exp_transform: Performs the exponential transform on mutational count data
In antoine186/convSig: Mutational Process Inference via Expectation Maximisation

Description Usage Arguments Value Important Examples

Performs the exponential transform on mutational count data

1	exp_transform(mut_obj, five = FALSE, K = 5, iter_num = 5000)

`mut_obj`	An object of class 'Shallowres' as produced by the function mut_count()
`five`	A boolean that specifies whether you want to apply a transformation based on a 5-nucleotide convolution window.
`K`	An integer that indicates the number of mutational processes you want to detect in your mutational count data via the transformation.

A Feature matrix (feat), which contains the convolution weights associated with each mutational processes. An M matrix (mat), which contains the probability for all mutation types. A P matrix (P), which contains the mutational intensity/activity of each mutational process. A LOSS variable (LOSS), which displays the LOSS value achieved by the exponential optimisation. A testing LOSS variable (test_LOSS), which displays the LOSS value achieved on the testing samples.

This tranformation will typically fail if your count input data contains too small frequencies for each mutational signature (i.e. hundreds). This situation can arise if you have only processed a small portion of the reference genome or a small mutational input file during the data processing stages.