FBN (version 1.5.2)

FBN.valueCenter: The SNP normalization function

Description

Normalization of the raw SNP microarray values, by multiplication (on linear scale) or addition (in log scale) of all the raw SNP values with the normalization factor. The normalization factor is estimated such that it brings the normalizingValue of the raw SNP values onto the nominalValueCN.

Usage

FBN.valueCenter(inputData, normalizingValue, nominalValueCN, 
	logScale)
FBN.valueCenter(inputData = NULL, normalizingValue = NULL, 
	nominalValueCN = 2, logScale = FALSE)

Value

Returns a vector containing the normalized values of the inputData

Arguments

inputData

The vector of raw SNP values, as they come out from, e.g. Circular Binary Segmentation in DNAcopy package from Bioconductor

normalizingValue

The value representing the center of the cluster identified as having a certain CN

nominalValueCN

The nominal value representing a certain CN on which the normalizingValue has to be brought.

logScale

A logical value, specifying wether the data is on linear (FALSE) or logarithmic scale (TRUE).

Author

Adrian Andronache
Luca Agnelli

Details

The nominalValueCN is a real value representing the CN, e.g. CN\(=2\) has a nominalValueCN of 2, but all other CN\(=n\) (\(n\) != 2) will have a nominalValueCN different from \(n\). Such nominalValueCN is identified by the FBN.kmeans function.

See Also

FBN.kmeans, FBNormalization

Examples

require(stats)
require(graphics)
x = c(rnorm(1000, 1, .1), rnorm(1000, 1.5, .1))
y = FBN.valueCenter(x, normalizingValue = 1, nominalValueCN = 2, 
	logScale = FALSE)
par(mfrow = c(2, 1), new = FALSE)
h = hist(x)
par(new = TRUE)
plot(1, 0, col = 'red', xlim = c(min(h$breaks), max(h$breaks)), 
	ylim = c(0,max(h$counts)), xlab = NA, ylab = NA)
par(new = FALSE)
h = hist(y)
par(new = TRUE)
plot(2, 0, col = 'red', xlim = c(min(h$breaks), max(h$breaks)), 
	ylim = c(0,max(h$counts)), xlab = NA, ylab = NA)