These functions help to manipulate alignments of class phyDat.
Usage
# S3 method for class 'phyDat'
print(x, ...)
# S3 method for class 'phyDat'
cbind(..., gaps = "-", compress = TRUE)
# S3 method for class 'phyDat'
rbind(...)
# S3 method for class 'phyDat'
c(..., gaps = "-", compress = TRUE)
# S3 method for class 'phyDat'
subset(x, subset, select, site.pattern = TRUE, ...)
# S3 method for class 'phyDat'
x[i, j, ..., drop = FALSE]
# S3 method for class 'phyDat'
unique(x, incomparables = FALSE, identical = TRUE, ...)
removeUndeterminedSites(x, ...)
removeAmbiguousSites(x)
allSitePattern(n, levels = NULL, names = NULL, type = "DNA", code = 1)Arguments
- x
An object containing sequences.
- ...
further arguments passed to or from other methods.
- gaps
character for gaps (default is '-').
- compress
logical, compress data to store only site patterns.
- subset
a subset of taxa.
- select
a subset of characters.
- site.pattern
select site pattern or sites (see details).
- i, j
indices of the rows and/or columns to select or to drop. They may be numeric, logical, or character (in the same way than for standard R objects).
- drop
for compatibility with the generic (unused).
- incomparables
for compatibility with unique.
- identical
if TRUE (default) sequences have to be identical, if FALSE sequences are considered duplicates if distance between sequences is zero (happens frequently with ambiguous sites).
- n
Number of sequences.
- levels
Level attributes.
- names
Names of sequences.
- type
Type of sequences ("DNA", "AA" or "USER").
- code
The ncbi genetic code number for translation. By default the standard genetic code is used.
Details
allSitePattern generates all possible site patterns and can be useful
in simulation studies. For further details see the vignette
AdvancedFeatures.
The generic function c can be used to to combine sequences and
unique to get all unique sequences or unique haplotypes.
phyDat stores identical columns of an alignment only once and keeps an
index of the original positions. This saves memory and especially
computations as these are usually need to be done only once for each site
pattern.
In the example below the matrix x in the example has 8 columns, but column 1
and 2 and also 3 and 5 are identical. The phyDat object y has only 6
site pattern. If argument site.pattern=FALSE the indexing behaves like
on the original matrix x. site.pattern=TRUE can be useful inside
functions.
See also
DNAbin, as.DNAbin,
baseFreq, glance.phyDat, dna2codon,
read.dna, read.nexus.data
and the chapter 1 in the vignette("AdvancedFeatures",
package="phangorn") and the example of pmlMix for the use of
allSitePattern.
Author
Klaus Schliep klaus.schliep@gmail.com
Examples
data(Laurasiatherian)
class(Laurasiatherian)
#> [1] "phyDat"
Laurasiatherian
#> 47 sequences with 3179 character and 1605 different site patterns.
#> The states are a c g t
# base frequencies
baseFreq(Laurasiatherian)
#> a c g t
#> 0.3321866 0.1990791 0.2040652 0.2646691
# subsetting phyDat objects
# the first 5 sequences
subset(Laurasiatherian, subset=1:5)
#> 5 sequences with 3179 character and 1605 different site patterns.
#> The states are a c g t
# the first 5 characters
subset(Laurasiatherian, select=1:5, site.pattern = FALSE)
#> 47 sequences with 5 character and 5 different site patterns.
#> The states are a c g t
# subsetting with []
Laurasiatherian[1:5, 1:20]
#> 5 sequences with 20 character and 17 different site patterns.
#> The states are a c g t
# short for
subset(Laurasiatherian, subset=1:5, select=1:20, site.pattern = FALSE)
#> 5 sequences with 20 character and 17 different site patterns.
#> The states are a c g t
# the first 5 site patterns (often more than 5 characters)
subset(Laurasiatherian, select=1:5, site.pattern = TRUE)
#> 47 sequences with 454 character and 5 different site patterns.
#> The states are a c g t
x <- matrix(c("a", "a", "c", "g", "c", "t", "a", "g",
"a", "a", "c", "g", "c", "t", "a", "g",
"a", "a", "c", "c", "c", "t", "t", "g"), nrow=3, byrow = TRUE,
dimnames = list(c("t1", "t2", "t3"), 1:8))
(y <- phyDat(x))
#> 3 sequences with 8 character and 6 different site patterns.
#> The states are a c g t
subset(y, 1:2)
#> 2 sequences with 8 character and 6 different site patterns.
#> The states are a c g t
subset(y, 1:2, compress=TRUE)
#> 2 sequences with 8 character and 4 different site patterns.
#> The states are a c g t
subset(y, select=1:3, site.pattern = FALSE) |> as.character()
#> [,1] [,2] [,3]
#> t1 "a" "a" "c"
#> t2 "a" "a" "c"
#> t3 "a" "a" "c"
subset(y, select=1:3, site.pattern = TRUE) |> as.character()
#> [,1] [,2] [,3] [,4] [,5]
#> t1 "a" "a" "c" "c" "g"
#> t2 "a" "a" "c" "c" "g"
#> t3 "a" "a" "c" "c" "c"
y[,1:3] # same as subset(y, select=1:3, site.pattern = FALSE)
#> 3 sequences with 3 character and 2 different site patterns.
#> The states are a c g t
# Compute all possible site patterns
# for nucleotides there $4 ^ (number of tips)$ patterns
allSitePattern(5)
#> 5 sequences with 1024 character and 1024 different site patterns.
#> The states are a c g t