olin {OLIN}R Documentation

Optimised local intensity-dependent normalisation of two-colour microarrays

Description

This functions performs optimised local intensity-dependent normalisation (OLIN) and optimised scaled intensity-dependent normalisation (OSLIN).

Usage

olin(object,X=NA,Y=NA,alpha=seq(0.1,1,0.1),iter=3,
            scaling=FALSE,scale=c(0.05,0.1,0.5,1,2,10,20),weights=NA)

Arguments

object object of class “marrayRaw”
X matrix with x-coordinates of spots. If X=NA, columns on array are used as proxies for the location in x-direction
Y matrix with y-coordinates of spots. If Y=NA, rows on array are used as proxies for the location in y-direction
alpha vector of alpha parameters that are tested in the GCV procedure
iter number of iterations in the OLIN procedure
scaling If scaling=TRUE, a subsequent optimised scaling is performed
scale vector of scale parameters that are tested in a 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 “maRaw” objects.

Details

OLIN and OSLIN are based on iterative local regression and incorporate optimisation of model parameters. Local regression is performed using LOCFIT, which requires the user to choose a specific 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. Additionally, the setting of scale parameters controls for distinct amount of smoothing in Y-direction compared to smoothing in X-direction. The parameter scale can be of arbitrary value. The choice of model parameters alpha and scale for local regression is crucial for the efficiency and quality of normalization. To optimize the model parameters, a general cross-validation procedure (GCV) is applied. The arguments alpha and scale define the parameters values which are tested in the GCV. Detailed information about OLIN and OSLIN can be found in the package documentation and in the reference stated below. The weights argument specifies the influence of the single spots on the local regression. To exclude spots being used for the local regression (such as control spots), set their corresponding weight to zero. Note that OLIN and OSLIN are based on the assumptions that most genes are not differentially expressed (or up- and down-regulation is balanced) and that genes are randomly spotted across the array. If these assumptions are not valid, local regression can lead to an underestimation of differential expression. OSLIN is especially sensitive to violations of these assumptions. However, this sensitivity can be decreased if the minimal alpha-value is increased. Minimal alpha defines the smallest scale used for local regression. Increasing alpha can reduce the influence of localised artifacts as a larger fraction of data points is included.

It is also important to note that OLIN/OSLIN is fairly efficient in removing intensity- and spatial-dependent dye bias, so that normalised data will look quite “good” after normalisation independently of the true underlying data quality. Normalisation by local regression assumes smoothness of bias. Therefore, localised artifacts such as scratches, edge effects or bubbles should be avoided. Spots of these areas should be flagged (before normalisation is applied) to ensure data integrity. To stringently detect artifacts, the OLIN functions fdr.int, fdr.spatial, p.int and p.spatial can be used.

Value

Object of class “marrayNorm” with normalised logged ratios

Author(s)

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

References

  1. M.Futschik and T.Crompton (2004) Model selection and efficiency testing for normalization of cDNA microarray data, Genome Biology, 5:R60
  2. OLIN web-page: http://itb.biologie.hu-berlin.de/~futschik/software/R/OLIN

See Also

maNorm, locfit, gcv

Examples


# LOADING DATA
  data(sw)
  data(sw.xy)

# OPTIMISED LOCAL INTENSITY-DEPENDENT NORMALISATION OF FIRST ARRAY
 norm.olin <- olin(sw[,1],X=sw.xy$X[,1],Y=sw.xy$Y[,1])

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

# OPTIMISED SCALED LOCAL INTENSITY-DEPENDENT NORMALISATION
  norm.oslin <- olin(sw[,1],X=sw.xy$X[,1],Y=sw.xy$Y[,1],scaling=TRUE)
# MA-PLOT
  plot(maA(norm.oslin),maM(norm.oslin),main="OSLIN")
# MXY-PLOT
  Mtmp <- mxy.plot(maM(norm.oslin)[,1],Ngc=maNgc(norm.oslin),Ngr=maNgr(norm.oslin),
                 Nsc=maNsc(norm.oslin),Nsr=maNsr(norm.oslin),main="OSLIN")


[Package OLIN version 1.3.2 Index]