miAA: Compute the mutual information among individual amino...

In CorMut: Detect the correlated mutations based on selection pressure

Description

Compute the mutual information among individual amino mutations, the amino mutations in a specific position will be considered respectively.

Usage

miAA(seq_formated, kaks = TRUE, lod_cut = 2, setPosition = c(),fdr=FALSE)

Arguments

seq_formated	Formated multiple alignment sequence. i.e. the result after the treatment of DataFormatCorMut.
kaks	A logical variable to indicate whether kaks is turn on or off, if kaks is TRUE, conditional kaks will be computed only among positive seelction sites, or if kaks is FALSE, conditional kaks will be computed only among all sites of sequence.
lod_cut	The LOD confidence score cutoff, the default value is 2. If lod is larger than 2, it means the positive selection of individual site or the conditional selection pressure among sites are significant.
setPosition	The positions of sequence to compute. setPosition should be a vector of interger type.
fdr	Decide whether use FDR procedure to control the p value of the computation.The default value is False.

Value

A object of MI class will be returned. miAA includes two slots of matrix:mi and p.value, which indicate the mutual information and p value respectively.

Author(s)

Zhenpeng Li

References

Cover, T. M., Thomas, J. A. & Wiley, J. Elements of information theory. 6, (Wiley Online Library: 1991).

See Also

filterSites,miCodon

Examples

examplefile=system.file("extdata","PI_treatment.aln",package="CorMut")
example=seqFormat(examplefile)
result=miAA(example)

CorMut documentation built on April 28, 2020, 7:09 p.m.