ParallelFilter: The ParallelFilter class
In HenrikBengtsson/aroma: An R Object-oriented Microarray Analysis package

Description Usage Arguments Fields and Methods Author(s) See Also Examples

Package: aroma
Class ParallelFilter

Object
~~|
~~+--Filter
~~~~~~~|
~~~~~~~+--ParallelFilter

Directly known subclasses:
AndFilter, OrFilter

public abstract static class ParallelFilter
extends Filter

A ParallelFilter is a filter that passes through indices from several inputs given some criteria. Examples of parallel filters are the AndFilter and the OrFilter, which provides the logical operators AND and OR, respectively.

1	ParallelFilter(..., cex=NULL, col=NULL, pch=NULL, visible=TRUE)

`...`	The input `Filter`s to be connected to.
`cex`	The scale factor of symbols that this filter highlights.
`col`	The color of symbols that this filter highlights.
`pch`	The plot symbols that this filter highlights.
`visible`	If `TRUE`, the data points filtered out by this filter will be highlighted, otherwise not.

Fields

	filters	The input filters.

Methods:

	`changeInput`	Replaces input filters with other ones.
	`getInput`	Gets the input filters.

Methods inherited from Filter:
as.character, changeInput, getIndex, getInput, getParameter, getVisible, highlight, setParameter, setVisible, text

Methods inherited from Object:
$, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFields, getInstanciationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, save

Henrik Bengtsson (http://www.braju.com/R/)

See also the SerialFilter class. For logical filters see AndFilter, OrFilter, and NotFilter. For data filters see MFilter, AFilter, TFilter and SEFilter.

   SMA$loadData("mouse.data")
   layout <- Layout$read("MouseArray.Layout.dat", path=system.file("data-ex", package="aroma"))
   raw <- RawData(mouse.data, layout=layout)

   ma <- getSignal(raw)
   normalizeWithinSlide(ma, "s")
   normalizeAcrossSlides(ma)

   tma <- as.TMAData(ma)

   fM  <- MFilter(tma, top=0.05, col="red")
   fT  <- TFilter(tma, top=0.05, col="blue")
   fNotSE <- SEFilter(tma, range=c(-Inf,0.02), col="yellow")
   fSE <- NotFilter(fNotSE, visible=FALSE)
   myFilter <- AndFilter(fM, fT, fSE, col="purple")

   plot(tma, "TvsSE");
   highlight(myFilter, recursive=TRUE);