binarizeBASC: Binarization Across Multiple Scales

In BiTrinA: Binarization and Trinarization of One-Dimensional Data

Binarization Across Multiple Scales

Description

Binarizes real-valued data using the multiscale BASC methods.

Usage

binarize.BASC(vect, 
              method = c("A","B"), 
              tau = 0.01, 
              numberOfSamples = 999, 
              sigma = seq(0.1, 20, by=.1),
              na.rm=FALSE)

Arguments

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 method="B", this specifies a vector of different sigma values for the convolutions with the Bessel function. Ignored for method="A".
na.rm	If set to TRUE, NA values are removed from the input. Otherwise, binarization will fail in the presence of NA values.

Details

The two BASC methods can be subdivided into three steps:

Compute a series of step functions:

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.

Find strongest discontinuity in each step function:

A strong discontinuity is a high jump size (derivative) in combination with a low approximation error.

Estimate location and variation of the strongest discontinuities:

Based on these estimates, data values can be excluded from further analyses.

Value

Returns an object of class BASCResult.

References

M. Hopfensitz, C. Müssel, 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.).

See Also

BinarizationResult, BASCResult

Examples

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)

BiTrinA documentation built on Oct. 2, 2023, 5:07 p.m.