tests/normalizeDifferencesToAverage.R

In HenrikBengtsson/aroma.light: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types

library("aroma.light")

# Simulate three shifted tracks of different lengths with same profiles
ns <- c(A=2, B=1, C=0.25)*1000
xx <- lapply(ns, FUN=function(n) { seq(from=1, to=max(ns), length.out=n) })
zz <- mapply(seq_along(ns), ns, FUN=function(z,n) rep(z,n))

yy <- list(
  A = rnorm(ns["A"], mean=0, sd=0.5),
  B = rnorm(ns["B"], mean=5, sd=0.4),
  C = rnorm(ns["C"], mean=-5, sd=1.1)
)
yy <- lapply(yy, FUN=function(y) {
  n <- length(y)
  y[1:(n/2)] <- y[1:(n/2)] + 2
  y[1:(n/4)] <- y[1:(n/4)] - 4
  y
})

# Shift all tracks toward the first track
yyN <- normalizeDifferencesToAverage(yy, baseline=1)

# The baseline channel is not changed
stopifnot(identical(yy[[1]], yyN[[1]]))

# Get the estimated parameters
fit <- attr(yyN, "fit")

# Plot the tracks
layout(matrix(1:2, ncol=1))
x <- unlist(xx)
col <- unlist(zz)
y <- unlist(yy)
yN <- unlist(yyN)
plot(x, y, col=col, ylim=c(-10,10))
plot(x, yN, col=col, ylim=c(-10,10))