Description Usage Arguments Details Value Note Author(s) See Also Examples
Determines pairs of sequence alignment positions that mutate in a correlated fashion with respect to a consensus sequence.
1 2 3 |
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", |
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.
A csv file with every possible co-mutation below the given p-value.
For graphical output use:
visualizepair
.
Bettina Budeus
visualizepairfeat
, assocpairfeat
, assoctuple
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | #Input files
## Not run:
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")
## End(Not run)
|
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