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

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.

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, 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")

#

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(OLIN)
Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
Loading required package: marray
Loading required package: limma
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/OLIN/oin.Rd_%03d_medium.png", width=480, height=480)
> ### Name: oin
> ### Title: Optimised intensity-dependent normalisation of two-colour
> ###   microarrays
> ### Aliases: oin
> ### Keywords: utilities regression
> 
> ### ** 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")
> 
> #
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>