, Thibaut Jombart
[aut, cph] , Zhian N.
Kamvar [aut, cph] ,
Brian Knaus [aut, cph]
, Klaus Schliep [aut,
cph] , Alastair Potts
[aut, cph] , David
Winter [aut, cph] | Title: | Population and Evolutionary Genetics Analysis System |
|---|---|
| Description: | Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, including from VCF files, and for the analysis of population nucleotide sequences and micro-satellites including coalescent analyses, linkage disequilibrium, population structure (Fst, Amova) and equilibrium (HWE), haplotype networks, minimum spanning tree and network, and median-joining networks. |
| Authors: | Emmanuel Paradis [aut, cre, cph]
, Thibaut Jombart
[aut, cph] , Zhian N.
Kamvar [aut, cph] ,
Brian Knaus [aut, cph]
, Klaus Schliep [aut,
cph] , Alastair Potts
[aut, cph] , David
Winter [aut, cph] |
| Maintainer: | Emmanuel Paradis <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.3 |
| Built: | 2024-11-08 06:31:33 UTC |
| Source: | CRAN |
pegas provides functions for the analysis of allelic data and of haplotype data from DNA sequences. It requires and complements two other R-packages: ape and adegenet.
The complete list of functions can be displayed with
library(help = pegas).
More information on pegas can be found at https://emmanuelparadis.github.io/pegas.html.
Emmanuel Paradis, Thibaut Jombart, Zhian N. Kamvar, Brian Knaus, Alastair Potts, Klaus Schliep, David Winter
Maintainer: Emmanuel Paradis
This function compares two haplotype networks and returns either
TRUE or a description of the differences.
## S3 method for class 'haploNet' all.equal(target, current, use.steps = TRUE, ...)## S3 method for class 'haploNet' all.equal(target, current, use.steps = TRUE, ...)
target, current
|
two objects of class |
use.steps |
a logical value: whether to consider the number of steps (or length) in each link. |
... |
(unused). |
This function should return TRUE if the two networks are
identical even if the links are ordered differently. In all other
situations, a vector of character strings describing the differences
is returned.
As usual with the all.equal function, this cannot
be used directly to return a TRUE/FALSE value (see
examples).
either a logical value (TRUE), or a vector of mode character.
Emmanuel Paradis
data(woodmouse) d <- dist.dna(woodmouse, "n") nt1 <- mst(d) nt2 <- msn(d) (comp <- all.equal(nt1, nt2)) # clearly different ## how to use all.equal to return TRUE/FALSE: isTRUE(comp) # FALSEdata(woodmouse) d <- dist.dna(woodmouse, "n") nt1 <- mst(d) nt2 <- msn(d) (comp <- all.equal(nt1, nt2)) # clearly different ## how to use all.equal to return TRUE/FALSE: isTRUE(comp) # FALSE
These functions transform a matrix of alleles into an object of class
"loci", or the reverse operation.
alleles2loci(x, ploidy = 2, rownames = NULL, population = NULL, phased = FALSE) loci2alleles(x)alleles2loci(x, ploidy = 2, rownames = NULL, population = NULL, phased = FALSE) loci2alleles(x)
x |
a matrix or a data frame where each column is an allele, or
an object of class |
ploidy |
an integer specifying the level of ploidy. |
rownames |
an integer giving the column number to be used as rownames of the output. |
population |
an integer giving the column number to be used as population (if any). |
phased |
a logical specifying whether the genotypes should be output as phased. By default, they are unphased. |
Genetic data matrices are often arranged with one allele
in each column of the matrix (particularly for micro-satellites), so
that the number of columns is equal to the number of loci times the
level of ploidy. alleles2loci transforms such matrices into a
"loci" object.
If the rownames of the input matrix are already set, they are used in the output. Alternatively, it is possible to specify which column to use as rownames (this column will be deleted before creating the genotypes).
If the input matrix has colnames, then the names of the first column of each genotype is used as names of the output loci (see examples).
loci2alleles checks that all individuals have the ploidy for a
given locus (if not an error occurs), but ploidy can vary among loci.
an object of class "loci" or a matrix.
Emmanuel Paradis
The vignette “ReadingFiles” explains how to read such a data set from Dryad (https://datadryad.org/stash).
x <- matrix(c("A", "A", "A", "a"), 2) colnames(x) <- c("Loc1", NA) y <- alleles2loci(x) print(y, details = TRUE) loci2alleles(y)x <- matrix(c("A", "A", "A", "a"), 2) colnames(x) <- c("Loc1", NA) y <- alleles2loci(x) print(y, details = TRUE) loci2alleles(y)
These functions analyse allelic richness.
allelicrichness(x, pop = NULL, method = "extrapolation", min.n = NULL) rarefactionplot(x, maxn = nrow(x), type = "l", xlab = "Sample size", ylab = "Expected number of alleles", plot = TRUE, ...) rhost(x, pop = NULL, method = "extrapolation")allelicrichness(x, pop = NULL, method = "extrapolation", min.n = NULL) rarefactionplot(x, maxn = nrow(x), type = "l", xlab = "Sample size", ylab = "Expected number of alleles", plot = TRUE, ...) rhost(x, pop = NULL, method = "extrapolation")
x |
an object of class |
pop |
a vector or factor giving the population assignment of each
row of |
method |
a character string which should be one of “extrapolation”, “rarefaction”, “raw” or an unambiguous abbreviation of these. |
min.n |
the value of |
maxn |
the largest sample size used to calculate the rarefaction curve. |
type, xlab, ylab
|
arguments passed to |
plot |
a logical value specifying whether to do the rarefaction
plot ( |
... |
arguments passed to and from methods. |
allelicrichness computes for each locus in x the
estimated allelic richness. Three methods are available: the
extrapolation method (Foulley and Ollivier 2006), the rarefaction
method (Hurlbert 1971), and the raw numbers of alleles.
rarefactionplot computes the rarefaction curves of the number
of alleles with respect to sample size using Hurlbert's (1971)
method. A plot is made by default.
allelicrichness returns a numeric matrix.
rarefactionplot returns invisibly a list of matrices with the
coordinates of the rarefaction plots for each locus.
rhost returns a numeric vector.
Emmanuel Paradis
El Mousadik, A. and Petit, R. J. (1996) High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L. Skeels)] endemic to Morocco. Theoretical and Applied Genetics, 92, 832–836.
Foulley, J. L. and Ollivier, L. (2006) Estimating allelic richness and its diversity. Livestock Science, 101, 150–158.
Hurlbert, S. H. (1971) The nonconcept of species diversity: a critique and alternative parameters. Ecology, 52, 577–586.
data(jaguar) rarefactionplot(jaguar) allelicrichness(jaguar) rhost(jaguar)data(jaguar) rarefactionplot(jaguar) allelicrichness(jaguar) rhost(jaguar)
This function performs a hierarchical analysis of molecular variance as described in Excoffier et al. (1992). This implementation accepts any number of hierarchical levels.
amova(formula, data = NULL, nperm = 1000, is.squared = FALSE) ## S3 method for class 'amova' print(x, ...) getPhi(sigma2) write.pegas.amova(x, file = "")amova(formula, data = NULL, nperm = 1000, is.squared = FALSE) ## S3 method for class 'amova' print(x, ...) getPhi(sigma2) write.pegas.amova(x, file = "")
formula |
a formula giving the AMOVA model to be fitted with the
distance matrix on the left-hand side of the |
data |
an optional data frame where to find the hierarchical levels; by default they are searched for in the user's workspace. |
nperm |
the number of permutations for the tests of hypotheses (1000 by default). Set this argument to 0 to skip the tests and simply estimate the variance components. |
is.squared |
a logical specifying whether the distance matrix has already been squared. |
x |
an object of class |
sigma2 |
a named vector of variance components. |
file |
a file name. |
... |
unused (here for compatibility. |
The formula must be of the form d ~ A/B/... where d is a
distance object, and A, B, etc, are the hierarchical
levels from the highest to the lowest one. Any number of levels is
accepted, so specifying d ~ A will simply test for population
differentiation.
It is assumed that the rows of the distance matrix are in the same order than the hierarchical levels (which may be checked by the user).
The function getPhi() is a convenience function for extracting a
table of hierarchical Phi-statistics for reporting. This will be an N+1
by N matrix where N is the number of hierarchcial levels and GLOBAL is
always the first row of the matrix. The matrix can read as COLUMN in ROW.
If the variance components passed to getPhi() are not named, they will be reported as "level 1", "level 2", etc.
An object of class "amova" which is a list with a table of sums
of square deviations (SSD), mean square deviations (MSD), and the
number of degrees of freedom, and a vector of variance components.
If there are more than three levels, approximate formulae are used to estimate the variance components.
If there is an error message like this:
Error in FUN(X[[1L]], ...) : 'bin' must be numeric or a factor
it may be that the factors you use in the formula were not read
correctly. You may convert them with the function factor, or,
before reading your data files, do this command (in case this option
was modified):
options(stringsAsFactors = TRUE)
Emmanuel Paradis, Zhian N. Kamvar, and Brian Knaus
Excoffier, L., Smouse, P. E. and Quattro, J. M. (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479–491.
amova in ade4 for an implementation of the original
Excoffier et al.'s model; adonis in vegan for a general
(multivariate) implementation of an ANOVA framework with distances.
### All examples below have 'nperm = 100' for faster execution times. ### The default 'nperm = 1000' is recommended. require(ape) data(woodmouse) d <- dist.dna(woodmouse) g <- factor(c(rep("A", 7), rep("B", 8))) p <- factor(c(rep(1, 3), rep(2, 4), rep(3, 4), rep(4, 4))) (d_gp <- amova(d ~ g/p, nperm = 100)) # 2 levels sig2 <- setNames(d_gp$varcomp$sigma2, rownames(d_gp$varcomp)) getPhi(sig2) # Phi table amova(d ~ p, nperm = 100) # 1 level amova(d ~ g, nperm = 100) ## 3 levels (quite slow): ## Not run: pop <- gl(64, 5, labels = paste0("pop", 1:64)) region <- gl(16, 20, labels = paste0("region", 1:16)) conti <- gl(4, 80, labels = paste0("conti", 1:4)) dd <- as.dist(matrix(runif(320^2), 320)) (dd_crp <- amova(dd ~ conti/region/pop, nperm = 100)) sig2 <- setNames(dd_crp$varcomp$sigma2, rownames(dd_crp$varcomp)) getPhi(sig2) ## End(Not run)### All examples below have 'nperm = 100' for faster execution times. ### The default 'nperm = 1000' is recommended. require(ape) data(woodmouse) d <- dist.dna(woodmouse) g <- factor(c(rep("A", 7), rep("B", 8))) p <- factor(c(rep(1, 3), rep(2, 4), rep(3, 4), rep(4, 4))) (d_gp <- amova(d ~ g/p, nperm = 100)) # 2 levels sig2 <- setNames(d_gp$varcomp$sigma2, rownames(d_gp$varcomp)) getPhi(sig2) # Phi table amova(d ~ p, nperm = 100) # 1 level amova(d ~ g, nperm = 100) ## 3 levels (quite slow): ## Not run: pop <- gl(64, 5, labels = paste0("pop", 1:64)) region <- gl(16, 20, labels = paste0("region", 1:16)) conti <- gl(4, 80, labels = paste0("conti", 1:4)) dd <- as.dist(matrix(runif(320^2), 320)) (dd_crp <- amova(dd ~ conti/region/pop, nperm = 100)) sig2 <- setNames(dd_crp$varcomp$sigma2, rownames(dd_crp$varcomp)) getPhi(sig2) ## End(Not run)
These functions do conversion among different allelic data classes.
as.loci(x, ...) ## S3 method for class 'genind' as.loci(x, ...) genind2loci(x) ## S3 method for class 'data.frame' as.loci(x, allele.sep = "/|", col.pop = NULL, col.loci = NULL, ...) loci2genind(x, ploidy = 2, na.alleles = c("0", "."), unphase = TRUE) ## S3 method for class 'factor' as.loci(x, allele.sep = "/|", ...) ## S3 method for class 'character' as.loci(x, allele.sep = "/|", ...) loci2SnpMatrix(x, checkSNP = TRUE)as.loci(x, ...) ## S3 method for class 'genind' as.loci(x, ...) genind2loci(x) ## S3 method for class 'data.frame' as.loci(x, allele.sep = "/|", col.pop = NULL, col.loci = NULL, ...) loci2genind(x, ploidy = 2, na.alleles = c("0", "."), unphase = TRUE) ## S3 method for class 'factor' as.loci(x, allele.sep = "/|", ...) ## S3 method for class 'character' as.loci(x, allele.sep = "/|", ...) loci2SnpMatrix(x, checkSNP = TRUE)
x |
an object of class |
allele.sep |
the character(s) separating the alleles for each locus in the data file (a forward slash by default). |
col.pop |
specifies whether one of the column of the data file
identifies the population; default |
col.loci |
a vector of integers or of characters specifying the indices or the names of the columns that are loci. By default, all columns are taken as loci except the one labelled "population", if present or specified. |
ploidy |
the ploidy level (see details). |
na.alleles |
a vector of charater strings giving the alleles to be treated as missing data. |
unphase |
a logical value; by default, the genotypes are unphased before conversion (this should not be changed). |
... |
further arguments to be passed to or from other methods. |
checkSNP |
a logical value. If you are sure that all data in the
|
The main objectives of these functions is to provide easy conversion
between the data structures of adegenet and pegas, so both
packages can be used together smoothly. In addition, it is possible to
create a "loci" object directly from a data frame, a vector, or
a factor.
genind2loci(x) and as.loci(x) are the same if x
is of class "genind".
The ploidy level specified in loci2genind can be a vector in
which case it should be of length equal to the number of individuals
and will be interpreted as giving the ploidy of each of them. Note
that this is different from getPloidy which returns the
ploidy level of each locus.
An object of class c("loci", "data.frame") for as.loci
and genind2loci; an object of class "genind" for
loci2genind; an object of class "SnpMatrix" for
loci2SnpMatrix.
Emmanuel Paradis
read.loci, genind,
df2genind for converting data frames to
"genind", alleles2loci
x <- c("A-A", "A-a", "a-a") as.loci(x, allele.sep = "-") ## Not run: require(adegenet) data(nancycats) x <- as.loci(nancycats) y <- loci2genind(x) # back to "genind" identical(nancycats@tab, y@tab) identical(nancycats@pop, y@pop) ## End(Not run)x <- c("A-A", "A-a", "a-a") as.loci(x, allele.sep = "-") ## Not run: require(adegenet) data(nancycats) x <- as.loci(nancycats) y <- loci2genind(x) # back to "genind" identical(nancycats@tab, y@tab) identical(nancycats@pop, y@pop) ## End(Not run)
These functions combine objects of class "loci" by binding
their rows or their columns.
## S3 method for class 'loci' rbind(...) ## S3 method for class 'loci' cbind(...)## S3 method for class 'loci' rbind(...) ## S3 method for class 'loci' cbind(...)
... |
some object(s) of class |
These two methods call [rc]bind.data.frame and take care to
respect the attribute “locicol” of the returned object.
You can pass a data frame in the ..., but then you should
bypass the generic by calling cbind.loci directly. Do not try
to pass a vector: this will mess the “locicol” attribute. Instead,
make a data frame with this vector (see examples).
An object of class "loci".
Emmanuel Paradis
[.loci
a <- as.loci(data.frame(x = "A/a", y = 1), col.loci = 1) b <- as.loci(data.frame(y = 2, x = "A/A"), col.loci = 2) ## rbind.loci reorders the columns if necessary: str(rbind(a, b)) ## cbind sets "locicol" correctly: str(cbind(a, b)) str(cbind(b, a)) ## Unexpected result... str(cbind(a, data.frame(z = 10))) ## ... bypass the generic: str(pegas:::cbind.loci(a, data.frame(z = 10))) ## ... or much better: a$z <- 10 ## Here "locicol" is not correct... str(pegas:::cbind.loci(z = 10, a)) ## ... instead str(pegas:::cbind.loci(data.frame(z = 10), a))a <- as.loci(data.frame(x = "A/a", y = 1), col.loci = 1) b <- as.loci(data.frame(y = 2, x = "A/A"), col.loci = 2) ## rbind.loci reorders the columns if necessary: str(rbind(a, b)) ## cbind sets "locicol" correctly: str(cbind(a, b)) str(cbind(b, a)) ## Unexpected result... str(cbind(a, data.frame(z = 10))) ## ... bypass the generic: str(pegas:::cbind.loci(a, data.frame(z = 10))) ## ... or much better: a$z <- 10 ## Here "locicol" is not correct... str(pegas:::cbind.loci(z = 10, a)) ## ... instead str(pegas:::cbind.loci(data.frame(z = 10), a))
This is an implementation of the generic by
function which applies a function to some data for a each level of a
categorical factor.
## S3 method for class 'loci' by(data, INDICES = data$population, FUN = NULL, ..., simplify = TRUE)## S3 method for class 'loci' by(data, INDICES = data$population, FUN = NULL, ..., simplify = TRUE)
data |
an object of class |
INDICES |
a vector of the same length as the number of rows in |
FUN |
a function |
... |
(currently unused). |
simplify |
(currently unused). |
The default FUN = NULL calculates allele frequencies for each
population in data.
a list by default indexed by locus.
Emmanuel Paradis
data(jaguar) by(jaguar) by(na.omit(jaguar))data(jaguar) by(jaguar) by(na.omit(jaguar))
This function calculates the cophenetic distance on a network. The output can be used to find nodes with short distances to most nodes.
## S3 method for class 'haploNet' cophenetic(x)## S3 method for class 'haploNet' cophenetic(x)
x |
an object of class |
The results of the function are likely to be approximate in most cases with reticulations in the network. In the case of MSTs, the results are exact.
a numeric matrix with colnames and rownames set to the labels of the network nodes.
Emmanuel Paradis
cophenetic.phylo in ape,
cophenetic for the generic function
example(mst) coph <- cophenetic(r) rowSums(coph)example(mst) coph <- cophenetic(r) rowSums(coph)
This function compares two haplotypes and returns a summary of the differences.
diffHaplo(h, a = 1, b = 2, strict = FALSE, trailingGapsAsN = TRUE)diffHaplo(h, a = 1, b = 2, strict = FALSE, trailingGapsAsN = TRUE)
h |
an object of class |
a, b
|
two integers (or character strings) giving the indices (or labels) of the two haplotypes to be compared. |
strict |
a logical value; if |
trailingGapsAsN |
a logical value; if |
The options strict and trailingGapsAsN are passed to
seg.sites.
a data frame with three columns named pos (position of the
differences) and the labels of the two haplotypes compared.
Emmanuel Paradis
data(woodmouse) h <- haplotype(woodmouse) diffHaplo(h) # compares the 1st and 2nd haplotypes diffHaplo(h, 1, 3) diffHaplo(h, "I", "III") # same than above but using labelsdata(woodmouse) h <- haplotype(woodmouse) diffHaplo(h) # compares the 1st and 2nd haplotypes diffHaplo(h, 1, 3) diffHaplo(h, "I", "III") # same than above but using labels
This function computes the allelic sharing distance (ASD) for diploid genotypes.
dist.asd(x, scaled = TRUE, pairwise.deletion = FALSE)dist.asd(x, scaled = TRUE, pairwise.deletion = FALSE)
x |
an object of class |
scaled |
a logical value specifying whether the distances should be scaled by the number of loci. |
pairwise.deletion |
a logical value: whether to check for missing values for each pairwise comparison (see details). |
The ASD between two diploid genotypes is (Gao and Martin, 2009):
where is the number loci, is the value for the
th locus: 0 if both genotypes are identical, 1 if they have one
allele in common, or 2 if they have no allele in common.
dist.asd works for all diploid genotypes (phased or unphased,
with two alleles or more). Note that the required conditions are not
checked by the present function: see the functions below.
The pairwise deletion is done with respect to missing values coded as
NA, not on the ‘null alleles’ (‘0’ or ‘.’). You
may need to use the function nullAlleles2NA first if
your data has genotypes with null alleles that you want to treat as
missing values.
an object of class "dist".
Emmanuel Paradis
Gao, X. and Martin, E. R. (2009) Using allele sharing distance for detecting human population stratification. Human Hederity, 68, 182–191.
is.snp, is.phased, getPloidy,
nullAlleles2NA
data(jaguar) ## ASD for micro-satellites: d <- dist.asd(jaguar) co <- rainbow(nlevels(jaguar$pop)) plot(nj(d), "u", tip.color = co[jaguar$pop], font = 2, lab4 = "a") legend("topleft", legend = levels(jaguar$pop), text.col = co, text.font = 2)data(jaguar) ## ASD for micro-satellites: d <- dist.asd(jaguar) co <- rainbow(nlevels(jaguar$pop)) plot(nj(d), "u", tip.color = co[jaguar$pop], font = 2, lab4 = "a") legend("topleft", legend = levels(jaguar$pop), text.col = co, text.font = 2)
This function implements a general purpose Hamming distance.
dist.hamming(x)dist.hamming(x)
x |
a matrix or a data frame. |
This function should work for a wide range of data types. A typical
usage would be with an object of class c("haplotype",
"character").
For objects of class c("haplotype", "DNAbin"), it is better to
use dist.dna(x, "n") to compute the Hamming distances.
an object of class "dist".
Emmanuel Paradis
haplotype, dist.haplotype.loci
This allows to edit a data frame of class "loci" with R's
spreadsheet-like data editor.
## S3 method for class 'loci' edit(name, edit.row.names = TRUE, ...)## S3 method for class 'loci' edit(name, edit.row.names = TRUE, ...)
name |
an object of class |
edit.row.names |
a logical specifying to allow editing the
rownames, |
... |
further arguments to be passed to or from other methods. |
This ‘method’ of the generic edit respects the class and the
attribute "locicol" of the allelic data frame.
A data frame with class c("loci", "data.frame").
Emmanuel Paradis
These functions compute the F-statistics developed by Patterson et al. (2012).
F2(x, allele.freq = NULL, population = NULL, check.data = TRUE, pops = NULL, jackknife.block.size = 10, B = 1e4) F3(x, allele.freq = NULL, population = NULL, check.data = TRUE, pops = NULL, jackknife.block.size = 10, B = 1e4) F4(x, allele.freq = NULL, population = NULL, check.data = TRUE, pops = NULL, jackknife.block.size = 10, B = 1e4)F2(x, allele.freq = NULL, population = NULL, check.data = TRUE, pops = NULL, jackknife.block.size = 10, B = 1e4) F3(x, allele.freq = NULL, population = NULL, check.data = TRUE, pops = NULL, jackknife.block.size = 10, B = 1e4) F4(x, allele.freq = NULL, population = NULL, check.data = TRUE, pops = NULL, jackknife.block.size = 10, B = 1e4)
x |
an object of class |
allele.freq |
alternatively, a list of allele (absolute)
frequencies as output by |
population |
a column name or number giving which column of
|
check.data |
if |
pops |
a vector giving two, three, or four population names
depending on the function. The order of these names is important
(see Patterson et al. 2012). By default, the populations in
|
jackknife.block.size |
the size of the block used in the jackknife to assess the significance of the F-statistic (this should be around one thousandth of the number of loci, or not less than 10. |
B |
the number of replications of the bootstrap used to assess the significance of the F-statistic. |
These functions are provisional versions.
It is much better to compute the allele frequencies, and then use
allele.freq with different combinations of pops.
A vector with names.
Emmanuel Paradis
Patterson, N., Moorjani, P., Luo, Y., Mallick, S., Rohland, N., Zhan, Y., Genschoreck, T., Webster, T. and Reich, D. (2012) Ancient admixture in human history. Genetics, 192, 1065–1093.
by.loci, Fst, the package
admixturegraph that can draw graphs from the output of this
function.
Fst computes the , and
for each locus in the data. Rst computes the
for microsatellites.
Fst(x, pop = NULL, quiet = TRUE, na.alleles = "") Rst(x, pop = NULL, quiet = TRUE, na.alleles = "")Fst(x, pop = NULL, quiet = TRUE, na.alleles = "") Rst(x, pop = NULL, quiet = TRUE, na.alleles = "")
x |
an object of class |
pop |
a vector or factor giving the population assignment of each
row of |
quiet |
a logical value: should calculations be quiet? |
na.alleles |
by default, only genotypes coded as NA are considered as missing data. This option is to specify if some alleles code for missing data. |
Fst uses the formulae in Weir and Cockerham (1984) for each
allele, and then averaged within each locus over the different alleles
as suggested by these authors.
Rst uses the formulae in Slatkin (1995).
A matrix with genes (loci) as rows and the three F-statistics as columns.
Emmanuel Paradis
Slatkin, M. (1995) A measure of population subdivision based on microsatellite allele frequencies. Genetics, 139, 457–462.
Weir, B. S. and Cockerham, C. C. (1984) Estimating F-statistics for the analysis of population structure. Evolution, 38, 1358–1370.
Weir, B. S. and Hill, W. G. (2002) Estimating F-statistics. Annual Review of Genetics, 36, 721–750.
fstat in package hierfstat; package dirmult
on CRAN that implements various estimators of the
Dirichlet-multinomial distribution, including maximum likekihood and
the moments estimator of Weir and Hill (2002); Fst in
Biodem that caculates from a “kinship
matrix”.
data(jaguar) Fst(jaguar) Rst(jaguar) ## no Fst but Fit and Fis in case of single population: jaguar_corridor <- jaguar[jaguar$population == "Green Corridor", ] Fst(jaguar_corridor)data(jaguar) Fst(jaguar) Rst(jaguar) ## no Fst but Fit and Fis in case of single population: jaguar_corridor <- jaguar[jaguar$population == "Green Corridor", ] Fst(jaguar_corridor)
This function calculates geodesic (or great-circle) distances between pairs of points with their longitudes and latitudes given in (decimal) degrees.
geod(lon, lat = NULL, R = 6371)geod(lon, lat = NULL, R = 6371)
lon |
either a vector of numeric values with the longitudes in
degrees, or, if |
lat |
a vector with the latitudes. |
R |
the mean radius of the Earth (see details). |
The default value of R is the mean radius of the Earth which is
slightly smaller than the radius at the equator (6378.1 km).
a numeric symmetric matrix with the distances between pairs of points in kilometres.
Emmanuel Paradis
https://en.wikipedia.org/wiki/Great-circle_distance
https://en.wikipedia.org/wiki/Earth
## the distance between 0N 0E and 0N 180E... geod(c(0, 180), c(0, 0)) # ~ 20015.09 km ## ... the same using the radius of the Earth at the equator: geod(c(0, 180), c(0, 0), 6378.1) # ~ 20037.39 km ## The same comparison for two points 5 degrees apart: geod(c(0, 5), c(0, 0)) # ~ 555.9746 km geod(c(0, 5), c(0, 0), 6378.1) # ~ 556.5942 km## the distance between 0N 0E and 0N 180E... geod(c(0, 180), c(0, 0)) # ~ 20015.09 km ## ... the same using the radius of the Earth at the equator: geod(c(0, 180), c(0, 0), 6378.1) # ~ 20037.39 km ## The same comparison for two points 5 degrees apart: geod(c(0, 5), c(0, 0)) # ~ 555.9746 km geod(c(0, 5), c(0, 0), 6378.1) # ~ 556.5942 km
geoTrans transforms geographical coordinates in degrees,
minutes and seconds input as characters (or a factor) into numerical
values in degrees. geoTrans2 does the reverse operation.
geoTrans(x, degsym = NULL, minsym = "'", secsym = "\"") geoTrans2(lon, lat = NULL, degsym = NULL, minsym = "'", secsym = "\"", dropzero = FALSE, digits = 3, latex = FALSE)geoTrans(x, degsym = NULL, minsym = "'", secsym = "\"") geoTrans2(lon, lat = NULL, degsym = NULL, minsym = "'", secsym = "\"", dropzero = FALSE, digits = 3, latex = FALSE)
x |
a vector of character strings storing geographical coordinates; this can be a factor with the levels correctly set. |
degsym, minsym, secsym
|
a single character giving the symbol used for degrees, minutes and seconds, respectively. |
lon |
either a vector of numeric values with the longitudes in
degrees, or, if |
lat |
a vector with the latitudes. |
dropzero |
a logical value: if |
digits |
an integer used for rounding the number of arc-seconds. |
latex |
a logical value: if |
geoTrans should be robust to any pattern of spacing around the
values and the symbols (see examples). If the letter S, W, or O is
found is the coordinate, the returned value is negative. Note that
longitude and latitude should not be mixed in the same character
strings.
geoTrans2 can be used with cat (see
examples).
The default for degsym (NULL) is because the degree
symbol (°) is coded differently in different character encodings.
By default, the function will use the appropriate character depending
on the system and encoding used.
geoTrans returns a numeric vector with the coordinates in
degrees (eventually as decimal values). geoTrans2 returns a
character vector.
Emmanuel Paradis
coord <- c("N 43°27'30\"", "N43°27'30\"", "43°27'30\"N", "43° 27' 30\" N", "43 ° 27 ' 30 \" N", "43°27'30\"", "43°27.5'") cat(coord, sep = "\n") geoTrans(coord) geoTrans("43 D 27.5'", degsym = "D") geoTrans("43° 27' 30\" S") XL <- c(100.6417, 102.9500) YL <- c(11.55833, 14.51667) cat(geoTrans2(XL, YL, dropzero = TRUE), sep = "\n") cat(geoTrans2(XL, YL, latex = TRUE), sep = "\\\n")coord <- c("N 43°27'30\"", "N43°27'30\"", "43°27'30\"N", "43° 27' 30\" N", "43 ° 27 ' 30 \" N", "43°27'30\"", "43°27.5'") cat(coord, sep = "\n") geoTrans(coord) geoTrans("43 D 27.5'", degsym = "D") geoTrans("43° 27' 30\" S") XL <- c(100.6417, 102.9500) YL <- c(11.55833, 14.51667) cat(geoTrans2(XL, YL, dropzero = TRUE), sep = "\n") cat(geoTrans2(XL, YL, latex = TRUE), sep = "\\\n")
These functions change the graphical options to plot haplotype networks.
getHaploNetOptions() setHaploNetOptions(...)getHaploNetOptions() setHaploNetOptions(...)
... |
option(s) and value(s) to be changed (separated by commas if several). |
The options are listed below with their default values. Most of these
values use the standard R graphical paramters (see
par).
bg = "transparent": the background colour of the plot.
labels = TRUE: whether to show the haplotype labels.
labels.cex = 1: size of the haplotype labels.
labels.font = 2: font of the haplotype labels.
labels.color = "black": colour of the haplotype labels.
link.color = "black": colour of the links.
link.type = 1: type of line for the links.
link.type.alt = 2: type of lines for the alternative links.
link.width = 1: line width for the links.
link.width.alt = 1: line width for the alternative links.
haplotype.inner.color = "white": colour used inside the haplotype symbols.
haplotype.outer.color = "black": colour used for the border of the haplotype symbols.
mutations.cex = 1: size of the mutation annotations.
mutations.font = 1: font of the mutation annotations.
mutations.frame.background = "#0000FF4D": background colour (transparent blue).
mutations.frame.border = "black": colour of the frame.
mutations.text.color = 1: colour of the mutation annotations.
mutations.arrow.color = "black": colour of the arrow pointing to the link.
mutations.arrow.type = "triangle": type of the previous arrow.
mutations.sequence.color = "#BFBFBF4D": colour of the sequence (transparent grey).
mutations.sequence.end = "round": possible choices: "round",
"butt", or "square" (or alternatively 0, 1, or 2).
mutations.sequence.length = 0.3: the length of the segment showing the sequence as fraction of the graphical window.
mutations.sequence.width = 5: thickness of this segment.
pie.outer.color = "black": colour of the circle around pie charts.
pie.inner.segments.color = "black": colour of the segments separating the shares of the pies.
pie.colors.function = rainbow: function used to define colours for the frequencies.
scale.ratio = 1: the scale ratio between links and symbol sizes.
show.mutation = 1: option used to show mutation or not (0).
getHaploNetOptions returns a list of options. The other
function returns nothing.
Emmanuel Paradis
getHaploNetOptions()getHaploNetOptions()
This function computes haplotype diversity from DNA sequences. This is a generic function.
hap.div(x, ...) ## S3 method for class 'haplotype' hap.div(x, variance = FALSE, method = "Nei", ...) ## S3 method for class 'DNAbin' hap.div(x, variance = FALSE, method = "Nei", ...)hap.div(x, ...) ## S3 method for class 'haplotype' hap.div(x, variance = FALSE, method = "Nei", ...) ## S3 method for class 'DNAbin' hap.div(x, variance = FALSE, method = "Nei", ...)
x |
an object with DNA data. |
variance |
a logical value specifying whether to calculate the variance of the estimated haplotype diversity. |
method |
(unused, see details). |
... |
further arguments passed to and from methods. |
Currently, only Nei and Tajima's (1981) method is available.
a numeric vector with one or two values (if variance = TRUE).
Emmanuel Paradis
Nei, M. and Tajima, F. (1981) DNA polymorphism detectable by restriction endonuclease. Genetics, 97, 145–163.
data(woodmouse) hap.div(woodmouse) # all haplotypes are unique ## neuraminidase sequences from the 2009 H1N1 data (delivered with adegenet): fl <- system.file("files/pdH1N1-NA.fasta", package = "adegenet") H1N1.NA <- read.dna(fl, "fasta") hap.div(H1N1.NA, TRUE)data(woodmouse) hap.div(woodmouse) # all haplotypes are unique ## neuraminidase sequences from the 2009 H1N1 data (delivered with adegenet): fl <- system.file("files/pdH1N1-NA.fasta", package = "adegenet") H1N1.NA <- read.dna(fl, "fasta") hap.div(H1N1.NA, TRUE)
This utility function extracts the absolute frequencies of haplotypes with respect to a categorical variable (a factor). The output is useful when ploting haplotype networks.
haploFreq(x, fac, split = "_", what = 2, haplo = NULL)haploFreq(x, fac, split = "_", what = 2, haplo = NULL)
x |
a set of DNA sequences (as an object of class
|
fac |
a factor giving the categorical variable (can be missing). |
split |
a single character (see details). |
what |
a single integer (see details). |
haplo |
an object of class |
The frequencies of each haplotype in x are counted with respect
to a factor which is either specified with fac, or extracted
from the labels of x. In the second case, these labels are
split with respect to the character specified in split and the
what'th substrings are extracted and taken as the categorical
variable (see example).
If haplo is specified, the haplotype frequencies are taken from
it, otherwise they are calculated from x.
a matrix of counts.
Klaus Schliep and Emmanuel Paradis
## generate some artificial data from 'woodmouse': data(woodmouse) x <- woodmouse[sample(15, size = 50, replace = TRUE), ] ## labels IdXXX_PopXXX_LocXXX rownames(x) <- paste("Id", 1:50, "_Pop", 1:2, "_Loc", 1:5, sep = "") head(labels(x)) h <- haplotype(x) ## frequencies of haplotypes wrt 'Pop': f.pop <- haploFreq(x, haplo = h) ## frequencies of haplotypes wrt 'Loc': f.loc <- haploFreq(x, what = 3, haplo = h) nt <- haploNet(h) fq <- attr(nt, "freq") op <- par(mfcol = c(1, 2)) plot(nt, size = fq, pie = f.pop, labels = FALSE) plot(nt, size = fq, pie = f.loc, labels = FALSE) par(op)## generate some artificial data from 'woodmouse': data(woodmouse) x <- woodmouse[sample(15, size = 50, replace = TRUE), ] ## labels IdXXX_PopXXX_LocXXX rownames(x) <- paste("Id", 1:50, "_Pop", 1:2, "_Loc", 1:5, sep = "") head(labels(x)) h <- haplotype(x) ## frequencies of haplotypes wrt 'Pop': f.pop <- haploFreq(x, haplo = h) ## frequencies of haplotypes wrt 'Loc': f.loc <- haploFreq(x, what = 3, haplo = h) nt <- haploNet(h) fq <- attr(nt, "freq") op <- par(mfcol = c(1, 2)) plot(nt, size = fq, pie = f.pop, labels = FALSE) plot(nt, size = fq, pie = f.loc, labels = FALSE) par(op)
haploNet computes a haplotype network. There is a plot method
and two conversion functions towards other packages.
haploNet(h, d = NULL, getProb = TRUE) ## S3 method for class 'haploNet' print(x, ...) ## S3 method for class 'haploNet' plot(x, size = 1, col, bg, col.link, lwd, lty, shape = "circles", pie = NULL, labels, font, cex, col.lab, scale.ratio, asp = 1, legend = FALSE, fast = FALSE, show.mutation, threshold = c(1, 2), xy = NULL, ...) ## S3 method for class 'haploNet' as.network(x, directed = FALSE, altlinks = TRUE, ...) ## S3 method for class 'haploNet' as.igraph(x, directed = FALSE, use.labels = TRUE, altlinks = TRUE, ...) ## S3 method for class 'haploNet' as.phylo(x, quiet, ...) ## S3 method for class 'haploNet' as.evonet(x, ...)haploNet(h, d = NULL, getProb = TRUE) ## S3 method for class 'haploNet' print(x, ...) ## S3 method for class 'haploNet' plot(x, size = 1, col, bg, col.link, lwd, lty, shape = "circles", pie = NULL, labels, font, cex, col.lab, scale.ratio, asp = 1, legend = FALSE, fast = FALSE, show.mutation, threshold = c(1, 2), xy = NULL, ...) ## S3 method for class 'haploNet' as.network(x, directed = FALSE, altlinks = TRUE, ...) ## S3 method for class 'haploNet' as.igraph(x, directed = FALSE, use.labels = TRUE, altlinks = TRUE, ...) ## S3 method for class 'haploNet' as.phylo(x, quiet, ...) ## S3 method for class 'haploNet' as.evonet(x, ...)
h |
an object of class |
d |
an object giving the distances among haplotypes (see details). |
getProb |
a logical specifying whether to calculate Templeton's probabilities (see details). |
x |
an object of class |
size |
a numeric vector giving the diameter of the circles representing the haplotypes: this is in the same unit than the links and eventually recycled. |
col |
a character vector specifying the colours of the circles; eventually recycled. |
bg |
a character vector (or a function) specifying either the
colours of the background of the symbols (if |
col.link |
a character vector specifying the colours of the links; eventually recycled. |
lwd |
a numeric vector giving the width of the links; eventually recycled. |
lty |
idem for the line types. |
shape |
the symbol shape used for the haplotypes (eventually
recycled): |
pie |
a matrix used to draw pie charts for each haplotype; its number of rows must be equal to the number of haplotypes. |
labels |
a logical specifying whether to identify the haplotypes with their labels (the default). |
font |
the font used for these labels (bold by default); must be an integer between 1 and 4. |
cex |
a numerical specifying the character expansion of the labels. |
col.lab |
the color of the labels. |
scale.ratio |
the ratio of the scale of the links representing the number of steps on the scale of the circles representing the haplotypes. It may be needed to give a value greater than one to avoid overlapping circles. |
asp |
the aspect ratio of the plot. Do not change the default unless you want to distort your network. |
legend |
a logical specifying whether to draw the legend, or a
vector of length two giving the coordinates where to draw the
legend; |
fast |
a logical specifying whether to optimize the spacing of
the circles; |
show.mutation |
an integer value: if 0, nothing is drawn on the links; if 1, the mutations are shown with small segments on the links; if 2, they are shown with small dots; if 3, the number of mutations are printed on the links. |
threshold |
a numeric vector with two values (or 0) giving the
lower and upper numbers of mutations for alternative links to be
displayed. If |
directed |
a logical specifying whether the network is directed
( |
use.labels |
a logical specifying whether to use the original labels in the returned network. |
altlinks |
whether to output the alternative links when
converting to another class; |
quiet |
whether to give a warning when reticulations are dropped when converting a network into a tree. |
xy |
the coordinates of the nodes (see |
... |
further arguments passed to |
By default, the haplotype network is built using an infinite site
model (i.e., uncorrected or Hamming distance) of DNA sequences and
pairwise deletion of missing data (see dist.dna).
Users may specify their own distance with the argument d. There
is no check of labels, so the user must make sure that the distances
are ordered in the same way than the haplotypes.
The probabilities calculated with Templeton et al.'s (1992) method may
give non-finite values with very divergent sequences, resulting in an
error from haploNet. If this happens, it may be better to use
getProb = FALSE.
If two haplotypes are very different, haploNet will likely fail
(error during integration due to non-finite values).
haploNet returns an object of class "haploNet" which is
a matrix where each row represents a link in the network, the first
and second columns give the numbers of the linked haplotypes, the
third column, named "step", gives the number of steps in this
link, and the fourth column, named "Prob", gives the
probability of a parsimonious link as given by Templeton et
al. (1992). There are three additional attributes: "freq", the
absolute frequencies of each haplotype, "labels", their labels,
and "alter.links", the alternative links of the network.
as.network and as.igraph return objects of the
appropriate class.
Plotting haplotype networks is a difficult task. There is a vignette
in pegas (see vignette("PlotHaploNet")) giving some
information on this isseu. You may also see two posts on r-sig-genetics
(July 2022) that give some tricks in the situation when one haplotype
is abundant and the others are in low frequencies (the symbols are
likely to overlap a lot by default):
https://stat.ethz.ch/pipermail/r-sig-genetics/2022-July/000237.html
https://stat.ethz.ch/pipermail/r-sig-genetics/2022-July/000238.html
The first post explains how to use the package network in combination with pegas, and the second one gives a trick that works with pegas only for a similar result.
Emmanuel Paradis, Klaus Schliep
Templeton, A. R., Crandall, K. A. and Sing, C. F. (1992) A cladistic analysis of phenotypic association with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation. Genetics, 132, 619–635.
haplotype, haploFreq, replot,
diffHaplo, mst, mjn
## generate some artificial data from 'woodmouse': data(woodmouse) x <- woodmouse[sample(15, size = 110, replace = TRUE), ] h <- haplotype(x) (net <- haploNet(h)) plot(net) ## symbol sizes equal to haplotype sizes: plot(net, size = attr(net, "freq"), fast = TRUE) plot(net, size = attr(net, "freq")) plot(net, size = attr(net, "freq"), scale.ratio = 2, cex = 0.8)## generate some artificial data from 'woodmouse': data(woodmouse) x <- woodmouse[sample(15, size = 110, replace = TRUE), ] h <- haplotype(x) (net <- haploNet(h)) plot(net) ## symbol sizes equal to haplotype sizes: plot(net, size = attr(net, "freq"), fast = TRUE) plot(net, size = attr(net, "freq")) plot(net, size = attr(net, "freq"), scale.ratio = 2, cex = 0.8)
haplotype extracts the haplotypes from a set of DNA
sequences. The result can be plotted with the appropriate function.
haplotype(x, ...) ## S3 method for class 'DNAbin' haplotype(x, labels = NULL, strict = FALSE, trailingGapsAsN = TRUE, ...) ## S3 method for class 'character' haplotype(x, labels = NULL, ...) ## S3 method for class 'numeric' haplotype(x, labels = NULL, ...) ## S3 method for class 'haplotype' plot(x, xlab = "Haplotype", ylab = "Number", ...) ## S3 method for class 'haplotype' print(x, ...) ## S3 method for class 'haplotype' summary(object, ...) ## S3 method for class 'haplotype' sort(x, decreasing = ifelse(what == "frequencies", TRUE, FALSE), what = "frequencies", ...) ## S3 method for class 'haplotype' x[...]haplotype(x, ...) ## S3 method for class 'DNAbin' haplotype(x, labels = NULL, strict = FALSE, trailingGapsAsN = TRUE, ...) ## S3 method for class 'character' haplotype(x, labels = NULL, ...) ## S3 method for class 'numeric' haplotype(x, labels = NULL, ...) ## S3 method for class 'haplotype' plot(x, xlab = "Haplotype", ylab = "Number", ...) ## S3 method for class 'haplotype' print(x, ...) ## S3 method for class 'haplotype' summary(object, ...) ## S3 method for class 'haplotype' sort(x, decreasing = ifelse(what == "frequencies", TRUE, FALSE), what = "frequencies", ...) ## S3 method for class 'haplotype' x[...]
x |
a set of DNA sequences (as an object of class
|
object |
an object of class |
labels |
a vector of character strings used as names for the rows of the returned object. By default, Roman numerals are given. |
strict |
a logical value; if |
trailingGapsAsN |
a logical value; if |
xlab, ylab
|
labels for the x- and x-axes. |
... |
further arguments passed to
|
decreasing |
a logical value specifying in which order to sort
the haplotypes; by default this depends on the value of
|
what |
a character specifying on what feature the haplotypes
should be sorted: this must be |
The way ambiguities in the sequences are taken into account is explained in a post to r-sig-phylo (see the examples below):
https://www.mail-archive.com/[email protected]/msg05541.html
The sort method sorts the haplotypes in decreasing frequencies
(the default) or in alphabetical order of their labels (if what =
"labels"). Note that if these labels are Roman numerals (as assigned by
haplotype), their alphabetical order may not be their numerical
one (e.g., IX is alphabetically before VIII).
From pegas 0.7, haplotype extracts haplotypes taking into
account base ambiguities (see Note below).
haplotype returns an object of class c("haplotype",
"DNAbin") which is an object of class "DNAbin" with two
additional attributes: "index" identifying the index of each
observation that share the same haplotype, and "from" giving
the name of the original data.
sort returns an object of the same class respecting its
attributes.
The presence of ambiguous bases and/or alignment gaps in DNA sequences
can make the interpretation of haplotypes difficult. It is recommended
to check their distributions with image.DNAbin and
base.freq (using the options in both functions).
Comparing the results obtained playing with the options strict
and trailingGapsAsN of haplotype.DNAbin may be useful.
Note that the ape function seg.sites has the
same two options (as from ape 5.4) which may be useful to find the
relevant sites in the sequence alignment.
There are cases where the algorithm that pools the different sequences into haplotypes has difficulties, although it seems to require a specific configuration of missing/ambiguous data. The last example below is one of them.
Emmanuel Paradis
haploNet, haploFreq,
subset.haplotype,
DNAbin for manipulation of DNA sequences in R.
The haplotype method for objects of class "loci" is
documented separately: haplotype.loci.
## generate some artificial data from 'woodmouse': data(woodmouse) x <- woodmouse[sample(15, size = 110, replace = TRUE), ] (h <- haplotype(x)) ## the indices of the individuals belonging to the 1st haplotype: attr(h, "index")[[1]] plot(sort(h)) ## get the frequencies in a named vector: setNames(lengths(attr(h, "index")), labels(h)) ## data posted by Hirra Farooq on r-sig-phylo (see link above): cat(">[A]\nCCCGATTTTATATCAACATTTATTT------", ">[D]\nCCCGATTTT----------------------", ">[B]\nCCCGATTTTATATCAACATTTATTT------", ">[C]\nCCCGATTTTATATCACCATTTATTTTGATTT", file = "x.fas", sep = "\n") x <- read.dna("x.fas", "f") unlink("x.fas") ## show the sequences and the distances: alview(x) dist.dna(x, "N", p = TRUE) ## by default there are 3 haplotypes with a warning about ambiguity: haplotype(x) ## the same 3 haplotypes without warning: haplotype(x, strict = TRUE) ## if we remove the last sequence there is, by default, a single haplotype: haplotype(x[-4, ]) ## to get two haplotypes separately as with the complete data: haplotype(x[-4, ], strict = TRUE) ## a simpler example: y <- as.DNAbin(matrix(c("A", "A", "A", "A", "R", "-"), 3)) haplotype(y) # 1 haplotype haplotype(y, strict = TRUE) # 3 haplotypes haplotype(y, trailingGapsAsN = FALSE) # 2 haplotypes ## a tricky example with 4 sequences and 1 site: z <- as.DNAbin(matrix(c("Y", "A", "R", "N"), 4)) alview(z, showpos = FALSE) ## a single haplotype is identified: haplotype(z) ## 'Y' has zero-distance with (and only with) 'N', so they are pooled ## together; at a later iteration of this pooling step, 'N' has ## zero-distance with 'R' (and ultimately with 'A') so they are pooled ## if the sequences are ordered differently, 'Y' and 'A' are separated: haplotype(z[c(4, 1:3), ])## generate some artificial data from 'woodmouse': data(woodmouse) x <- woodmouse[sample(15, size = 110, replace = TRUE), ] (h <- haplotype(x)) ## the indices of the individuals belonging to the 1st haplotype: attr(h, "index")[[1]] plot(sort(h)) ## get the frequencies in a named vector: setNames(lengths(attr(h, "index")), labels(h)) ## data posted by Hirra Farooq on r-sig-phylo (see link above): cat(">[A]\nCCCGATTTTATATCAACATTTATTT------", ">[D]\nCCCGATTTT----------------------", ">[B]\nCCCGATTTTATATCAACATTTATTT------", ">[C]\nCCCGATTTTATATCACCATTTATTTTGATTT", file = "x.fas", sep = "\n") x <- read.dna("x.fas", "f") unlink("x.fas") ## show the sequences and the distances: alview(x) dist.dna(x, "N", p = TRUE) ## by default there are 3 haplotypes with a warning about ambiguity: haplotype(x) ## the same 3 haplotypes without warning: haplotype(x, strict = TRUE) ## if we remove the last sequence there is, by default, a single haplotype: haplotype(x[-4, ]) ## to get two haplotypes separately as with the complete data: haplotype(x[-4, ], strict = TRUE) ## a simpler example: y <- as.DNAbin(matrix(c("A", "A", "A", "A", "R", "-"), 3)) haplotype(y) # 1 haplotype haplotype(y, strict = TRUE) # 3 haplotypes haplotype(y, trailingGapsAsN = FALSE) # 2 haplotypes ## a tricky example with 4 sequences and 1 site: z <- as.DNAbin(matrix(c("Y", "A", "R", "N"), 4)) alview(z, showpos = FALSE) ## a single haplotype is identified: haplotype(z) ## 'Y' has zero-distance with (and only with) 'N', so they are pooled ## together; at a later iteration of this pooling step, 'N' has ## zero-distance with 'R' (and ultimately with 'A') so they are pooled ## if the sequences are ordered differently, 'Y' and 'A' are separated: haplotype(z[c(4, 1:3), ])
This function extracts haplotypes from phased genotypes.
## S3 method for class 'loci' haplotype(x, locus = 1:2, quiet = FALSE, compress = TRUE, check.phase = TRUE, ...) ## S3 method for class 'haplotype.loci' plot(x, ...) dist.haplotype.loci(x)## S3 method for class 'loci' haplotype(x, locus = 1:2, quiet = FALSE, compress = TRUE, check.phase = TRUE, ...) ## S3 method for class 'haplotype.loci' plot(x, ...) dist.haplotype.loci(x)
x |
an object of class |
locus |
a vector of integers giving the loci to analyse. |
quiet |
a logical value specifying whether to not print the
progress of the analysis ( |
compress |
by default only the unique haplotypes are returned
with their frequencies. If |
check.phase |
a logical value specifying whether to check if the individual genotypes are phased. |
... |
arguments passed to and from methods. |
The individuals with at least one unphased genotype are ignored with a warning.
dist.haplotype.loci computes pairwise distances among
haplotypes by counting the number of different alleles.
Checking whether the genotypes are phased can be time consuming with
very big data sets. It may be useful to set check.phase = FALSE
if several analyses are done on the same data and no warning was
issued after the first scan, or you are sure that the genotypes are phased.
haplotype returns a matrix of mode character with the loci as
rows and the haplotypes as columns. The attribute "freq" gives
the counts of each haplotype and the class is "haplotype.loci".
dist.haplotype.loci returns an object of class "dist".
haplotype is a generic function with methods for objects of
class "DNAbin" and of class "loci". Note that the class
returned by these methods is different: c("haplotype", "DNAbin")
and "haplotype.loci", respectively. This and other details are
likely to change in the future.
Emmanuel Paradis
Thes functions compute the mean heterozygosity(ies) from gene frequencies, and return optionally the associated variance(s).
H(x, ...) ## S3 method for class 'loci' H(x, variance = FALSE, observed = FALSE, ...) ## Default S3 method: H(x, variance = FALSE, ...) heterozygosity(x, variance = FALSE)H(x, ...) ## S3 method for class 'loci' H(x, variance = FALSE, observed = FALSE, ...) ## Default S3 method: H(x, variance = FALSE, ...) heterozygosity(x, variance = FALSE)
x |
an object of class |
variance |
a logical indicating whether the variance of the
estimated heterozygosity should be returned ( |
observed |
a logical specifying whether to calculate the observed heterozygosity. |
... |
unused. |
The argument x can be either a factor or a vector. If it is a
factor, then it is taken to give the individual alleles in the
population. If it is a numeric vector, then its values are taken to be
the numbers of each allele in the population. If it is a non-numeric
vector, it is a coerced as a factor.
The mean heterozygosity is estimated with:
where is the number of genes in the sample, is the
number of alleles, and is the observed (relative) frequency
of the th allele.
For the default method: a numeric vector of length one with the estimated mean heterozygosity (the default), or of length two if the variance is returned.
For the "loci" method: a numeric matrix with one, two, or three
columns with a row for each locus and the values of heterozygosity as
columns.
Emmanuel Paradis
Nei, M. (1987) Molecular evolutionary genetics. New York: Columbia University Press.
data(jaguar) H(jaguar, TRUE, TRUE) ## use the (old) default method: ## convert the data and compute frequencies: S <- summary(jaguar) ## compute H for all loci: sapply(S, function(x) H(x$allele)) ## ... and its variance sapply(S, function(x) H(x$allele, variance = TRUE))data(jaguar) H(jaguar, TRUE, TRUE) ## use the (old) default method: ## convert the data and compute frequencies: S <- summary(jaguar) ## compute H for all loci: sapply(S, function(x) H(x$allele)) ## ... and its variance sapply(S, function(x) H(x$allele, variance = TRUE))
This function tests, for a series of loci, the hypothesis that
genotype frequencies follow the Hardy–Weinberg equilibrium.
hw.test is a generic with methods for the classes
"loci" and genind. Note that the latter
replaces HWE.test.genind in the adegenet package.
hw.test(x, B = 1000, ...) ## S3 method for class 'loci' hw.test(x, B = 1000, ...) ## S3 method for class 'genind' hw.test(x, B = 1000, ...)hw.test(x, B = 1000, ...) ## S3 method for class 'loci' hw.test(x, B = 1000, ...) ## S3 method for class 'genind' hw.test(x, B = 1000, ...)
x |
an object of class |
B |
the number of replicates for the Monte Carlo procedure; for the regular HW test, set B = 0 (see details). |
... |
further arguments to be passed. |
This test can be performed with any level of ploidy. Two versions
of the test are available: the classical -test based
on the expected genotype frequencies calculated from the allelic
frequencies, and an exact test based on Monte Carlo permutations of
alleles (Guo and Thompson 1992). For the moment, the latter version is
available only for diploids. Set B = 0 if you want to skip the
second test.
A matrix with three or four columns with the -value,
the number of degrees of freedom, the associated P-value, and
possibly the P-value from the Monte Carlo test. The rows of
this matrix are the different loci in x.
Main code by Emmanuel Paradis; wrapper for genind
objects by Thibaut Jombart.
Guo, S. W. and Thompson, E. A. (1992) Performing the exact test of Hardy–Weinberg proportion for multiple alleles. Biometrics, 48, 361–372.
## Not run: require(adegenet) ## load data data(nancycats) ## test on genind object, no permutation hw.test(nancycats, B=0) ## test on loci object x <- as.loci(nancycats) hw.test(x) ## End(Not run) data(jaguar) hw.test(jaguar)## Not run: require(adegenet) ## load data data(nancycats) ## test on genind object, no permutation hw.test(nancycats, B=0) ## test on loci object x <- as.loci(nancycats) hw.test(x) ## End(Not run) data(jaguar) hw.test(jaguar)
Fifty nine jaguars (Panthera onca) from four populations genotyped at thirteen micro-satellites by Haag et al. (2010).
data(jaguar)data(jaguar)
An object of class "loci" with 59 rows and 14 columns.
Haag, T., Santos, A. S., Sana, D. A., Morato, R. G., Cullen, Jr., L., Crawshaw, Jr., P. G., De Angelo, C., Di Bitetti, M. S., Salzano, F. M. and Eizirik, E. (2010) The effect of habitat fragmentation on the genetic structure of a top predator: loss of diversity and high differentiation among remnant populations of Atlantic Forest jaguars (Panthera onca). Molecular Ecology, 22, 4906–4921.
Haag, T., Santos, A. S., Sana, D. A., Morato, R. G., Cullen, Jr., L., Crawshaw, Jr., P. G., De Angelo, C., Di Bitetti, M. S., Salzano, F. M. and Eizirik, E. (2010) Data from: The effect of habitat fragmentation on the genetic structure of a top predator: loss of diversity and high differentiation among remnant populations of Atlantic Forest jaguars (Panthera onca). Dryad Digital Repository. doi:10.5061/dryad.1884
The vignette “ReadingFiles” explains how to read data like these from Dryad (https://datadryad.org/stash).
data(jaguar) str(jaguar) s <- summary(jaguar) ## Not run: ## works if the device is large enough: plot(s, layout = 30, las = 2) ## End(Not run)data(jaguar) str(jaguar) s <- summary(jaguar) ## Not run: ## works if the device is large enough: plot(s, layout = 30, las = 2) ## End(Not run)
These two functions analyse linkage disequilibrium in the case of
phased (LD) or unphased (LD2) genotypes.
LD(x, locus = c(1, 2), details = TRUE) LD2(x, locus = c(1, 2), details = TRUE)LD(x, locus = c(1, 2), details = TRUE) LD2(x, locus = c(1, 2), details = TRUE)
x |
an object of class |
locus |
a vector of two integers giving the loci to analyse. |
details |
a logical value indicating whether to print the correlation matrix among alleles. |
These functions consider a pair of loci and compute the correlations among pairs of alleles.
LD first scans the data for unphased genotypes: all individuals
with at least one unphased genotype are dropped with a warning. It is
based on the observed frequencies of haplotypes (Zaykin et
al. 2008). LD2 is based on the observed frequencies of
different genotypes (Schaid 2004).
Both functions accept any number of alleles. LD can work with
any level of ploidy; LD2 works with diploid data.
The present version does not test the significance of the
test (Zaykin et al. 2008) with permutations. These authors present
simulation results suggesting that the chi-squared approximation has
similar type I error rates and power than the test based on
permutations even for small sample sizes. Furthermore, this test has
better statistical properties than alternatives such as those reported
here (LRT and Pearson's test).
For both functions, if details = FALSE, only the T2 test is
returned.
For LD: if details = TRUE, a named list with the
following elements:
Observed frequencies |
the counts of haplotypes in the data. |
Expected frequencies |
the expected frequencies of haplotypes computed from the observed proportions of alleles under the assumption of no linkage disequilibrium. |
Correlations among alleles |
the observed correlations among alleles from both loci. |
LRT (G-squared) |
the likelihood-ratio test of the null hypothesis of no linkage disequilibrium. |
Pearson's test (chi-squared) |
the chi-squared test based on haplotypes counts. |
T2 |
the |
For LD2: if details = TRUE, a named list with two
elements:
Delta |
the correlations among alleles (denoted |
T2 |
the |
Emmanuel Paradis
Schaid, D. J. (2004) Linkage disequilibrium testing when linkage phase is unknown. Genetics, 166, 505–512.
Zaykin, D. V., Pudovkin, A. and Weir, B. S. (2008) Correlation-based inference for linkage disequilibrium with multiple alleles. Genetics, 180, 533–545.
haplotype.loci, is.phased,
LDscan
data(jaguar) LD2(jaguar, details = FALSE) LD2(jaguar, locus = 8:9, details = FALSE)data(jaguar) LD2(jaguar, details = FALSE) LD2(jaguar, locus = 8:9, details = FALSE)
LDscan computes a matrix of pairwise linkage disequilibrium (LD)
coefficients () from a set of loci (which must be bi-allelic;
if not, the results are not guaranteed to be meaningful). The
genotypes must be phased.
LDmap plots a matrix of LD coefficients, optionally with the
positions of the loci.
LDscan(x, ...) ## S3 method for class 'DNAbin' LDscan(x, quiet = FALSE, what = c("r", "Dprime"), ...) ## S3 method for class 'loci' LDscan(x, depth = NULL, quiet = FALSE, what = c("r", "Dprime"), ...) LDmap(d, POS = NULL, breaks = NULL, col = NULL, border = NA, angle = 0, asp = 1, cex = 1, scale.legend = 0.8, ...)LDscan(x, ...) ## S3 method for class 'DNAbin' LDscan(x, quiet = FALSE, what = c("r", "Dprime"), ...) ## S3 method for class 'loci' LDscan(x, depth = NULL, quiet = FALSE, what = c("r", "Dprime"), ...) LDmap(d, POS = NULL, breaks = NULL, col = NULL, border = NA, angle = 0, asp = 1, cex = 1, scale.legend = 0.8, ...)
x |
an object of class |
depth |
a vector of integers giving the the depth(s) (or lags) at
which the |
quiet |
a logical: should the progress of the operation be printed? |
what |
the quantity to be computed. Two choices are possible:
|
d |
a correlation matrix (can be an object of class |
POS |
an optional vector of locus positions (e.g., from a VCF file; see examples). |
breaks |
a vector of break intervals to count the values in
|
col |
an optional vector of colours; a scale from lightyellow to red is used by default. |
border |
the border of the rectangles: the default is to have no
border (this is not the same than default in
|
angle |
value (in degrees) to rotate the graphic. |
asp |
the aspect ratio of the graphic; one by default so the elements are squares (not rectangles). |
cex |
the scaling of the labels and text. |
scale.legend |
the scaling of the legend rectangles. |
... |
further arguments passed to methods ( |
The LD coefficient is well defined when the two loci have
only two alleles. In other cases, LD is well defined (see
LD) but the definition of is not clear.
All levels of ploidy are accepted, but all loci should have the same ploidy level.
If depth is used, the 's are calculated only for the
pairs of loci that are distant by these values in x, but
necessarily on the chromosome. The returned list has names set with
the values of depth.
The value returned is actually (not ).
LDscan returns an object of class "dist" by default, or
a list if depth is used.
Emmanuel Paradis
data(woodmouse) d <- LDscan(woodmouse) LDmap(d, seg.sites(woodmouse), seq(0, 1, .1)) ## Not run: ## Download the VCF file from Dryad: ## https://doi.org/10.5061/dryad.446sv.2 ## the VCF file should have this name: fl <- "global.pop.GATK.SNP.hard.filters.V3.phased_all.pop.maf.05.recode.vcf.gz" info.fly <- VCFloci(fl) ## LD map from the first 100 loci: x <- read.vcf(fl, to = 100) # read only 100 loci res <- LDscan(x) bks <- seq(0, 1, 0.2) LDmap(res, info.fly$POS[1:100], bks, scale.legend = 3) ## check the chromosomes: table(info.fly$CHROM) ## LD map from 100 loci randomly distributed on the chromosome: s <- ceiling(seq(1, 224253, length.out = 100)) xs <- read.vcf(fl, which.loci = s) res2 <- LDscan(xs) LDmap(res2, info.fly$POS[s], bks, scale.legend = 3) ## something simpler with 10 loci: x10 <- x[, 1:10] ## the VCF file has no locus IDs, so we give some here: names(x10) <- paste0("Loc", 1:10) res10 <- LDscan(x10, quiet = TRUE) LDmap(res10, angle = 45, border = NULL) ## End(Not run)data(woodmouse) d <- LDscan(woodmouse) LDmap(d, seg.sites(woodmouse), seq(0, 1, .1)) ## Not run: ## Download the VCF file from Dryad: ## https://doi.org/10.5061/dryad.446sv.2 ## the VCF file should have this name: fl <- "global.pop.GATK.SNP.hard.filters.V3.phased_all.pop.maf.05.recode.vcf.gz" info.fly <- VCFloci(fl) ## LD map from the first 100 loci: x <- read.vcf(fl, to = 100) # read only 100 loci res <- LDscan(x) bks <- seq(0, 1, 0.2) LDmap(res, info.fly$POS[1:100], bks, scale.legend = 3) ## check the chromosomes: table(info.fly$CHROM) ## LD map from 100 loci randomly distributed on the chromosome: s <- ceiling(seq(1, 224253, length.out = 100)) xs <- read.vcf(fl, which.loci = s) res2 <- LDscan(xs) LDmap(res2, info.fly$POS[s], bks, scale.legend = 3) ## something simpler with 10 loci: x10 <- x[, 1:10] ## the VCF file has no locus IDs, so we give some here: names(x10) <- paste0("Loc", 1:10) res10 <- LDscan(x10, quiet = TRUE) LDmap(res10, angle = 45, border = NULL) ## End(Not run)
This function computes the median-joining network (MJN) as described by Bandelt et al. (1999).
mjn(x, epsilon = 0, max.n.cost = 10000, prefix = "median.vector_", quiet = FALSE) ## S3 method for class 'mjn' plot(x, shape = c("circles", "diamonds"), bg = c("green", "slategrey"), labels = FALSE, ...)mjn(x, epsilon = 0, max.n.cost = 10000, prefix = "median.vector_", quiet = FALSE) ## S3 method for class 'mjn' plot(x, shape = c("circles", "diamonds"), bg = c("green", "slategrey"), labels = FALSE, ...)
x |
a matrix (or data frame) of DNA sequences or binary 0/1
data; an object of class |
epsilon |
tolerance parameter. |
max.n.cost |
the maximum number of costs to be computed. |
prefix |
the prefix used to label the median vectors. |
quiet |
a logical value; by default, the progress of the calculatins is printed. |
shape, bg
|
the default shapes and colours for observed haplotypes and median vectors. |
labels |
by default, the labels of the haplotypes are printed. |
... |
other arguments passed to |
MJN is a network method where unobserved sequences (the median
vectors) are reconstructed and included in the final network. Unlike
mst, rmst, and msn, mjn works with
the original sequences, the distances being calculated internally
using a Hamming distance method (with dist(x, "manhattan") for
binary data or dist.dna(x, "N") for DNA sequences).
The parameter epsilon controls how the search for new median
vectors is performed: the larger this parameter, the wider the search
(see the example with binary data).
If the sequences are very divergent, the search for new median vectors
can take a very long time. The argument max.n.cost controls how
many such vectors are added to the network (the default value should
avoid the function to run endlessly).
The arguments shape and bg must be of length two (unlike
in plot.haploNet). It is possible to have more
flexibility when plotting the MJN by changing its class, for instance
with the output in the examples below: class(nt0) <- "haplotNet".
an object of class c("mjn", "haploNet") with an extra attribute
(data) containing the original data together with the median vectors.
Since pegas 1.0, mjn is expected to run in reasonable
times (less than 15 sec with 100 sequences). Bandelt et al. (1999)
reported long computing times because of the need to compute a lot of
median vectors. Running times also depend on the level of polymorphism
in the data (see above).
Emmanuel Paradis
Bandelt, H. J., Forster, P. and Rohl, A. (1999) Median-joining networks for inferring intraspecific phylogenies. Molecular Biology and Evolution, 16, 37–48.
## data in Table 1 of Bandelt et al. (1999): x <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1) x <- matrix(x, 4, 9, byrow = TRUE) rownames(x) <- LETTERS[1:4] (nt0 <- mjn(x)) (nt1 <- mjn(x, 1)) (nt2 <- mjn(x, 2)) plot(nt0) ## Not run: ## same like in Fig. 4 of Bandelt et al. (1999): plotNetMDS(nt2, dist(attr(nt2, "data"), "manhattan"), 3) ## End(Not run) ## data in Table 2 of Bandelt et al. (1999): z <- list(c("g", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a"), c("a", "g", "g", "a", "a", "a", "a", "a", "a", "a", "a", "a"), c("a", "a", "a", "g", "a", "a", "a", "a", "a", "a", "g", "g"), c("a", "a", "a", "a", "g", "g", "a", "a", "a", "a", "g", "g"), c("a", "a", "a", "a", "a", "a", "a", "a", "g", "g", "c", "c"), c("a", "a", "a", "a", "a", "a", "g", "g", "g", "g", "a", "a")) names(z) <- c("A1", "A2", "B1", "B2", "C", "D") z <- as.matrix(as.DNAbin(z)) (ntz <- mjn(z, 2)) ## Not run: ## same like in Fig. 5 of Bandelt et al. (1999): plotNetMDS(ntz, dist.dna(attr(ntz, "data"), "N"), 3) ## End(Not run)## data in Table 1 of Bandelt et al. (1999): x <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1) x <- matrix(x, 4, 9, byrow = TRUE) rownames(x) <- LETTERS[1:4] (nt0 <- mjn(x)) (nt1 <- mjn(x, 1)) (nt2 <- mjn(x, 2)) plot(nt0) ## Not run: ## same like in Fig. 4 of Bandelt et al. (1999): plotNetMDS(nt2, dist(attr(nt2, "data"), "manhattan"), 3) ## End(Not run) ## data in Table 2 of Bandelt et al. (1999): z <- list(c("g", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a", "a"), c("a", "g", "g", "a", "a", "a", "a", "a", "a", "a", "a", "a"), c("a", "a", "a", "g", "a", "a", "a", "a", "a", "a", "g", "g"), c("a", "a", "a", "a", "g", "g", "a", "a", "a", "a", "g", "g"), c("a", "a", "a", "a", "a", "a", "a", "a", "g", "g", "c", "c"), c("a", "a", "a", "a", "a", "a", "g", "g", "g", "g", "a", "a")) names(z) <- c("A1", "A2", "B1", "B2", "C", "D") z <- as.matrix(as.DNAbin(z)) (ntz <- mjn(z, 2)) ## Not run: ## same like in Fig. 5 of Bandelt et al. (1999): plotNetMDS(ntz, dist.dna(attr(ntz, "data"), "N"), 3) ## End(Not run)
This function draws a histogram of the frequencies of pairwise distances from a set of DNA sequences.
MMD(x, xlab = "Distance", main = "", rug = TRUE, legend = TRUE, lcol = c("blue", "red"), lty = c(1, 1), bw = 2, ...)MMD(x, xlab = "Distance", main = "", rug = TRUE, legend = TRUE, lcol = c("blue", "red"), lty = c(1, 1), bw = 2, ...)
x |
a set of DNA sequences (object of class |
xlab |
the label for the x-axis. |
main |
the title (none by default). |
rug |
a logical specifying whether to add a rug of the pairwise
distances on the horizontal axis (see |
legend |
a logical specifying whether to draw a legend. |
lcol |
the colours used for the curves. |
lty |
the line types for the curves |
bw |
the bandwidth used for the empirical density curve (passed
to |
... |
further arguments passed to |
The histogram shows the observed distribution of pairwise distances. The lines show an empirical density estimate (in blue) and the expected distribution under stable population (Rogers and Harpending 1992).
an invisible list with three elements:
histogram |
the output of the |
empirical.density |
the empirical density as estimated by
|
expected.curve: |
the values of the curve expected under stable population. |
Emmanuel Paradis and David Winter
Rogers, A. R. and Harpending, H. (1992) Population growth makes waves in the distribution of pairwise genetic-differences. Molecular Biology and Evolution, 9, 552–569.
data(woodmouse) mmd.woodm <- MMD(woodmouse) str(mmd.woodm) MMD(woodmouse, breaks = 20, legend = FALSE) MMD(woodmouse, lty = 1:2, lcol = rep("black", 2), col = "lightgrey")data(woodmouse) mmd.woodm <- MMD(woodmouse) str(mmd.woodm) MMD(woodmouse, breaks = 20, legend = FALSE) MMD(woodmouse, lty = 1:2, lcol = rep("black", 2), col = "lightgrey")
Computes a minimum spanning tree using Kruskal's algorithm, the minimum spanning network using Bandelt et al.'s algorithm, or the randomized minimum spanning tree (Paradis 2018).
mst(d) msn(d) rmst(d, B = NULL, stop.criterion = NULL, iter.lim = 1000, quiet = FALSE)mst(d) msn(d) rmst(d, B = NULL, stop.criterion = NULL, iter.lim = 1000, quiet = FALSE)
d |
a distance matrix, either as an object of class |
B |
number of randomizations. |
stop.criterion |
the stopping criterion if |
iter.lim |
the maximum number of iterations. |
quiet |
a logical value specifying whether to indicate progress of calculations. |
For the RMST, the calculations stop when no new links are found after a
number of successive iterations specified by stop.criterion. By
default, this number is ceiling(sqrt(n)) where n is the number of
observations. This criterion is ignored if B is given, or if n
< 6 in which case complete enumeration is done. In all cases, no more
than iter.lim iterations are done.
an object of class "haploNet".
ape has a function named mst which is older (and used by
other packages) and returns its results in a different form. The
present version is more efficient. If you want to use the older
version after loading pegas, use ape::mst since ape
will certainly always be loaded before pegas.
Emmanuel Paradis
Bandelt, H. J., Forster, P. and Rohl, A. (1999) Median-joining networks for inferring intraspecific phylogenies. Molecular Biology and Evolution, 16, 37–48.
Kruskal, J. B., Jr. (1956) On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society, 7, 48–50.
Paradis, E. (2018) Analysis of haplotype networks: the randomized minimum spanning tree method. Methods in Ecology and Evolution, 9, 1308–1317. DOI: 10.1111/2041-210X.12969.
data(woodmouse) d <- dist.dna(woodmouse, "n") (r <- mst(d)) plot(r) ## a case where the RMST and the MJN are identical: x <- c(">A", "TAAGTGCAT", ">B", "TAAATGCAT", ">C", "TAGGTGCAT", ">D", "TAAGTACAT", ">E", "TAAGTGTAT", ">F", "TAAGTACAC", ">G", "TAAGTACGT", ">H", "CAAGTACAC", ">I", "CAAGCACAC", ">J", "CAAGTACAT", ">K", "CGAGTACAT", ">L", "TAAGTACGC", ">M", "CAAGCACAT") fl <- tempfile() cat(x, file = fl, sep = "\n") x <- read.dna(fl, "f") tr <- rmst(dist.dna(x, "n")) ts <- mjn(x) stopifnot(all.equal(tr, ts)) unlink(fl)data(woodmouse) d <- dist.dna(woodmouse, "n") (r <- mst(d)) plot(r) ## a case where the RMST and the MJN are identical: x <- c(">A", "TAAGTGCAT", ">B", "TAAATGCAT", ">C", "TAGGTGCAT", ">D", "TAAGTACAT", ">E", "TAAGTGTAT", ">F", "TAAGTACAC", ">G", "TAAGTACGT", ">H", "CAAGTACAC", ">I", "CAAGCACAC", ">J", "CAAGTACAT", ">K", "CGAGTACAT", ">L", "TAAGTACGC", ">M", "CAAGCACAT") fl <- tempfile() cat(x, file = fl, sep = "\n") x <- read.dna(fl, "f") tr <- rmst(dist.dna(x, "n")) ts <- mjn(x) stopifnot(all.equal(tr, ts)) unlink(fl)
mutations draws annotations about mutations related to the link
of a haplotype network.
mutations(haploNet, link, x, y, data = NULL, style = "table", POS, SEQLEN, ...)mutations(haploNet, link, x, y, data = NULL, style = "table", POS, SEQLEN, ...)
haploNet |
an object of class |
link |
the link number; can be left missing in which case the list of links in the network is printed and the function exits. |
x, y
|
the coordinates where to draw the annotations; can be left missing: the user is then asked to click where to draw them and the chosen coordinates are printed. |
data |
the sequence data; can be left missing if the data are
attached to the network (for a MJN network output by
|
style |
the type annotations. There two possible choices:
|
POS, SEQLEN
|
a vector of genomic positions and the sequence
length in case |
... |
options |
The easiest way to use this function is with an output from
mjn since the data are attached to the network. In other
cases, the sequence data must given to the argument data or
attached to the network as an attribute named "data".
none
Emmanuel Paradis
## simple example x <- as.DNAbin(matrix(c("a", "g"), 2, 1)) rownames(x) <- paste("Ind", 1:2, sep = "_") nt <- mst(dist.dna(x, "N")) plot(nt) mutations(nt, link = 1, x = 2, y = 2, data = x) example(mjn) plot(ntz, xlim = c(-5, 20)) mutations(ntz, 6, 10, 0, style = "s") mutations(ntz, 8, 10, -2, style = "s")## simple example x <- as.DNAbin(matrix(c("a", "g"), 2, 1)) rownames(x) <- paste("Ind", 1:2, sep = "_") nt <- mst(dist.dna(x, "N")) plot(nt) mutations(nt, link = 1, x = 2, y = 2, data = x) example(mjn) plot(ntz, xlim = c(-5, 20)) mutations(ntz, 6, 10, 0, style = "s") mutations(ntz, 8, 10, -2, style = "s")
The first function is a method of the generic function
na.omit.
nullAlleles2NA changes all genotypes with at least one ‘null’
allele (that is among the values in na.alleles) into NA.
## S3 method for class 'loci' na.omit(object, na.alleles = c("0", "."), ...) nullAlleles2NA(object, na.alleles = c("0", "."))## S3 method for class 'loci' na.omit(object, na.alleles = c("0", "."), ...) nullAlleles2NA(object, na.alleles = c("0", "."))
object |
an object of class |
na.alleles |
a vector of character strings giving the alleles to be treated as missing data. |
... |
(unused) |
The side effect of na.omit is to drop the rows (individuals)
with unclearly identified genotypes, i.e., with at least one allele
among na.alleles.
Other variables in the data table are eventually checked and levels with no observation (e.g., population) are dropped.
nullAlleles2NA does not remove any observation but changes
these genotypes into NA.
an object of class "loci".
Emmanuel Paradis
data(jaguar) nrow(jaguar) nrow(na.omit(jaguar)) nrow(nullAlleles2NA(jaguar))data(jaguar) nrow(jaguar) nrow(na.omit(jaguar)) nrow(nullAlleles2NA(jaguar))
This function computes the nucleotide diversity from a sample of DNA sequences or a set of haplotypes.
nuc.div(x, ...) ## S3 method for class 'DNAbin' nuc.div(x, variance = FALSE, pairwise.deletion = FALSE, ...) ## S3 method for class 'haplotype' nuc.div(x, variance = FALSE, pairwise.deletion = FALSE, ...)nuc.div(x, ...) ## S3 method for class 'DNAbin' nuc.div(x, variance = FALSE, pairwise.deletion = FALSE, ...) ## S3 method for class 'haplotype' nuc.div(x, variance = FALSE, pairwise.deletion = FALSE, ...)
x |
a matrix or a list which contains the DNA sequences. |
variance |
a logical indicating whether to compute the variance of the estimated nucleotide diversity. |
pairwise.deletion |
a logical indicating whether to delete the sites with missing data in a pairwise way. The default is to delete the sites with at least one missing data for all sequences. |
... |
further arguments to be passed. |
This is a generic function with methods for classes "DNAbin"
and "haplotype". The first method uses the sum of the number of
differences between pairs of sequences divided by the number of
comparisons (i.e. , where is the number of
sequences). The second method uses haplotype frequencies. It could be
that both methods give (slightly) different results because of missing
or ambiguous nucleotides: this is generally solved by setting
pairwise.deletion = TRUE.
The variance of the estimated diversity uses formula (10.9) from Nei
(1987). This applies only if all sequences are of the same lengths,
and cannot be used if pairwise.deletion = TRUE. A bootstrap
estimate may be in order if you insist on using the latter option.
A numeric vector with one or two values if variance = TRUE.
Emmanuel Paradis
Nei, M. (1987) Molecular evolutionary genetics. New York: Columbia University Press.
base.freq, GC.content,
theta.s, seg.sites
data(woodmouse) nuc.div(woodmouse) nuc.div(woodmouse, TRUE) nuc.div(woodmouse, FALSE, TRUE)data(woodmouse) nuc.div(woodmouse) nuc.div(woodmouse, TRUE) nuc.div(woodmouse, FALSE, TRUE)
This function plots a haplotype network using a layout calculated from an MDS performed on the pairwise distance matrix. The haplotypes have always the same positions for different networks.
plotNetMDS(net, d, k = 2, show.mutation = FALSE, col = NULL, font = 2, cex = 1)plotNetMDS(net, d, k = 2, show.mutation = FALSE, col = NULL, font = 2, cex = 1)
net |
an object of class |
d |
an object of class |
k |
the number of dimensions of the plot (2 or 3). |
show.mutation |
a logical value: if |
col |
the colours of the links; by default, semi-transparent green. |
font |
the font used to print the labels; bold by default. |
cex |
the character expansion of the labels. |
NULL
Emmanuel Paradis
Paradis, E. (2017) Analysis of haplotype networks: the randomized minimum spanning tree method. Manuscript.
data(woodmouse) d <- dist.dna(woodmouse, "n") net <- rmst(d) plotNetMDS(net, d)data(woodmouse) d <- dist.dna(woodmouse, "n") net <- rmst(d) plotNetMDS(net, d)
This function computes Ramos-Onsins and Rozas's test of neutrality for a set of DNA sequences.
R2.test(x, B = 1000, theta = 1, plot = TRUE, quiet = FALSE, ...)R2.test(x, B = 1000, theta = 1, plot = TRUE, quiet = FALSE, ...)
x |
a DNA matrix (object of class |
B |
the number of replicates used for the simulation procedure. |
theta |
the value of the |
plot |
a logical value specifying whether to plot the results
( |
quiet |
a logical value specifying whether to not display the
progress of the simulations. The default is |
... |
further arguments passed to |
a list with two elements: R2 the value of the test statistic
, and P.val the associated P-value. If
B = 0 a single value, the test statistic, is returned
The simulation procedure probably needs to be tested and improved. However the results make sense so far.
Emmanuel Paradis
Ramos-Onsins, R. and Rozas, R. (2002) Statistical properties of new neutrality tests against population growth. Molecular Biology and Evolution, 19, 2092–2100.
Sano, J. and Tachida, G. (2005) Gene genealogy and properties of test statistics of neutrality under population growth. Genetics, 169, 1687–1697.
data(woodmouse) R2.test(woodmouse, quiet = TRUE)data(woodmouse) R2.test(woodmouse, quiet = TRUE)
This function reads allelic data from a Genetix file (.gtx).
read.gtx(file)read.gtx(file)
file |
a file name specified by either a variable of mode character or a quoted string. |
A data frame with class c("loci", "data.frame").
The package adegenet has a similar function,
read.genetix, but it returns an object of
class "genind".
Emmanuel Paradis
Belkhir, K., Borsa, P., Chikhi, L., Raufaste, N. and Bonhomme, F. (1996–2004) GENETIX 4.05, logiciel sous Windows(TM) pour la genetique des populations. Laboratoire Genome, Populations, Interactions, CNRS UMR 5000, Universite de Montpellier II, Montpellier (France). https://kimura.univ-montp2.fr/genetix/
read.loci, write.loci,
read.vcf, read.genetix
require(adegenet) (X <- read.gtx(system.file("files/nancycats.gtx", package = "adegenet"))) ## compare with the example in ?read.genetixrequire(adegenet) (X <- read.gtx(system.file("files/nancycats.gtx", package = "adegenet"))) ## compare with the example in ?read.genetix
This function reads allelic data from a text file: rows are individuals, and columns are loci and optional variables. By default, the first line of the file gives the locus names. If one column is labelled ‘population’, it is taken as a population variable.
read.loci(file, header = TRUE, loci.sep = "", allele.sep = "/|", col.pop = NULL, col.loci = NULL, ...)read.loci(file, header = TRUE, loci.sep = "", allele.sep = "/|", col.pop = NULL, col.loci = NULL, ...)
file |
a file name specified by either a variable of mode character, or a quoted string. |
header |
a logical specifying whether the first line of the data
file gives the names of the loci ( |
loci.sep |
the character(s) separating the loci (columns) in the data file (a white space by default). |
allele.sep |
the character(s) separating the alleles for each locus in the data file (a forward slash by default). |
col.pop |
specifies whether one of the column of the data file identifies the population. By default, if one column is labelled ‘population’ (case-insensitive), it is taken as the population variable; otherwise an integer giving the number of the column or a character string giving its name. It is eventually renamed ‘population’ and transformed as a factor. |
col.loci |
a vector of integers or characters specifying the indices or the names of the columns that are loci. By default, all columns are taken as loci except the population one, if present or specified. |
... |
further arguments passed to |
The rownames of the returned object identify the individual genotypes;
they are either taken from the data file if present, or given the
values "1", "2", ... Similarly for the colnames: if
absent in the file (in which case header = FALSE must be set),
they are given the values "V1", "V2", ...
In the returned genotypes, alleles are separated by "/", even
if it is not the case in the data file.
The vignette “Reading Genetic Data Files Into R with adegenet
and pegas” explains how to read various file formats including
Excel files (type vignette("ReadingFiles") in R).
A data frame with class c("loci", "data.frame"). It is a data
frame with an attribute "locicol" specifying the columns that
must be treated as loci. The latter are factors. The other columns can
be of any type.
Details on the structure can be found in https://emmanuelparadis.github.io/pegas/DefinitionDataClassesPegas.pdf
Emmanuel Paradis
read.gtx, read.vcf,
write.loci, summary.loci
read.vcf reads allelic data from VCF (variant calling format)
files.
write.vcf writes allelic data from an object of class
"loci" into a VCF file.
read.vcf(file, from = 1, to = 10000, which.loci = NULL, quiet = FALSE) write.vcf(x, file, CHROM = NULL, POS = NULL, quiet = FALSE)read.vcf(file, from = 1, to = 10000, which.loci = NULL, quiet = FALSE) write.vcf(x, file, CHROM = NULL, POS = NULL, quiet = FALSE)
file |
a file name specified by either a variable of mode character, or a quoted string. |
from, to
|
the loci to read; by default, the first 10,000. |
which.loci |
an alternative way to specify which loci to read is
to give their indices (see |
quiet |
a logical: should the progress of the operation be printed? |
x |
an object of class |
CHROM, POS
|
two vectors giving the chromosomes and (genomic)
positions of the loci (typically from the output of |
The VCF file can be compressed (*.gz) or not. Since pegas 0.11, compressed remote files can be read (see examples).
A TABIX file is not required (and will be ignored if present).
In the VCF standard, missing data are represented by a dot and these
are read “as is” by the present function without trying to
substitute by NA.
an object of class c("loci", "data.frame").
Like for VCFloci, the present function can read either
compressed (*.gz) or uncompressed files. There should be no difference
in performance between both types of files if they are relatively
small (less than 1 Gb as uncompressed, equivalent to ~50 Mb when
compressed). For bigger files, it is more efficient to uncompress them
(if disk space is sufficient), especially if they have to be accessed
several times during the same session.
Emmanuel Paradis
https://www.internationalgenome.org/wiki/Analysis/vcf4.0
https://github.com/samtools/hts-specs
VCFloci, read.loci,
read.gtx, write.loci
## Not run: ## Chr Y from the 1000 Genomes: a <- "https://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502" b <- "ALL.chrY.phase3_integrated_v1b.20130502.genotypes.vcf.gz" ## WARNING: the name of the file above may change url <- paste(a, b, sep = "/") ## Solution 1: download first download.file(url, "chrY.vcf.gz") ## no need to uncompress: (info <- VCFloci("chrY.vcf.gz")) str(info) # show the modes of the columns ## Solution 2: read remotely (since pegas 0.11) info2 <- VCFloci(url) identical(info, info2) rm(info2) SNP <- is.snp(info) table(SNP) # how many loci are SNPs? ## compare with: table(getINFO(info, "VT")) op <- par(mfcol = c(4, 1), xpd = TRUE) lim <- c(2.65e6, 2.95e6) ## distribution of SNP and non-SNP mutations along the Y chr: plot(info$POS, !SNP, "h", col = "red", main = "non-SNP mutations", xlab = "Position", ylab = "", yaxt = "n") rect(lim[1], -0.1, lim[2], 1.1, lwd = 2, lty = 2) plot(info$POS, SNP, "h", col = "blue", main = "SNP mutations", xlab = "Position", ylab = "", yaxt = "n") rect(lim[1], -0.1, lim[2], 1.1, lwd = 2, lty = 2) par(xpd = FALSE) ## same focusing on a smaller portion of the chromosome: plot(info$POS, !SNP, "h", col = "red", xlim = lim, xlab = "Position", ylab = "", yaxt = "n") plot(info$POS, SNP, "h", col = "blue", xlim = lim, xlab = "Position", ylab = "", yaxt = "n") par(op) ## read both types of mutations separately: X.SNP <- read.vcf("chrY.vcf.gz", which.loci = which(SNP)) X.other <- read.vcf("chrY.vcf.gz", which.loci = which(!SNP)) identical(rownames(X.SNP), VCFlabels("chrY.vcf.gz")) # TRUE cat(VCFheader("chrY.vcf.gz")) ## get haplotypes for the first 10 loci: h <- haplotype(X.SNP, 1:10) ## plot their frequencies: op <- par(mar = c(3, 10, 1, 1)) plot(h, horiz=TRUE, las = 1) par(op) ## End(Not run)## Not run: ## Chr Y from the 1000 Genomes: a <- "https://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502" b <- "ALL.chrY.phase3_integrated_v1b.20130502.genotypes.vcf.gz" ## WARNING: the name of the file above may change url <- paste(a, b, sep = "/") ## Solution 1: download first download.file(url, "chrY.vcf.gz") ## no need to uncompress: (info <- VCFloci("chrY.vcf.gz")) str(info) # show the modes of the columns ## Solution 2: read remotely (since pegas 0.11) info2 <- VCFloci(url) identical(info, info2) rm(info2) SNP <- is.snp(info) table(SNP) # how many loci are SNPs? ## compare with: table(getINFO(info, "VT")) op <- par(mfcol = c(4, 1), xpd = TRUE) lim <- c(2.65e6, 2.95e6) ## distribution of SNP and non-SNP mutations along the Y chr: plot(info$POS, !SNP, "h", col = "red", main = "non-SNP mutations", xlab = "Position", ylab = "", yaxt = "n") rect(lim[1], -0.1, lim[2], 1.1, lwd = 2, lty = 2) plot(info$POS, SNP, "h", col = "blue", main = "SNP mutations", xlab = "Position", ylab = "", yaxt = "n") rect(lim[1], -0.1, lim[2], 1.1, lwd = 2, lty = 2) par(xpd = FALSE) ## same focusing on a smaller portion of the chromosome: plot(info$POS, !SNP, "h", col = "red", xlim = lim, xlab = "Position", ylab = "", yaxt = "n") plot(info$POS, SNP, "h", col = "blue", xlim = lim, xlab = "Position", ylab = "", yaxt = "n") par(op) ## read both types of mutations separately: X.SNP <- read.vcf("chrY.vcf.gz", which.loci = which(SNP)) X.other <- read.vcf("chrY.vcf.gz", which.loci = which(!SNP)) identical(rownames(X.SNP), VCFlabels("chrY.vcf.gz")) # TRUE cat(VCFheader("chrY.vcf.gz")) ## get haplotypes for the first 10 loci: h <- haplotype(X.SNP, 1:10) ## plot their frequencies: op <- par(mar = c(3, 10, 1, 1)) plot(h, horiz=TRUE, las = 1) par(op) ## End(Not run)
This function makes possible to change the layout of a haplotype network interactively or with specified coordinates.
replot(xy = NULL, col.identifier = "purple", ...)replot(xy = NULL, col.identifier = "purple", ...)
xy |
an optional list with vectors names |
col.identifier |
the colour used to identify the node to be moved. |
... |
further arguments passed to |
This function can be used in two ways. By default (i.e.,
replot()), the user can edit a plotted haplotype network by
clicking with the mouse on the graphical window: a message is printed
asking to click once close to the node to move and then clicking again
where this node should be placed (careful: two separate single
clicks). Editing is stopped with a right click.
The second possible use is to specify the new coordinates of the nodes
with the argument xy, typically, from a previous call to
replot (see examples).
Since pegas 1.0, these coordinates can be used directly in
plot.haploNet making possible to combine networks with
other graphics (which not possible with replot because the
network is replotted).
a named list with two numeric vertors (x and y).
For users of RStudio: the function does not work within this
application. It seems the best is to run R from a shell (or maybe
opening a new graphical device with X11).
Emmanuel Paradis
## a non-interactive example: example(mjn) layout(matrix(1:2, 1)) plot(ntz, labels = TRUE) ## it is possible plot this network with no line-crossing ## with these coordinates: xy <- list(x = c(3.2, -2.6, -6.6, -7.2, 0, 3.5, 2.6, -2.9, -0.3, 3.4, -3.4), y = c(3.4, 4.4, 1.3, -3.9, -5.5, -10.9, 0.1, -0.8, -2.3, -7.9, -8.1)) replot(ntz, xy = xy) # or plot(ntz, xy = xy, labels = TRUE) layout(1) ## an interactive example: ## Not run: data(woodmouse) net <- haploNet(haplotype(woodmouse)) plot(net) o <- replot() # interactive ## click to rearrange the network at will... ## then do a different plot using the same coordinates: plot(net, bg = "red", labels = FALSE, show.mutation = 2) replot(o) # not interactive ## End(Not run)## a non-interactive example: example(mjn) layout(matrix(1:2, 1)) plot(ntz, labels = TRUE) ## it is possible plot this network with no line-crossing ## with these coordinates: xy <- list(x = c(3.2, -2.6, -6.6, -7.2, 0, 3.5, 2.6, -2.9, -0.3, 3.4, -3.4), y = c(3.4, 4.4, 1.3, -3.9, -5.5, -10.9, 0.1, -0.8, -2.3, -7.9, -8.1)) replot(ntz, xy = xy) # or plot(ntz, xy = xy, labels = TRUE) layout(1) ## an interactive example: ## Not run: data(woodmouse) net <- haploNet(haplotype(woodmouse)) plot(net) o <- replot() # interactive ## click to rearrange the network at will... ## then do a different plot using the same coordinates: plot(net, bg = "red", labels = FALSE, show.mutation = 2) replot(o) # not interactive ## End(Not run)
This function tests the hypothesis of a molecular evolutionary clock (i.e., a constant rate of molecular evolution) between two samples using an outgroup sample. It can be applied to both nucleotide and amino acid sequences.
rr.test(x, y, out)rr.test(x, y, out)
x, y
|
a single DNA sequence (object class |
out |
a single DNA sequence to be used as outgroup. |
a list with two numeric values: Chi (Chi-squared statistic) and
Pval (the P-value).
Alastair Potts [email protected]
Tajima, F. (1993) Simple methods for testing molecular clock hypothesis. Genetics, 135, 599–607. (Equation 4)
require(ape) data(woodmouse) rr.test(x = woodmouse[2, ], y = woodmouse[3, ], out = woodmouse[1, ]) # Test all pairs in a sample: outgroup <- woodmouse[1, ] n <- nrow(woodmouse) cc <- combn(2:n, 2) FUN <- function(x) rr.test(woodmouse[x[1], ], woodmouse[x[2], ], outgroup)$Pval OUT <- apply(cc, 2, FUN) ### two ways to arrange the output: RES <- matrix(NA, n - 1, n - 1) RES[row(RES) > col(RES)] <- OUT RES <- t(RES) RES[row(RES) > col(RES)] <- OUT RES <- t(RES) dimnames(RES) <- list(2:n, 2:n) RES <- as.dist(RES) ### 2nd method: class(OUT) <- "dist" attr(OUT, "Labels") <- as.character(2:15) attr(OUT, "Size") <- n - 1L attr(OUT, "Diag") <- attr(OUT, "Upper") <- FALSE ### they are the same: all(OUT == RES)require(ape) data(woodmouse) rr.test(x = woodmouse[2, ], y = woodmouse[3, ], out = woodmouse[1, ]) # Test all pairs in a sample: outgroup <- woodmouse[1, ] n <- nrow(woodmouse) cc <- combn(2:n, 2) FUN <- function(x) rr.test(woodmouse[x[1], ], woodmouse[x[2], ], outgroup)$Pval OUT <- apply(cc, 2, FUN) ### two ways to arrange the output: RES <- matrix(NA, n - 1, n - 1) RES[row(RES) > col(RES)] <- OUT RES <- t(RES) RES[row(RES) > col(RES)] <- OUT RES <- t(RES) dimnames(RES) <- list(2:n, 2:n) RES <- as.dist(RES) ### 2nd method: class(OUT) <- "dist" attr(OUT, "Labels") <- as.character(2:15) attr(OUT, "Size") <- n - 1L attr(OUT, "Diag") <- attr(OUT, "Upper") <- FALSE ### they are the same: all(OUT == RES)
site.spectrum computes the (un)folded site frequency spectrum
of a set of aligned DNA sequences or SNPs.
site.spectrum(x, ...) ## S3 method for class 'DNAbin' site.spectrum(x, folded = TRUE, outgroup = 1, ...) ## S3 method for class 'loci' site.spectrum(x, folded = TRUE, ancestral = NULL, ...) ## S3 method for class 'spectrum' plot(x, col = "red", main = NULL, ...)site.spectrum(x, ...) ## S3 method for class 'DNAbin' site.spectrum(x, folded = TRUE, outgroup = 1, ...) ## S3 method for class 'loci' site.spectrum(x, folded = TRUE, ancestral = NULL, ...) ## S3 method for class 'spectrum' plot(x, col = "red", main = NULL, ...)
x |
a set of DNA sequences (as an object of class
|
folded |
a logical specifying whether to compute the folded site
frequency spectrum (the default), or the unfolded spectrum if
|
outgroup |
a single integer value giving which sequence is
ancestral; ignored if |
ancestral |
a vector of ancestral alleles (required if
|
col |
the colour of the barplot (red by default). |
main |
a character string for the title of the plot; a generic
title is given by default (use |
... |
further arguments passed to
|
Under the infinite sites model of mutation, mutations occur on
distinct sites, so every segregating (polymorphic) site defines a
partition of the sequences (see Wakeley, 2009). The site
frequency spectrum is a series of values where the th element
is the number of segregating sites defining a partition of and
sequences. The unfolded version requires to define
an ancestral state with an external (outgroup) sequence, so
varies between 1 and . If no ancestral state can be
defined, the folded version is computed, so varies
between 1 and or , for even or odd,
respectively.
If folded = TRUE, sites with more than two states are ignored
and a warning is returned giving how many were found.
If folded = FALSE, sites with an ambiguous state at the
external sequence are ignored and a warning is returned giving how
many were found. Note that it is not checked if some sites have more
than two states.
If x is an object of class "loci", the loci which are
not biallelic (e.g., SNPs) are dropped with a warning.
site.spectrum returns an object of class "spectrum"
which is a vector of integers (some values may be equal to zero) with
the attributes "sample.size" and "folded" (a logical
value) indicating which version of the spectrum has been computed.
Emmanuel Paradis
Wakeley, J. (2009) Coalescent Theory: An Introduction. Greenwood Village, CO: Roberts and Company Publishers.
DNAbin for manipulation of DNA sequences in R,
haplotype
require(ape) data(woodmouse) (sp <- site.spectrum(woodmouse)) plot(sp)require(ape) data(woodmouse) (sp <- site.spectrum(woodmouse)) plot(sp)
This function fits a model of population change using the site
frequency spectrum (SFS). The default assumes . A model of population change estimates the temporal changes in
with respect to the value of this parameter at
present time. The model is specified by the user with the option
epoch.
stairway(x, epoch = NULL, step.min = 1e-6, step.max = 1e-3) ## S3 method for class 'stairway' plot(x, type = "S", xlab = "Coalescent intervals", ylab = expression(Theta), ...) ## S3 method for class 'stairway' lines(x, type = "S", ...)stairway(x, epoch = NULL, step.min = 1e-6, step.max = 1e-3) ## S3 method for class 'stairway' plot(x, type = "S", xlab = "Coalescent intervals", ylab = expression(Theta), ...) ## S3 method for class 'stairway' lines(x, type = "S", ...)
x |
an object of class |
epoch |
an optional vector of integers giving the periods of time
(or epochs) with distinct |
step.min |
a single numeric value giving the smallest step size used during optimization. |
step.max |
id. for the largest step size (see
|
type |
the type of lines. |
xlab, ylab
|
the default labels on the axes. |
... |
further arguments passed to other methods. |
The basic method implemented in this function is similar to Polanski and Kimmel (2003). The temporal model with “epochs” is from Liu and Fu (2015).
By default, a single numeric value with the null deviance. If
epoch is used, a list with the following components:
estimates |
the maximum likelihood estimates. |
deviance |
the deviance of the fitted model. |
null.deviance |
the deviance of the null model. |
LRT |
the likelihood-ratio test comparing the null and the fitted models. |
AIC |
the Akaike information criterion of the fitted model. |
Emmanuel Paradis
Liu, X. M. and Fu, Y. X. (2015) Exploring population size changes using SNP frequency spectra. Nature Genetics, 47, 555–559.
Polanski, A. and Kimmel, M. (2003) New explicit expressions for relative frequencies of single-nucleotide polymorphisms with application to statistical inference on population growth. Genetics, 165, 427–436.
data(woodmouse) sp <- site.spectrum(woodmouse) stairway(sp, c(1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2))data(woodmouse) sp <- site.spectrum(woodmouse) stairway(sp, c(1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2))
This function selects haplotypes based on their (absolute) frequencies and/or proportions of missing nucleotides.
## S3 method for class 'haplotype' subset(x, minfreq = 1, maxfreq = Inf, maxna = Inf, na = c("N", "?"), ...)## S3 method for class 'haplotype' subset(x, minfreq = 1, maxfreq = Inf, maxna = Inf, na = c("N", "?"), ...)
x |
an object of class |
minfreq, maxfreq
|
the lower and upper limits of (absolute) haplotype frequencies. By default, all haplotypes are selected whatever their frequency. |
maxna |
the maximum frequency (absolute or relative; see details) of missing nucleotides within a given haplotype. |
na |
a vector of mode character specifying which nucleotide symbols should be treated as missing data; by default, unknown nucleotide (N) and completely unknown site (?) (can be lower- or uppercase). There are two shortcuts: see details. |
... |
unused. |
The value of maxna can be either less than one, or greater or
equal to one. In the former case, it is taken as specifying the
maximum proportion (relative frequency) of missing data within a given
haplotype. In the latter case, it is taken as the maximum number
(absolute frequency).
na = "all" is a shortcut for all ambiguous nucleotides
(including N) plus alignment gaps and completely unknown site (?).
na = "ambiguous" is a shortcut for only ambiguous nucleotides
(including N).
an object of class c("haplotype", "DNAbin").
Emmanuel Paradis
data(woodmouse) h <- haplotype(woodmouse) subset(h, maxna = 20) subset(h, maxna = 20/ncol(h)) # same thing than abovedata(woodmouse) h <- haplotype(woodmouse) subset(h, maxna = 20) subset(h, maxna = 20/ncol(h)) # same thing than above
These functions print and summarize table of alleles and loci (objects
of class "loci").
## S3 method for class 'loci' print(x, details = FALSE, ...) ## S3 method for class 'loci' summary(object, ...) ## S3 method for class 'summary.loci' print(x, ...) ## S3 method for class 'loci' x[i, j, drop = FALSE] ## S3 method for class 'summary.loci' plot(x, loci, what = "both", layout = 1, col = c("blue", "red"), ...)## S3 method for class 'loci' print(x, details = FALSE, ...) ## S3 method for class 'loci' summary(object, ...) ## S3 method for class 'summary.loci' print(x, ...) ## S3 method for class 'loci' x[i, j, drop = FALSE] ## S3 method for class 'summary.loci' plot(x, loci, what = "both", layout = 1, col = c("blue", "red"), ...)
x, object
|
an object of class |
details |
a logical value: if |
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 |
a logical specifying whether to returned an object of
the smallest dimension possible, i.e., may return a vector or a
factor if |
loci |
the loci (genes) to be plotted. By default, all loci are plotted. |
what |
the frequencies to be plotted. Three choices are possible:
|
layout |
the number of graphs to be plotted simultaneously. |
col |
the colours used for the barplots. |
... |
further arguments to be passed to or from other methods. |
Genotypes not observed in the data frame are not counted.
When using the [ method, if only one column is extracted and
the option drop = TRUE, or if the returned data frame has no ‘locus’
column, then the class "loci" is dropped. The option drop = FALSE
(default) keeps the class (see examples).
An object of class "loci" can be edited in the R data editor
with, e.g., fix(x) or x <- edit(x).
summary.loci computes the absolute frequencies (counts); see
the examples on how to compute the relative frequencies (proportions).
summary.loci returns a list with the genes as names and each
element made a list with two vectors "genotype" and
"allele" with the frequencies (numbers) of genotypes and
alleles, respectively. The names of these two vectors are the observed
genotypes and alleles.
print and plot methods return NULL.
Emmanuel Paradis
read.loci, getAlleles, edit.loci
data(jaguar) s <- summary(jaguar) ## Not run: ## works if the device is large enough: plot(s, layout = 30, las = 2) layout(1) ## End(Not run) ## compute the relative frequencies: rapply(s, function(x) x/sum(x), how = "replace") ## extract a single locus: jaguar[, 1] jaguar[, 1, drop = TRUE] # returns a vector jaguar[[1]] # also returns a vectordata(jaguar) s <- summary(jaguar) ## Not run: ## works if the device is large enough: plot(s, layout = 30, las = 2) layout(1) ## End(Not run) ## compute the relative frequencies: rapply(s, function(x) x/sum(x), how = "replace") ## extract a single locus: jaguar[, 1] jaguar[, 1, drop = TRUE] # returns a vector jaguar[[1]] # also returns a vector
Applies a function over a matrix or a vector using sliding
windows. sw is a generic function with a method for
"DNAbin" matrices.
sw(x, width, step, ...) ## Default S3 method: sw(x, width = 100, step = 50, POS = NULL, FUN = mean, out.of.pos = NA_real_, na.rm = TRUE, L = NULL, ...) ## S3 method for class 'DNAbin' sw(x, width = 100, step = 50, FUN = GC.content, rowAverage = FALSE, quiet = TRUE, ...) ## S3 method for class 'sw' plot(x, type = "l", xlab = "Position", x.scaling = 1, show.ranges = FALSE, col.ranges = "blue", lty.ranges = 1, lwd.ranges = 1, ...)sw(x, width, step, ...) ## Default S3 method: sw(x, width = 100, step = 50, POS = NULL, FUN = mean, out.of.pos = NA_real_, na.rm = TRUE, L = NULL, ...) ## S3 method for class 'DNAbin' sw(x, width = 100, step = 50, FUN = GC.content, rowAverage = FALSE, quiet = TRUE, ...) ## S3 method for class 'sw' plot(x, type = "l", xlab = "Position", x.scaling = 1, show.ranges = FALSE, col.ranges = "blue", lty.ranges = 1, lwd.ranges = 1, ...)
x |
a vector or a matrix. |
width |
an integer giving the window width. |
step |
an integer giving the step separating successive windows. |
POS |
a numeric vector giving the positions of the sites. |
FUN |
the function to be applied to the windows. |
rowAverage |
a logical value: if |
out.of.pos |
the values used for the sites which are not in
|
na.rm |
option passed to |
L |
the length of the chromosome (or sequence). If not given,
this is largest value in |
quiet |
a logical value: if |
type |
the type of plotting (see |
xlab |
the label under the x-axis. |
x.scaling |
the scaling of the x-axis. |
show.ranges |
a logical value specifying whether to show the
ranges of the windows with horizontal segments (ignored with a
warning if |
col.ranges, lty.ranges, lwd.ranges
|
arguments to modify the
appearance of the above segments (see |
... |
further arguments passed to and from methods. |
FUN should return a single value.
x should be a matrix for the "DNAbin" method, or a
vector for the default one.
For the default method, the vector x is expanded into a vector
of length L (see above on how this value is found) and the
positions which are not in POS are filled with the value given
in out.of.pos. The resulting vector is then analysed with the
function FUN which must have an option na.rm. If the
function you want to use does not have this option, you can use
something like FUN = function(x, na.rm = TRUE)
foo(x[!is.na(x)]), replacing ‘foo’ by the name of your function. You
may also include more control on the handling of missing data.
a matrix or a vector (if rowAverage = TRUE).
Emmanuel Paradis
data(woodmouse) sw(woodmouse) sw(woodmouse, 200, 200) sw(woodmouse, 200, 200, rowAverage = TRUE) ## to get the proportions of G: foo <- function(x) base.freq(x)["g"] sw(woodmouse, 200, 200, FUN = foo, rowAverage = TRUE) ## a simulated example with the default method: x <- runif(100) pos <- sort(sample(1e6, 100)) resx <- sw(x, w = 2e4, s = 5e3, POS = pos, L = 1e6) plot(resx, show.ranges = TRUE, x.scaling = 1e6, xlab = "Position (Mb)")data(woodmouse) sw(woodmouse) sw(woodmouse, 200, 200) sw(woodmouse, 200, 200, rowAverage = TRUE) ## to get the proportions of G: foo <- function(x) base.freq(x)["g"] sw(woodmouse, 200, 200, FUN = foo, rowAverage = TRUE) ## a simulated example with the default method: x <- runif(100) pos <- sort(sample(1e6, 100)) resx <- sw(x, w = 2e4, s = 5e3, POS = pos, L = 1e6) plot(resx, show.ranges = TRUE, x.scaling = 1e6, xlab = "Position (Mb)")
This function tests the neutral mutation hypothesis with Tajima's D.
tajima.test(x)tajima.test(x)
x |
a set of DNA sequences (object of class |
A list with three numeric values:
D |
Tajima's D statistic. |
Pval.normal |
the p-value assuming that D follows a normal distribution with mean zero and variance one. |
Pval.beta |
the p-value assuming that D follows a beta distribution after rescaling on [0, 1] (Tajima, 1989). |
Alignment gaps in the sequences are ignored when calculating pairwise distances.
Emmanuel Paradis
Tajima, F. (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123, 595–595.
require(ape) data(woodmouse) tajima.test(woodmouse)require(ape) data(woodmouse) tajima.test(woodmouse)
This function computes the population parameter THETA using the homozygosity (or mean heterozygosity) from gene frequencies.
theta.h(x, standard.error = FALSE)theta.h(x, standard.error = FALSE)
x |
a vector or a factor. |
standard.error |
a logical indicating whether the standard error
of the estimated theta should be returned ( |
The argument x can be either a factor or a vector. If it is a
factor, then it is taken to give the individual alleles in the
population. If it is a numeric vector, then its values are taken to be
the numbers of each allele in the population. If it is a non-numeric
vector, it is a coerced as a factor.
The standard error is computed with an approximation due to Chakraborty and Weiss (1991).
A numeric vector of length one with the estimated theta (the default),
or of length two if the standard error is returned
(standard.error = TRUE).
Emmanuel Paradis
Zouros, E. (1979) Mutation rates, population sizes and amounts of electrophoretic variation at enzyme loci in natural populations. Genetics, 92, 623–646.
Chakraborty, R. and Weiss, K. M. (1991) Genetic variation of the mitochondrial DNA genome in American Indians is at mutation-drift equilibrium. American Journal of Physical Anthropology, 86, 497–506.
heterozygosity, theta.s,
theta.k, theta.tree
data(jaguar) ## compute frequencies: S <- summary(jaguar) ## compute THETA for all loci: sapply(S, function(x) theta.h(x$allele))data(jaguar) ## compute frequencies: S <- summary(jaguar) ## compute THETA for all loci: sapply(S, function(x) theta.h(x$allele))
This function computes the population parameter THETA using the expected number of alleles.
theta.k(x, n = NULL, k = NULL)theta.k(x, n = NULL, k = NULL)
x |
a vector or a factor. |
n |
a numeric giving the sample size. |
k |
a numeric giving the number of alleles. |
This function can be used in two ways: either with a vector giving the
individual genotypes from which the sample size and number of alleles
are derived (e.g., theta.k(x)), or giving directly these two
quantities (e.g., theta.k(n = 50, k = 5)).
The argument x can be either a factor or a vector. If it is a
factor, then it is taken to give the individual alleles in the
population. If it is a numeric vector, then its values are taken to be
the numbers of each allele in the population. If it is a non-numeric
vector, it is a coerced as a factor.
Both arguments n and k must be single numeric values.
A numeric vector of length one with the estimated theta.
For the moment, no standard-error or confidence interval is computed.
Emmanuel Paradis
Ewens, W. J. (1972) The sampling theory of selectively neutral alleles. Theoretical Population Biology, 3, 87–112.
data(jaguar) ## compute frequencies: S <- summary(jaguar) ## compute THETA for all loci: sapply(S, function(x) theta.k(x$allele))data(jaguar) ## compute frequencies: S <- summary(jaguar) ## compute THETA for all loci: sapply(S, function(x) theta.k(x$allele))
This function estimates the population parameter
using micro-satellite data with three different estimators.
theta.msat(x)theta.msat(x)
x |
an object of class |
The three estimators are based on (i) the variance of the number of repeats, (ii) the expected homozygosity (both described in Kimmel et al., 1998), and (iii) the mean allele frequencies (Haasl and Payseur, 2010).
The data must be micro-satellites, so the allele names must be the allele sizes (see the example). If the data are expressed in repeat counts, then only the first estimator is affected.
a numeric matrix with loci as rows and the three estimates of
as columns.
Emmanuel Paradis
Kimmel, M., Chakraborty, R., King, J. P., Bamshad, M., Watkins, W. S. and Jorde, L. B. (1998) Signatures of population expansion in microsatellite repeat data. Genetics, 148, 1921–1930.
Haasl, R. J. and Payseur, B. A. (2010) The number of alleles at a
microsatellite defines the allele frequency spectrum and facilitates
fast accurate estimation of . Molecular
Biology and Evolution, 27, 2702–2715.
data(jaguar) theta.msat(jaguar)data(jaguar) theta.msat(jaguar)
This function computes the population parameter THETA using the
number of segregating sites in a sample of DNA sequences.
theta.s(x, ...) ## S3 method for class 'DNAbin' theta.s(x, variance = FALSE, ...) ## Default S3 method: theta.s(x, n, variance = FALSE, ...)theta.s(x, ...) ## S3 method for class 'DNAbin' theta.s(x, variance = FALSE, ...) ## Default S3 method: theta.s(x, n, variance = FALSE, ...)
x |
a numeric giving the number of segregating sites. |
n |
a numeric giving the number of sequences. |
variance |
a logical indicating whether the variance of the
estimated THETA should be returned ( |
... |
arguments passed to methods. |
A numeric vector of length one with the estimated theta (the default),
or of length two if the standard error is returned
(variance = TRUE).
Emmanuel Paradis
Watterson, G. A. (1975) On the number of segragating sites in genetical models without recombination. Theoretical Population Biology, 7, 256–276.
Tajima, F. (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123, 585–595.
theta.h, theta.k,
seg.sites, nuc.div, theta.tree
data(woodmouse) theta.s(woodmouse) theta.s(woodmouse, variance = TRUE) ## using the default: s <- length(seg.sites(woodmouse)) n <- nrow(woodmouse) theta.s(s, n)data(woodmouse) theta.s(woodmouse) theta.s(woodmouse, variance = TRUE) ## using the default: s <- length(seg.sites(woodmouse)) n <- nrow(woodmouse) theta.s(s, n)
These functions estimate the population parameter
from a genealogy (coded a as phylogenetic tree) under the coalescent.
theta.tree(phy, theta, fixed = FALSE, analytical = TRUE, log = TRUE) theta.tree.hetero(phy, theta, fixed = FALSE, log = TRUE)theta.tree(phy, theta, fixed = FALSE, analytical = TRUE, log = TRUE) theta.tree.hetero(phy, theta, fixed = FALSE, log = TRUE)
phy |
an object of class |
theta |
a numeric vector. |
fixed |
a logical specifying whether to estimate |
analytical |
a logical specifying whether to use analytical
formulae to estimate |
.
log |
a logical specifying whether to return the likelihoods on a
log scale (the default); ignored if |
With theta.tree, the tree phy is considered as a
genealogy with contemporaneous samples, and therefore should be
ultrametric. With theta.tree.hetero, the samples may be
heterochronous so phy can be non-ultrametric. If phy is
ultrametric, both functions return the same results.
By default, is estimated by maximum likelihood and
the value given in theta is used as starting value for the
minimisation function (if several values are given as a vector the
first one is used). If fixed = TRUE, then the [log-]likelihood
values are returned corresponding to each value in theta.
The present implementation does a numerical optimisation of the
log-likelihood function (with nlminb) with the
first partial derivative as gradient. It is possible to solve the
latter and have a direct analytical MLE of (and
its standard-error), but this does not seem to be faster.
If fixed = FALSE, a list with two elements:
theta |
the maximum likelihood estimate of |
logLik |
the log-likelihood at its maximum. |
If fixed = TRUE, a numeric vector with the [log-]likelihood
values.
Emmanuel Paradis
Kingman, J. F. C. (1982) The coalescent. Stochastic Processes and their Applications, 13, 235–248.
Kingman, J. F. C. (1982) On the genealogy of large populations. Journal of Applied Probability, 19A, 27–43.
Wakeley, J. (2009) Coalescent Theory: An Introduction. Greenwood Village, CO: Roberts and Company Publishers.
tr <- rcoal(50) (o <- theta.tree(tr)) theta.tree(tr, 10, analytical = FALSE) # uses nlminb() ## profile log-likelihood: THETA <- seq(0.5, 2, 0.01) logLikelihood <- theta.tree(tr, THETA, fixed = TRUE) plot(THETA, logLikelihood, type = "l") xx <- seq(o$theta - 1.96 * o$se, o$theta + 1.96 * o$se, 0.01) yy <- theta.tree(tr, xx, fixed = TRUE) polygon(c(xx, rev(xx)), c(yy, rep(0, length(xx))), border = NA, col = "lightblue") segments(o$theta, 0, o$theta, o$logLik, col = "blue") abline(v = 1, lty = 3) legend("topright", legend = expression("log-likelihood", "True " * theta, hat(theta) * " (MLE)", "95%\ conf. interv."), lty = c(1, 3, 1, 1), lwd = c(1, 1, 1, 15), col = c("black", "black", "blue", "lightblue"))tr <- rcoal(50) (o <- theta.tree(tr)) theta.tree(tr, 10, analytical = FALSE) # uses nlminb() ## profile log-likelihood: THETA <- seq(0.5, 2, 0.01) logLikelihood <- theta.tree(tr, THETA, fixed = TRUE) plot(THETA, logLikelihood, type = "l") xx <- seq(o$theta - 1.96 * o$se, o$theta + 1.96 * o$se, 0.01) yy <- theta.tree(tr, xx, fixed = TRUE) polygon(c(xx, rev(xx)), c(yy, rep(0, length(xx))), border = NA, col = "lightblue") segments(o$theta, 0, o$theta, o$logLik, col = "blue") abline(v = 1, lty = 3) legend("topright", legend = expression("log-likelihood", "True " * theta, hat(theta) * " (MLE)", "95%\ conf. interv."), lty = c(1, 3, 1, 1), lwd = c(1, 1, 1, 15), col = c("black", "black", "blue", "lightblue"))
The first three functions extract information on loci,
expand.genotype creates a table of all possible genotypes given
a set of alleles, proba.genotype calculates expected
probabilities of genotypes under Hardy–Weinberg equilibrium,
is.snp tests whether a locus is a SNP, is.phased tests
whether a gentotype is phased, and unphase unphase phased
genotypes.
getPloidy(x) getAlleles(x) getGenotypes(x) expand.genotype(n, alleles = NULL, ploidy = 2, matrix = FALSE) proba.genotype(alleles = c("1", "2"), p, ploidy = 2) is.snp(x) ## S3 method for class 'loci' is.snp(x) is.phased(x) unphase(x)getPloidy(x) getAlleles(x) getGenotypes(x) expand.genotype(n, alleles = NULL, ploidy = 2, matrix = FALSE) proba.genotype(alleles = c("1", "2"), p, ploidy = 2) is.snp(x) ## S3 method for class 'loci' is.snp(x) is.phased(x) unphase(x)
x |
an object of class |
n |
an integer giving how many alleles to consider (ignored if
|
alleles |
the allele names as a vector of mode character. |
ploidy |
an integer giving the ploidy level (either 2 or 4 for the moment). |
matrix |
a logical specifying whether to return the genotypes in a matrix or as a character vector. |
p |
a vector of allele probabilities; if missing, equal probabilities are assumed. |
expand.genotype and proba.genotype accept any level of
ploidy and any number of alleles.
For is.snp, a locus is defined as a SNP if it has two alleles
and their labels are made of a single character (e.g., A and T, or 1
and 2, but not A and AT).
getPloidy returns the ploidy level of all genotypes as a matrix
of integers with rownames and colnames taken from x.
getAlleles and getGenotypes return the alleles and
genotypes, respectively, observed in all loci in an object of class
"loci" as a list.
expand.genotype returns a character vector (the default) or a
matrix where the rows are the genotypes and the columns are the
alleles. The matrix is numeric by default, or character if the
argument alleles is given.
proba.genotype returns a numeric vector with names set as the
genotypes.
is.snp returns a logical vector specifying whether each locus
is a SNP.
is.phased returns a matrix of the same size than the original
data specifying whether each genotype is phased or not.
unphase unphases the genotypes and eventually pools those that
become identical once unphased (e.g., A|T and T|A).
Emmanuel Paradis
data(jaguar) X <- jaguar[, 1:2] getAlleles(X) getGenotypes(X) expand.genotype(2) expand.genotype(2, LETTERS[1:3]) expand.genotype(3, ploidy = 4) proba.genotype() # classical HWE with 2 alleles ## an octoploid with a six-allele locus (1287 possible genotypes): length(p <- proba.genotype(alleles = LETTERS[1:6], ploidy = 8)) max(p) # ~ 0.006 ## back to the jaguar data: s <- summary(X) ## allele counts from the first locus: p <- s[[1]]$allele ## expected probabilities for the 136 possible genotypes... proba.genotype(names(p), p/sum(p)) ## ... to be compared with s[[1]]$genotypedata(jaguar) X <- jaguar[, 1:2] getAlleles(X) getGenotypes(X) expand.genotype(2) expand.genotype(2, LETTERS[1:3]) expand.genotype(3, ploidy = 4) proba.genotype() # classical HWE with 2 alleles ## an octoploid with a six-allele locus (1287 possible genotypes): length(p <- proba.genotype(alleles = LETTERS[1:6], ploidy = 8)) max(p) # ~ 0.006 ## back to the jaguar data: s <- summary(X) ## allele counts from the first locus: p <- s[[1]]$allele ## expected probabilities for the 136 possible genotypes... proba.genotype(names(p), p/sum(p)) ## ... to be compared with s[[1]]$genotype
These functions help to extract information from VCF files and to
select which loci to read with read.vcf.
VCFloci(file, what = "all", chunk.size = 1e9, quiet = FALSE) ## S3 method for class 'VCFinfo' print(x, ...) VCFheader(file) VCFlabels(file) ## S3 method for class 'VCFinfo' is.snp(x) rangePOS(x, from, to) selectQUAL(x, threshold = 20) getINFO(x, what = "DP", as.is = FALSE)VCFloci(file, what = "all", chunk.size = 1e9, quiet = FALSE) ## S3 method for class 'VCFinfo' print(x, ...) VCFheader(file) VCFlabels(file) ## S3 method for class 'VCFinfo' is.snp(x) rangePOS(x, from, to) selectQUAL(x, threshold = 20) getINFO(x, what = "DP", as.is = FALSE)
file |
file name of the VCF file. |
what |
a character specifying the information to be extracted (see details). |
chunk.size |
the size of data in bytes read at once. |
quiet |
a logical: should the progress of the operation be printed? |
x |
an object of class |
from, to
|
integer values giving the range of position values. |
threshold |
a numerical value indicating the minimum value of quality for selecting loci. |
as.is |
a logical. By default, |
... |
further arguments passed to and from other methods. |
The variant call format (VCF) is described in details in the References. Roughly, a VCF file is made of two parts: the header and the genotypes. The last line of the header gives the labels of the genotypes: the first nine columns give information for each locus and are (always) "CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO", and "FORMAT". The subsequent columns give the labels (identifiers) of the individuals; these may be missing if the file records only the variants. Note that the data are arranged as the transpose of the usual way: the individuals are as columns and the loci are as rows.
VCFloci is the main function documented here: it reads the
information relative to each locus. The option what specifies
which column(s) to read. By default, all of them are read. If the user
is interested in only the locus positions, the option what =
"POS" would be used.
Since VCF files can be very big, the data are read in portions of
chunk.size bytes. The default (1 Gb) should be appropriate in
most situations. This value should not exceed 2e9.
VCFheader returns the header of the VCF file (excluding the
line of labels). VCFlabels returns the individual labels.
The output of VCFloci is a data frame with as many rows as
there are loci in the VCF file and storing the requested
information. The other functions help to extract specific information
from this data frame: their outputs may then be used to select which
loci to read with read.vcf.
is.snp tests whether each locus is a SNP (i.e., the reference
allele, REF, is a single charater and the alternative allele, ALT,
also). It returns a logical vector with as many values as there are
loci. Note that some VCF files have the information VT (variant type)
in the INFO column.
rangePOS and selectQUAL select some loci with respect to
values of position or quality. They return the indices (i.e., row
numbers) of the loci satisfying the conditions.
getINFO extracts a specific information from the INFO
column. By default, these are the total depths (DP) which can be
changed with the option what. The meaning of these information
should be described in the header of the VCF file.
VCFloci returns an object of class "VCFinfo" which is a
data frame with a specific print method.
VCFheader returns a single character string which can be
printed nicely with cat.
VCFlabels returns a vector of mode character.
is.snp returns a vector of mode logical.
rangePOS and selectQUAL return a vector of mode
numeric.
getINFO returns a vector of mode character or numeric (see above).
VCFloci is able to read either compressed (*.gz) or
uncompressed files.
Emmanuel Paradis
https://www.internationalgenome.org/wiki/Analysis/vcf4.0
https://github.com/samtools/hts-specs
## see ?read.vcf## see ?read.vcf
This function writes allelic data into a text file.
write.loci(x, file = "", loci.sep = " ", allele.sep = "/|", ...)write.loci(x, file = "", loci.sep = " ", allele.sep = "/|", ...)
x |
an object of class |
file |
a file name specified by either a variable of mode character, or a quoted string. By default, the data are printed on the console. |
loci.sep |
the character(s) use to separate the loci (columns) in the file (a space by default). |
allele.sep |
the character(s) used to separate the alleles for each locus in the file (a slash by default). |
... |
further arguments passed to |
NULL
Emmanuel Paradis
read.loci, write.table for all its options
data(jaguar) x <- jaguar[1:10, 1:3] # take a small subset write.loci(x) ## use of '...': write.loci(x, loci.sep = "\t", quote = FALSE, col.names = FALSE)data(jaguar) x <- jaguar[1:10, 1:3] # take a small subset write.loci(x) ## use of '...': write.loci(x, loci.sep = "\t", quote = FALSE, col.names = FALSE)