are set to one. In order to achieve a reliable regression, it is important, however, that there is a sufficient number of house-keeping genes that are distributed over the whole expression range and spotted accross the whole array.

In contrast to OLIN and OSLIN, the OIN scheme does not correct for spatial dye bias. It can, therefore, be used if the assumption of random spotting does not hold.

See Also

maNorm, locfit, gcv, olin ,lin, ino

Examples

# LOADING DATA
  data(sw)

# OPTIMISED INTENSITY-DEPENDENT NORMALISATION
 norm.oin <- oin(sw)

# MA-PLOT OF NORMALISATION RESULTS OF FIRST ARRAY
 plot(maA(norm.oin)[,1],maM(norm.oin)[,1],main="OIN")
 
# CORRESPONDING MXY-PLOT
  mxy.plot(maM(norm.oin)[,1],Ngc=maNgc(norm.oin),Ngr=maNgr(norm.oin),
                Nsc=maNsc(norm.oin),Nsr=maNsr(norm.oin),main="OIN")

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