b02-0-ColorCoding-class: Class "ColorCoding"
In oompaBase: Class Unions, Matrix Operations, and Color Schemes for OOMPA

Description Usage Arguments Details Value Objects from the Class Slots Methods Author(s) See Also Examples

A class for associating plotting symbols and colors with a logical vector or with levels of a factor.

1 2	ColorCoding(v, color, mark = 1) colorCode(fac, colorScheme = 1:length(levels(fac)), mark = 1)

`v`	a logical vector
`color`	a string or integer representing a color
`mark`	an integer representing a plotting symbol, or list of plotting symbols that should be associated with levels of the factor `fac`
`fac`	A factor
`colorScheme`	A list of colors that should be associated with levels of the factor `fac`

It is often necessary with microarray data to produce multiple plots, where each point on the plot corresponds to a gene or a spot on the microarray. Across multiple plots, we often want to use symbols or colors to mark subsets of he genes with certain properties. The ColorCoding class works in tandem with the ColorCodedPair class to make it easier to maintain consistent plotting conventions across multiple graphs.

The constructor returns a valid object of the ColorCoding class.

The colorCode function provides a simple interface to associate colors and symbols with the levels of a factor. It returns a list of valid ColorCoding objects that can be passed directly to the constructor of a ColorCodedPair object.

Although objects can be created using new, the preferred method is to use the constructor function, ColorCoding. To create a set of colors and symbols associated with different levels of a factor, use the colorCode function.

v: a logical vector
color: a string or integer representing a color
mark: an integer representing a plotting symbol

There are no specialized methods for objects of this class; all activities can be performed by directly accessing the documented slots.

Kevin R. Coombes krc@silicovore.com, P. Roebuck proebuck@mdanderson.org

ColorCodedPair

showClass("ColorCoding")

theta <- (0:360)*pi/180
x <- cos(theta)
y <- sin(theta)
xp <- x > 0
yp <- y > 0
colors <- list(ColorCoding(xp&yp, oompaColor$BORING),
               ColorCoding(xp&!yp, oompaColor$TOP.TEN),
               ColorCoding(!xp&yp, oompaColor$BOTTOM.TEN),
               ColorCoding(!xp&!yp, oompaColor$CONFIDENCE.CURVE))
plot(ColorCodedPair(x, y, colors))

plot(ColorCodedPair(theta, x, colors))

plot(ColorCodedPair(theta, y, colors),
     xlab='angle in radians', ylab='sine', main='colored sine')

fac <- factor(rep(c('left', 'right'), c(180, 181)))
colors <- colorCode(fac, c('blue', 'red'))
plot(ColorCodedPair(x, y, colors))

## cleanup
rm(x, y, xp, yp, theta, colors, fac)

colorList <- c(oompaColor$BORING, oompaColor$SIGNIFICANT,
               oompaColor$EXPECTED, oompaColor$OBSERVED,
               oompaColor$PERMTEST, oompaColor$FITTED,
               oompaColor$CENTRAL.LINE, oompaColor$CONFIDENCE.CURVE,
               oompaColor$BAD.REPLICATE, oompaColor$WORST.REPLICATE,
               oompaColor$FOLD.DIFFERENCE, oompaColor$BAD.REPLICATE.RATIO,
               oompaColor$TOP.TEN, oompaColor$BOTTOM.TEN,
               oompaColor$TOP.TEN.SOLO, oompaColor$BOTTOM.TEN.SOLO
               )
plot(c(1,4), c(1,4), type='n')
for (i in 1:4) {
  for (j in 1:4) {
    points(i,j, col=colorList[i + 4*(j-1)], pch=16, cex=4)
  }
}
rm(colorList, i, j)