SeqFeatR (version 0.3.3)

assocpair: Finding pairs of alignment positions that jointly mutate

Description

Determines pairs of sequence alignment positions that mutate in a correlated fashion with respect to a consensus sequence.

Usage

assocpair(path_to_file_sequence_alignment = NULL,
    path_to_file_consensus = NULL, save_name_csv, dna = FALSE,
    significance_level = 0.05, multiple_testing_correction = "bonferroni")

Value

A csv file with every possible co-mutation below the given p-value.

Arguments

path_to_file_sequence_alignment

FASTA file with sequence alignment. See example file.

path_to_file_consensus

FASTA file with consensus sequence. See example file.

save_name_csv

name of file to which results are written in csv format.

dna

indicates whether sequences are DNA or amino acids.

significance_level

significance level for Fisher's exact test.

multiple_testing_correction

multiple testing correction applied to p-values. Input can be: "holm",
"hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

Author

Bettina Budeus

Details

For every position in the sequence alignment from the FASTA file a Fisher's exact test is applied with every other position in the sequence to check whether at both positions we have correlated mutations with respect to a given consensus sequence. Significant p-values are collected in one big table. p.adjust from stats package is used for multiple testing correction; corrected values are given as extra column in csv output.

In contrast to assocpairfeat, assocpair does not use features, but uses a consensus approach. Please be sure, that this is really what you want to use. Otherwise, use assocpairfeat or assoctuple instead.

See Also

visualizepairfeat, assocpairfeat, assoctuple

Examples

#Input files
if (FALSE) {
fasta_input <- system.file("extdata", "Example_aa.fasta", package="SeqFeatR")
consensus_input <- system.file("extdata", "Example_Consensus_aa.fasta", package="SeqFeatR")
	
#Usage
assocpair(
	path_to_file_sequence_alignment=fasta_input,
	path_to_file_consensus=consensus_input,
	save_name_csv="assocpair_results.csv",
	dna=FALSE,
	significance_level=0.05,
	multiple_testing_correction="bonferroni")
}