calculateScaleSpace {Scale4C} | R Documentation

## Calculate scale-space representation for given 4C-seq data

### Description

Scale4C uses Gauss kernels of increasing (square) sigma to smooth the original 4C-seq signal. The resulting data is stored in a 2D matrix (position x sigma).

### Usage

    calculateScaleSpace(data, maxSQSigma = 5000)

### Arguments

`data` | Scale-space object for the 4C-seq data
`maxSQSigma` | Maximum square sigma used to calculate the scale space

### Details

The central idea of the scale-space transformation is to smooth the original signal with increasing strength, identify inflection points, track those inflection points throughout the different smoothing layers, and find singularities in those inflection point 'lines'. In case of 4C-seq data, this corresponds to smoothing the signal gradually, while making notes when features such as 'peaks' or 'valleys' disappear by merging with other features. `calculateScaleSpace` smoothes the original signal up to a provided smoothing factor square sigma (Gauss kernel).

### Value

A `SummarizedExperiment` that contains in its first assay the scale space representation for a `Scale4C` object

### Author(s)

Carolin Walter

### Examples

        # read prepared example data
        data(liverData)
        # use small maxSQSigma for a fast example
        scaleSpace(liverData)<-calculateScaleSpace(liverData, maxSQSigma = 10)
        head(t(assay(scaleSpace(liverData), 1))[,1:20])

[Package Scale4C version 1.8.0 Index]