| FBN.valueCenter {FBN} | R Documentation |
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.
FBN.valueCenter(inputData, normalizingValue, nominalValueCN,
logScale)
FBN.valueCenter(inputData = NULL, normalizingValue = NULL,
nominalValueCN = 2, logScale = FALSE)
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). |
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.
Returns a vector containing the normalized values of the inputData
Adrian Andronache adi.andronache@gmail.com
Luca Agnelli luca.agnelli@gmail.com
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)