beadarrayMSV (version 1.0.3)

transformChannels: Signal transformation

Description

Signal and standard error estimates are subjected to log or nth-root transformation

Usage

transformChannels(X, Y = NULL, normOpts = setNormOptions())
transformSEs(X, se.X, normOpts = setNormOptions())

Arguments

X

Matrix of (mean bead type) intensity data for red or green channel

Y

Matrix of intensity data for the alternate channel

normOpts

List specifying pre-processing settings. See setNormOptions

se.X

Matrix holding the standard error of the mean bead intensities

Value

• The output from transformChannels or transformSEs is a list containg at least two of the following items
• X
  Transformed X
• Y
  Transformed Y
• SE
  Estimated standard errors of transformed signal mean
• lstr
  Character string with description of transformation

Details

The channel(s) are transformed according to the specifications in normOpts.

As signal intensities are estimated as (robust) means within bead-types on an array, the estimated standard errors are in fact standard errors of means. The standard errors of means cannot be log or nth-root transformed in order to get the standard error of the transformed signal mean. To avoid loading the bead-level data into the workspace, the function transformSEs estimates the transformed standard errors using a first order Taylor-expansion around the mean. In our experience, these estimates are accurate except for very low signal intensities (below noise level).

See Also

preprocessBeadSet

Examples

#Simulate intensity data
X <- matrix(exp(rnorm(4000))*100,nrow=1000,ncol=4,
    dimnames=list(1:1000,paste('Array',1:4)))
Y <- matrix(exp(rnorm(4000))*70,nrow=1000,ncol=4,
    dimnames=list(1:1000,paste('Array',1:4)))

#Transform signal
normOpts <- setNormOptions(offset=0)
trChannel <- transformChannels(X,Y,normOpts)

#Plot one array before and after transformation
dev.new()
plot(X[,1],Y[,1],pch='o',main='Raw data')
dev.new()
plot(trChannel$X[,1],trChannel$Y[,1],pch='o',
    main=paste(trChannel$lstr,'transformed data'))

#Simulate a single bead type represented by 12 beads
beadLevelX <- rnorm(12,mean=800,sd=10) 
sd.X <- sd(beadLevelX)
X <- mean(beadLevelX)
se.X <- sd.X/sqrt(length(beadLevelX))

#Transformed signal (4th-root)
transfX <- mean(beadLevelX^(1/4))   #true value
print(transfX)
transfX.1 <- X^(1/4)                #good approximation
print(transfX.1)

#Transformed standard error (4th-root)
transfSE <- sd(beadLevelX^(1/4))/sqrt(length(beadLevelX))  #true value
print(transfSE)
transfSE.1 <- se.X^(1/4)                                   #bad approximation
print(transfSE.1)
transfSE.2 <- transformSEs(X,se.X,normOpts=normOpts)       #good approximation
print(transfSE.2$SE)