binarize.BASC {BiTrinA} | R Documentation |
Binarizes real-valued data using the multiscale BASC methods.
binarize.BASC(vect, method = c("A","B"), tau = 0.01, numberOfSamples = 999, sigma = seq(0.1, 20, by=.1), na.rm=FALSE)
method |
Chooses the BASC method to use (see details), i.e. either "A" or "B". |
vect |
A real-valued vector of data to binarize. |
tau |
This parameter adjusts the sensitivity and the specificity of the statistical testing procedure that rates the quality of the binarization. Defaults to 0.01. |
numberOfSamples |
The number of samples for the bootstrap test. Defaults to 999. |
sigma |
If |
na.rm |
If set to |
The two BASC methods can be subdivided into three steps:
An initial step function is obtained by rearranging the original time series measurements in increasing order. Then, step functions with fewer discontinuities are calculated. BASC A calculates these step functions in such a way that each minimizes the Euclidean distance to the initial step function. BASC B obtains step functions from smoothened versions of the input function in a scale-space manner.
A strong discontinuity is a high jump size (derivative) in combination with a low approximation error.
Based on these estimates, data values can be excluded from further analyses.
Returns an object of class BASCResult
.
M. Hopfensitz, C. MÃ¼ssel, C. Wawra, M. Maucher, M. Kuehl, H. Neumann, and H. A. Kestler. Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9(2):487-498, 2012.).
BinarizationResult
,
BASCResult
par(mfrow=c(2,1)) result <- binarize.BASC(iris[,"Petal.Length"], method="A", tau=0.15) print(result) plot(result) result <- binarize.BASC(iris[,"Petal.Length"], method="B", tau=0.15) print(result) plot(result)