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Last data update: 2014.03.03

R: Intensity-dependent normalisation of two-colour microarrays

ino	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)

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")

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/ino.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ino
> ### Title: Intensity-dependent normalisation of two-colour microarrays
> ### Aliases: ino
> ### Keywords: utilities regression
> 
> ### ** 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")
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>