tests/normalizeQuantileSpline.matrix.R

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

library("aroma.light")

# Simulate three samples with on average 20% missing values
N <- 10000
X <- cbind(rnorm(N, mean=3, sd=1),
           rnorm(N, mean=4, sd=2),
           rgamma(N, shape=2, rate=1))
X[sample(3*N, size=0.20*3*N)] <- NA_real_

# Plot the data
layout(matrix(c(1,0,2:5), ncol=2, byrow=TRUE))
xlim <- range(X, na.rm=TRUE)
plotDensity(X, lwd=2, xlim=xlim, main="The three original distributions")

Xn <- normalizeQuantile(X)
plotDensity(Xn, lwd=2, xlim=xlim, main="The three normalized distributions")
plotXYCurve(X, Xn, xlim=xlim, main="The three normalized distributions")

Xn2 <- normalizeQuantileSpline(X, xTarget=Xn[,1], spar=0.99)
plotDensity(Xn2, lwd=2, xlim=xlim, main="The three normalized distributions")
plotXYCurve(X, Xn2, xlim=xlim, main="The three normalized distributions")