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

Description Usage Arguments Fields and Methods Author(s) Examples

Package: aroma
Class Filter

Object
~~|
~~+--Filter

Directly known subclasses:
AcceptFilter, AFilter, AndFilter, BFilter, DfFilter, FieldFilter, GeneAcceptFilter, GeneRejectFilter, MFilter, NotFilter, OrFilter, ParallelFilter, RejectFilter, SEFilter, SerialFilter, TFilter

public abstract static class Filter
extends Object

A Filter object takes a vector of indices from its input(s) and passes some or all of them through given some criteria. There are two main classes of filters; (1) the SerialFilter class and (2) the ParallelFilter class. Shortly, the serial filters gets some indices from one single source, whereas the parallel filters gets indices from several sources. Example of serial filters are the FieldFilters and the NotFilter. Example of parallel filters are the logical filters AndFilter and OrFilter.

1	Filter(cex=NULL, col=NULL, pch=20, visible=TRUE)

`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

	parameter	A list of parameters for the filter.
	visible	Specifies if the data through this filter should be displayed in plots etc or not.

Methods:

	`as.character`	-
	`changeInput`	Change input(s) on this filter and optionally all filters down the stream.
	`getIndex`	Gets the indices accepted by the filter.
	`getInput`	Gets all the input objects connected to the filter.
	`getParameter`	Gets the values for a specific parameter for indices accepted by the filter.
	`getVisible`	Checks if the filter is visible or not.
	`highlight`	Highlights the data points accepted by the filter.
	`setParameter`	Sets the values for a specific parameter for indices accepted by the filter.
	`setVisible`	Sets if the filter should bee visible or not.
	`text`	Labels the data points accepted by the filter.

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/)

   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, method="s")
   normalizeAcrossSlides(ma)

   tma <- as.TMAData(ma)

   # Filter out too weak spots, i.e. A < 8 (<256).
   fA  <- AFilter(tma, range=c(8, Inf), visible=FALSE)

   # Look at the top 5% M-values.
   fM  <- MFilter(tma, top=0.05, col="red")

   # Look at the top 5% T-values, but not those with to small SE.
   fT  <- TFilter(tma, top=0.05, col="blue")
   fNotSE <- SEFilter(tma, range=c(-Inf,0.02), col="yellow")
   fSE <- NotFilter(fNotSE, visible=FALSE)

   # Selects those spot that passes through all filters.
   myFilter <- AndFilter(fA, fM, fT, fSE, col="purple", pch=19)

   plot(tma, "TvsSE", xlog=2)
   highlight(myFilter, recursive=TRUE)