# entropy: Calculate LowMACA statistics In LowMACA: LowMACA - Low frequency Mutation Analysis via Consensus Alignment

## Description

`entropy` is a method for objects of class LowMACA. It calculates global entropy score of the mutation profile of the alignment and a test for every position in the consensus comparing the number of observed mutations against a weigthed random uniform distribution.

## Usage

 `1` ```entropy(object, bw = NULL , conservation=0.1) ```

## Arguments

 `object` an object of class LowMACA `bw` a character string or a numeric positive value representing the desired bandwith to launch the function density for the uniform distribution. 0 will not launch density (every position is not aggregated to the surrounded ones) , 'auto' will let the simulation decide according to the Silverman's rule of thumb and every other number is a user defined bandwidth passed to the function `density`. `conservation` a number between 0 and 1. Represents the minimum level of conservation to test a mutation

## Details

The parameter `bw` overwrites the bandwidth set with `lmParams`. Therefore, if bw is set to NULL, the method entropy uses the predefined bandwidth of the LowMACA object.

## Value

`entropy` returns an object of class LowMACA updating the slot `entropy` and the slot `alignment`. The slot `entropy` becomes a list of 6 elements:

• bw the bandwidth used to calculate the null profile

• uniform a function to calculate the null profile

• absval absolute value of entrpy calculated

• log10pval p value of the entropy test in log 10

• pvalue p value of the entropy test

• conservation_thr the minimum conservation level accepted

The slot `alignment` is updated in the `df` element by adding 6 new columns

• mean a numeric vector representing the mean value of the empirical uniform function at every position in the consensus

• lTsh a numeric vector representing the limit inferior of the 95% confidence interval of the empirical uniform function at every position in the consensus

• uTsh a numeric vector representing the limit superior of the 95% confidence interval of the empirical uniform function at every position in the consensus

• profile a numeric vector representing the density of mutations at every position in the sample normalized by the number of position. In case of bandwidth 0, this vector is equal to the number of mutations divided by the total number of mutations

• pvalue a numeric vector representing the pvalue of the number of mutations found at every position against the weigthed random uniform distribution of mutations

• qvalue a numeric vector representing the corrected pvalues using FDR method. Only positions with a conservation score >= 10% are considered

## Author(s)

Stefano de Pretis , Giorgio Melloni

## References

doi:10.1186/gm563 923 Melloni et al.: DOTS-Finder: a comprehensive tool for assessing driver genes in cancer genomes. Genome Medicine 2014 6:44

Silverman, B. W. (1986) Density Estimation. London: Chapman and Hall.

`alignSequences` `lmParams` `lmEntropy`
 ```1 2 3 4``` ```#Load homeobox example and run entropy data(lmObj) lmObj <- entropy(lmObj) lmEntropy(lmObj) ```