binarizeBASC: Binarization Across Multiple Scales In BiTrinA: Binarization and Trinarization of One-Dimensional Data

Description

Binarizes real-valued data using the multiscale BASC methods.

Usage

 ```1 2 3 4 5 6``` ```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.).

`BinarizationResult`, `BASCResult`
 ```1 2 3 4 5 6 7 8``` ```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) ```