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

Description Usage Arguments Details Value Author(s) References See Also Examples

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.

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

`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

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.

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

Stefano de Pretis , Giorgio Melloni

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

#Load homeobox example and run entropy
data(lmObj)
lmObj <- entropy(lmObj)
lmEntropy(lmObj)

Checking if clustalo is in the PATH...
Checking perl installation...
Checking perl modules XML::Simple and LWP...
sh: 1: /usr/bin/perl: Permission denied
sh: 1: /usr/bin/perl: Permission denied
Warning messages:
1: In .ClustalChecks(ClustalCommand = "clustalo") :
  Clustal Omega is not in the PATH:
You can either change clustalo command using lmParams function or use the web service. See ?setup
2: In system(paste(perl, "-MXML::Simple -e 1"), intern = TRUE) :
  running command '/usr/bin/perl -MXML::Simple -e 1' had status 126
3: In .PerlModuleChecks(stop = FALSE, perl = "perl") :
  XML::Simple module for perl is not installed. 
            If you don't want to install a local clustal omega and use the web service, XML::Simple is required

4: In system(paste(perl, "-MLWP -e 1"), intern = TRUE) :
  running command '/usr/bin/perl -MLWP -e 1' had status 126
5: In .PerlModuleChecks(stop = FALSE, perl = "perl") :
  LWP module for perl is not installed. 
            If you don't want to install a local clustal omega and use the web service, LWP is required

Making uniform model...
Assigned bandwidth: 0
$bw
[1] 0

$uniform
function (nmut) 
list(mean = model.mean(nmut), sd = model.sd(nmut), max = model.max(nmut))
<bytecode: 0x55e1769c0730>
<environment: 0x55e1769bd658>

$absval
[1] 3.599257

$log10pval
[1] -12.4788

$pvalue
[1] 3.320437e-13

$conservation_thr
[1] 0.1