Spatial trend of microarray spots statistic

Description

The function arrayTrend computes the spatial trend.

Usage

## Default S3 method:
arrayTrend(Statistic, Col, Row, ...)
## S3 method for class 'arrayCGH'
arrayTrend(arrayCGH, variable, ...)

Arguments

Details

Spatial trend of microarray spots statistic.

Value

Either a data frame with elements :

or the element Trend is added to the data.frame arrayValues of the arrayCGH object.

Note

People interested in tools for array-CGH analysis can visit our web-page: http://bioinfo.curie.fr.

Author(s)

Philippe Hupé, Philippe.Hupe@curie.fr.

References

P. Neuvial, P. Hupé, I. Brito, S. Liva, E. Manié, C. Brennetot, A. Aurias, F. Radvanyi, and E. Barillot. Spatial normalization of array-CGH data. BMC Bioinformatics, 7(1):264. May 2006.

See Also

loess, loess.control.

Examples

data(spatial)  ## arrays with local spatial effects

edgeTrend <- arrayTrend(edge, "LogRatio", span=0.03, degree=1,
iterations=3, family="symmetric") 
arrayPlot(edgeTrend, "Trend", main="Spatial trend of array CGH", bar="v") 

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.

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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(MANOR)
Loading required package: GLAD

######################################################################################

Have fun with GLAD

For smoothing it is possible to use either
the AWS algorithm (Polzehl and Spokoiny, 2002,
or the HaarSeg algorithm (Ben-Yaacov and Eldar, Bioinformatics,  2008,

If you use the package with AWS, please cite:
Hupe et al. (Bioinformatics, 2004, and Polzehl and Spokoiny (2002,

If you use the package with HaarSeg, please cite:
Hupe et al. (Bioinformatics, 2004, and (Ben-Yaacov and Eldar, Bioinformatics, 2008,

For fast computation it is recommanded to use
the daglad function with smoothfunc=haarseg

######################################################################################

New options are available in daglad: see help for details.


Attaching package: 'MANOR'

The following object is masked from 'package:base':

    norm

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MANOR/arrayTrend.Rd_%03d_medium.png", width=480, height=480)
> ### Name: arrayTrend
> ### Title: Spatial trend of microarray spots statistic
> ### Aliases: arrayTrend arrayTrend.default arrayTrend.arrayCGH
> ### Keywords: smooth loess spatial
> 
> ### ** Examples
> 
> data(spatial)  ## arrays with local spatial effects
> 
> edgeTrend <- arrayTrend(edge, "LogRatio", span=0.03, degree=1,
+ iterations=3, family="symmetric") 
> arrayPlot(edgeTrend, "Trend", main="Spatial trend of array CGH", bar="v") 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>