Package 'strataG'

Title: Summaries and Population Structure Analyses of Genetic Data
Description: A toolkit for analyzing stratified population genetic data. Functions are provided for summarizing and checking loci (haploid, diploid, and polyploid), single stranded DNA sequences, calculating most population subdivision metrics, and running external programs such as structure and fastsimcoal. The package is further described in Archer et al (2016) <doi:10.1111/1755-0998.12559>.
Authors: Eric Archer [aut, cre], Paula Adams [aut], Brita Schneiders [aut], Sarina Fernandez [aut], Warren Asfazadour [aut]
Maintainer: Eric Archer <[email protected]>
License: GNU General Public License
Version: 2.5.01
Built: 2024-10-25 07:54:52 UTC
Source: https://github.com/EricArcher/strataG

Help Index

Summaries and population structure analyses of DNA sequence genotypic data

Description

strataG

Allele Frequencies

Description

Calculate allele frequencies or proportions for each locus.

Usage

alleleFreqs(g, by.strata = FALSE, type = c("freq", "prop"))

Arguments

g

a gtypes object.
by.strata

logical determining if results should be returned by strata?
type

return counts ("freq") or proportions ("prop")

Value

A list of allele frequencies for each locus. Each element is a vector (by.strata = FALSE) or matrix (by.strata = TRUE) with the frequency or proportion of each allele.

Note

If g is a haploid object with sequences, the function will run labelHaplotypes if sequences aren't already grouped by haplotype. The gtypes object used with haplotype assignments and unassigned individuals will be stored in attr(*, "gtypes").

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)

f <- alleleFreqs(msats.g)
f$D11t # Frequencies for Locus D11t

f.pop <- alleleFreqs(msats.g, TRUE, "prop")
f.pop$EV94[, "Coastal"] # Proportions of EV94 alleles in the Coastal population

Split Alleles For Diploid Data

Description

Split loci stored in one column to two columns for each allele in a matrix of diploid data.

Usage

alleleSplit(x, sep = NULL)

Arguments

x

a matrix or data.frame containing diploid data. Every column represents one locus with two alleles.
sep

separator used between alleles of a locus. If NULL, then alleles should be of equal length (e.g., 145095 = 145 and 095, or AG = A and G).

Value

matrix with alleles for each locus in one column split into separate columns.

Author(s)

Eric Archer [email protected]

Examples

# A sample SNP data set with no separators between nucleotides in a genotype
snps <- do.call(cbind, lapply(1:3, function(i) {
  a1 <- sample(c("A", "G"), 10, rep = TRUE)
  a2 <- sample(c("A", "G"), 10, rep = TRUE)
  paste(a1, a2, sep = "")
}))
colnames(snps) <- paste("Loc", LETTERS[1:3], sep = "_")
snps
alleleSplit(snps)

# A sample microsatellie data set with alleles separated by "/"
alleles <- seq(100, 150, 2)
msats <- do.call(cbind, lapply(1:3, function(i) {
  a1 <- sample(alleles, 10, rep = TRUE)
  a2 <- sample(alleles, 10, rep = TRUE)
  paste(a1, "/", a2, sep = "")
}))
colnames(msats) <- paste("Loc", LETTERS[1:3], sep = "_")
msats
alleleSplit(msats, sep = "/")

Allelic Richness

Description

Calculate allelic richness for each locus.

Usage

allelicRichness(g, by.strata = FALSE)

Arguments

g

a gtypes object.
by.strata

logical - return results grouped by strata?

Value

a data frame with the allelic richness of each locus calculated as the number of alleles divided by the number of samples without missing data at that locus.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)
allelicRichness(msats.g)

Read and Write Arlequin Files

Description

Read an Arlequin-formatted project input file (.arp). Convert .arp data into gtypes object. Write an input file from a gtypes object.

Usage

arlequinRead(file)

arp2gtypes(arp, avoid.dups = FALSE)

arlequinWrite(g, file = NULL, locus = 1, haploid.microsat = FALSE)

read.arlequin(file)

write.arlequin(g, file = NULL, locus = 1)

Arguments

file

filename of an arlequin project (.arp) file. See Notes for details on how .arp files are parsed.
arp

a list of arlequin profile information and data as returned from arlequinRead.
avoid.dups

logical. Should sample identifiers be combined with strata names to avoid duplicate identifiers between strata? If set to FALSE, ids will be left unchanged, but an error will be thrown when the gtypes object is created if duplicated ids are found.
g

a gtypes object.
locus

numeric or character designation of which locus to write for haploid data.
haploid.microsat

logical. If g is a haploid object (ploidy = 1), but a DataType=MICROSAT .arp file should be written, set this to TRUE. If FALSE (default) all haploid objects will be written as DataType=FREQUENCY if no sequences are present or DataType=DNA if sequences are present.

Details

arlequinRead parses a .arp file.
arp2gtypes converts list from parsed .arp file to gtypes.
arlequinWrite writes gtypes to .arp file.

Value

arlequinRead

a list containing:

file name and full path of .arp file that was read.
profile.info list containing parameters in [[Profile]] section of .arp file. All parameters are provided. Parameters unset in .arp file are set to default values.
data.info list containing data from [[Data]] section of .arp file. Can contain elements for haplotype.definition (a data.frame), distance.matrix (a matrix), sample.data (a data.frame), or genetic.structure (a list).
arp2gtypes

a gtypes object.

arlequinWrite

the filename of the .arp file that was written.

Note

arp2gtypes() will not create a gtypes object for Arlequin projects with relative frequency data (DataType=FREQUENCY and FREQUENCY=REL). If DataType=DNA and GenotypicData=0, sequences for each haplotype or individual are assumed to be from a single locus.

Author(s)

Eric Archer [email protected]

References

Excoffier, L.G. Laval, and S. Schneider (2005) Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1:47-50.
Available at http://cmpg.unibe.ch/software/arlequin3/

Examples

# write test microsat data .arp file
f <- arlequinWrite(msats.g, tempfile())

# read .arp file and show structure
msats.arp <- arlequinRead(f)
print(str(msats.arp))

# convert parsed data to gtypes object
msats.arp.g <- arp2gtypes(msats.arp)
msats.arp.g

# compare to original
msats.g

Convert gtypes to data.frame or matrix

Description

Create a formatted data.frame or matrix from a gtypes object.

Usage

## S4 method for signature 'gtypes'
as.data.frame(
  x,
  one.col = FALSE,
  sep = "/",
  ids = TRUE,
  strata = TRUE,
  sort.alleles = TRUE,
  coded.snps = FALSE,
  ref.allele = NULL,
  ...
)

## S4 method for signature 'gtypes'
as.matrix(
  x,
  one.col = FALSE,
  sep = "/",
  ids = TRUE,
  strata = TRUE,
  sort.alleles = TRUE,
  ...
)

Arguments

x

a gtypes object.
one.col

logical. If TRUE, then result has one column per locus.
sep

character to use to separate alleles if one.col is TRUE.
ids

logical. include a column for individual identifiers (ids)?
strata

logical. include a column for current statification (strata)?
sort.alleles

logical. for non-haploid objects, should alleles be sorted in genotypes or left in original order? (only takes affect if one.col = TRUE)
coded.snps

return diploid SNPs coded as 0 (reference allele homozygote), 1 (heterozygote), or 2 (alternate allele homozygote). If this is 'TRUE', the data is diploid, and all loci are biallelic, a data frame of coded genotypes will be returned with one column per locus.
ref.allele

an optional vector of reference alleles for each SNP. Only used if 'coded.snps = TRUE'. If provided, it must be at least as long as there are biallelic SNPs in g. If named, the names must match those of all loci in g. If set to 'NULL' (default) the major allele at each SNP is used as the reference.
...

additional arguments to be passed to or from methods.

Value

A data.frame or matrix with one row per individual.

Author(s)

Eric Archer [email protected]

See Also

df2gtypes as.matrix

Examples

data(msats.g)

# with defaults (alleles in multiple columns, with ids and stratification)
df <- as.data.frame(msats.g)
str(df)

# one column per locus
onecol.df <- as.data.frame(msats.g, one.col = TRUE)
str(onecol.df)

# just the genotypes
genotypes.df <- as.data.frame(msats.g, one.col = TRUE, ids = FALSE, strata = FALSE)
str(genotypes.df)

# as a matrix instead
genotypes.mat <- as.matrix(msats.g)
str(genotypes.mat)

Convert to multidna

Description

Convert a set of sequences to a multidna object if possible.

Usage

as.multidna(x, ...)

Arguments

x

a valid set of sequences: character matrix, list of character vectors, DNAbin object or list of them, gtypes object, or multidna object.
...

arguments to pass to getSequences if x is a gtypes object.

Author(s)

Eric Archer [email protected]

See Also

getSequences

Examples

# convert list of character vectors
data(dolph.seqs)
list.mdna <- as.multidna(dolph.seqs)
list.mdna

# convert gtypes object
data(dloop.g)
gtype.mdna <- as.multidna(dloop.g)
gtype.mdna

Base Frequencies

Description

Calculate nucleotide base frequencies along a sequence.

Usage

baseFreqs(x, bases = NULL, ignore = c("n", "x", "-", "."), simplify = TRUE)

Arguments

x

a gtypes object with aligned sequences or a list of aligned DNA sequences.
bases

character vector of bases. Must contain valid IUPAC codes. If NULL, will return summary of frequencies of observed bases.
ignore

a character vector of bases to ignore when calculating site frequencies.
simplify

if there is a single locus, return result in a simplified form? If FALSE a list will be returned wth one element per locus.

Value

For each gene, a list containing:

site.freqs a matrix of base frequencies at each site.
base.freqs a vector of overall base proportion composition.

Author(s)

Eric Archer [email protected]

Examples

data(dloop.g)
bf <- baseFreqs(dloop.g)

# Frequencies of first 10 sites
bf$site.freqs[, 1:10]

# Base composition
bf$base.freqs

Bowhead Whale SNP Genotype Groups

Description

A data.frame of position information for SNPs to be phased

Usage

data(bowhead.snp.position)

Format

data.frame

References

Morin, P.A., Archer, F.I., Pease, V.L., Hancock-Hanser, B.L., Robertson, K.M., Huebinger, R.M., Martien, K.K., Bickham, J.W., George, J.C., Postma, L.D., Taylor, B.L., 2012. Empirical comparison of single nucleotide polymorphisms and microsatellites for population and demographic analyses of bowhead whales. Endangered Species Research 19, 129-147.

Bowhead Whale SNP Genotypes

Description

A data.frame of 42 SNPs with sample ids and stratification

Usage

data(bowhead.snps)

Format

data.frame

References

Morin, P.A., Archer, F.I., Pease, V.L., Hancock-Hanser, B.L., Robertson, K.M., Huebinger, R.M., Martien, K.K., Bickham, J.W., George, J.C., Postma, L.D., Taylor, B.L., 2012. Empirical comparison of single nucleotide polymorphisms and microsatellites for population and demographic analyses of bowhead whales. Endangered Species Research 19, 129-147.

Run CLUMPP

Description

Run CLUMPP to aggregate multiple STRUCTURE runs.

Usage

clumpp(
  sr,
  k,
  align.algorithm = "greedy",
  sim.stat = "g",
  greedy.option = "ran.order",
  repeats = 100,
  order.by.run = 0,
  label = NULL,
  delete.files = TRUE
)

Arguments

sr

result from structure or folder name containing STRUCTURE output files.
k

choice of k in sr to combine.
align.algorithm

algorithm to be used for aligning the runs. Can be "full.search", "greedy", or "large.k".
sim.stat

pairwise matrix similarity statistic to be used. Can be "g" or "g.prime".
greedy.option

input order of runs to be tested. Required if align.algorithm is "greedy" or "large.k". Valid choices are:

all test all possible input orders of runs (note that this option increases the run-time sub-stantially unless R is small).
ran.order test a specified number of random input orders of runs set by the repeats parameter.
repeats

the number of input orders of runs to be tested. Only used if align.algorithm is "greedy" or "large.k", and greedy.option is "ran.order".
order.by.run

permute the clusters according to the cluster order of a specific run. Set this parameter to a number from 1 to the number of runs in sr.
label

label to use for input and output files.
delete.files

logical. Delete all files when CLUMPP is finished?

Note

CLUMPP is not included with strataG and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. See the vignette for external.programs.

Author(s)

Eric Archer [email protected]

References

Mattias Jakobsson and Noah A. Rosenberg. 2007. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23(14):1801-1806. Available at http://web.stanford.edu/group/rosenberglab/clumppDownload.html

See Also

structure

Consensus Sequence

Description

Return a consensus sequence from set of aligned sequences, introducing IUPAC ambiguity codes where necessary.

Usage

createConsensus(x, ignore.gaps = FALSE, simplify = TRUE)

Arguments

x

a gtypes object with aligned sequences or a list of aligned DNA sequences.
ignore.gaps

logical. Ignore gaps at a site when creating consensus. If TRUE, then bases with a gap are removed before consensus is calculated. If FALSE and a gap is present, then the result is a gap.
simplify

if there is a single locus, return result in a simplified form? If FALSE a list will be returned wth one element per locus.

Value

A character vector of the consensus sequence.

Author(s)

Eric Archer [email protected]

Examples

data(dolph.seqs)
createConsensus(dolph.seqs)

Convert a data.frame to gtypes

Description

Load allelic data from a data.frame or matrix into a gtypes object.

Usage

df2gtypes(
  x,
  ploidy,
  id.col = 1,
  strata.col = 2,
  loc.col = 3,
  sequences = NULL,
  schemes = NULL,
  description = NULL,
  other = NULL
)

Arguments

x

a matrix or data.frame of genetic data.
ploidy

number of number of columns in x storing alleles at each locus.
id.col

column name or number where individual sample ids are stored. If NULL then rownames are used. If there are no rownames, then samples are labelled with consecutive numbers.
strata.col

column name or number where stratification is stored. If NULL then all samples are in one (default) stratum.
loc.col

column number of first allele of first locus.
sequences

a list, matrix, DNAbin, or multidna object containing sequences.
schemes

an optional data.frame of stratification schemes.
description

a label for the object (optional).
other

a list to carry other related information (optional).

Details

The genetic data in x starting at loc.col should be formatted such that every consecutive ploidy columns represent alleles of one locus. Locus names are taken from the column names in x and should be formatted with the same root locus name, with unique suffixes representing allels (e.g., for Locus1234: Locus1234.1 and Locus1234.2, or Locus1234_A and Locus1234_B).
If sequences are provided in sequences, then they should be named and match haplotype labels in loc.col of x. If multiple genes are given as a multidna, then they should have the same names as column names in x from loc.col to the end.

Value

a gtypes object.

Author(s)

Eric Archer [email protected]

See Also

gtypes.initialize, sequence2gtypes, as.data.frame.gtypes, gtypes2genind, gtypes2loci, gtypes2phyDat

Examples

#--- create a diploid (microsatellite) gtypes object
data(dolph.msats)
ms.g <- df2gtypes(dolph.msats, ploidy = 2, strata.col = NULL, loc.col = 2)
ms.g

#' #--- create a haploid sequence (mtDNA) gtypes object
data(dolph.strata)
data(dolph.haps)

seq.df <- dolph.strata[ c("id", "broad", "dLoop")]
dl.g <- df2gtypes(seq.df, ploidy = 1, sequences = dolph.haps)
dl.g

Dolphin dLoop gtypes Object

Description

A gtypes object of 126 samples and 33 haplotypes.

Usage

data(dloop.g)

Format

gtypes

References

Lowther-Thieleking J.L., F.I. Archer, A.R. Lang, and D.W. Weller. 2015. Genetic variation of coastal and offshore bottlenose dolphins, Tursiops truncatus, in the eastern North Pacific Ocean. Marine Mammal Science 31:1-20

Dolphin mtDNA Haplotype Sequences

Description

A list of 33 aligned d-loop haplotypes

Usage

data(dolph.haps)

Format

list

References

Lowther-Thieleking J.L., F.I. Archer, A.R. Lang, and D.W. Weller. 2015. Genetic variation of coastal and offshore bottlenose dolphins, Tursiops truncatus, in the eastern North Pacific Ocean. Marine Mammal Science 31:1-20

Dolphin Microsatellite Genotypes

Description

A data.frame of 126 samples and 4 microsatellite loci

Usage

data(dolph.msats)

Format

data.frame

References

Lowther-Thieleking J.L., F.I. Archer, A.R. Lang, and D.W. Weller. 2015. Genetic variation of coastal and offshore bottlenose dolphins, Tursiops truncatus, in the eastern North Pacific Ocean. Marine Mammal Science 31:1-20

Dolphin mtDNA D-loop Sequences

Description

A list of 126 aligned control region sequences

Usage

data(dolph.seqs)

Format

list

References

Lowther-Thieleking J.L., F.I. Archer, A.R. Lang, and D.W. Weller. 2015. Genetic variation of coastal and offshore bottlenose dolphins, Tursiops truncatus, in the eastern North Pacific Ocean. Marine Mammal Science 31:1-20

Dolphin Genetic Stratification and Haplotypes

Description

A data.frame of 126 samples with assignment of samples to either broad-scale or fine-scale stratifications and mtDNA haplotype designations

Usage

data(dolph.strata)

Format

data.frame

References

Lowther-Thieleking J.L., F.I. Archer, A.R. Lang, and D.W. Weller. 2015. Genetic variation of coastal and offshore bottlenose dolphins, Tursiops truncatus, in the eastern North Pacific Ocean. Marine Mammal Science 31:1-20

Duplicate Genotypes

Description

Identify duplicate or very similar genotypes.

Usage

dupGenotypes(g, num.shared = 0.8)

Arguments

g

a gtypes object.
num.shared

either number of loci or percentage of loci two individuals must share to be considered duplicate individuals.

Value

if no duplicates are present, the result is NULL, otherwise a data frame with the following columns is returned:

ids.1, ids.2 sample ids.
strata.1, strata.2 sample stratification.
mismatch.loci loci where the two samples do not match.
num.loci.genotyped number of loci genotyped for both samples.
num.loci.shared number of loci shared (all alleles the same) between both samples.
prop.loci.shared proportion of loci genotyped for both samples that are shared.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)

# identify potential duplicates in Coastal strata
coastal <- msats.g[, , "Coastal"]
coastal.5 <- coastal[getIndNames(coastal)[1:5], , ]
dupes <- dupGenotypes(coastal.5)
dupes

Run Evanno Method on STRUCTURE Results

Description

Calculate first and second order rates of changes of LnPr(K) from STRUCTURE results based on Evanno et al. 2005.

Usage

evanno(sr, plot = TRUE)

Arguments

sr

output from a call to structure.
plot

logical. Generate a plot of Evanno metrics?

Value

a list with:

df data.frame with Evanno log-likelihood metrics for each value of K.
plots list of four ggplot objects for later plotting.

Author(s)

Eric Archer [email protected]

References

Evanno, G., Regnaut, S., and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14:2611-2620.

See Also

structure clumpp

Examples

## Not run: 
data(msats.g)

# Run STRUCTURE
sr <- structureRun(msats, k.range = 1:4, num.k.rep = 10)

# Calculate Evanno metrics
evno <- evanno(sr)
evno

## End(Not run)

Expand Haplotypes

Description

Expand haplotypes to a single sequence per individual.

Usage

expandHaplotypes(g)

Arguments

g

a haploid gtypes object with sequences.

Value

a gtypes object with sequences expanded and renamed so there is one sequence per individual. Sequence names are set to individual sample IDs.

Author(s)

Eric Archer [email protected]

See Also

labelHaplotypes

Examples

data(dloop.g)

# Haplotypes have already been labelled
dloop.g

# Haplotypes expanded to individual sequences (num.alleles == num.samples)
expanded.g <- expandHaplotypes(dloop.g)
expanded.g

Read and Write FASTA

Description

Read and write FASTA formatted files of sequences.

Usage

read.fasta(file)

write.fasta(x, file = NULL)

Arguments

file

a FASTA-formatted file of sequences.
x

a list or a matrix of DNA sequences (see write.dna), or a gtypes object with sequences.

Value

read.fasta

a set of sequences in DNAbin format

write.fasta

invisbly, name(s) of file(s) written

Author(s)

Eric Archer [email protected]

Fixed Differences

Description

Summarize fixed base pair differences between strata.

Usage

fixedDifferences(
  g,
  count.indels = TRUE,
  consec.indels.as.one = TRUE,
  bases = c("a", "c", "g", "t", "-")
)

Arguments

g

a gtypes object.
count.indels

logical. Count indels when evaluating sites for fixed differences?
consec.indels.as.one

logical. If count.indels is TRUE, count consecutive indels as a a single indel?
bases

a character vector of valid bases to consider.

Value

a list with components:

sites

list of sites with fixed differences for each pair of strata

num.fixed

data.frame of number of sites fixed between each pair of strata

Author(s)

Eric Archer <[email protected]>

See Also

fixedSites, variableSites

Examples

data(dloop.g)
fd <- fixedDifferences(dloop.g)
fd

Fixed Sites

Description

Identify fixed sites among sequences.

Usage

fixedSites(x, bases = c("a", "c", "g", "t", "-"), simplify = TRUE)

Arguments

x

a gtypes object with sequences, a list of sequences, or a consensus sequence. Sequences must be aligned.
bases

a character vector of valid bases to consider.
simplify

if there is a single locus, return result in a simplified form? If FALSE a list will be returned wth one element per locus.

Value

a vector of fixed sites. Element names are site positions in the original sequence.

Author(s)

Eric Archer <[email protected]>

See Also

variableSites

Examples

data(dolph.haps)

fixedSites(dolph.haps)

Convert Haplotype Frequency Matrices

Description

Create a data frame of stratified individuals and their haplotypes from a frequency table

Usage

freq2GenData(
  freq.mat,
  hap.col = NULL,
  freq.col = 1,
  id.label = NULL,
  hap.label = NULL
)

Arguments

freq.mat

a matrix or data.frame containing haplotypic frequencies with strata as column names.
hap.col

a number giving the column providing haplotype labels or a vector the same length as freq.mat. If NULL rownames are used.
freq.col

a number giving the first column containing haplotype frequencies.
id.label

character to label sample IDs with in resulting data.frame.
hap.label

character to label haplotypes with in resulting data.frame.

Value

a data frame with one row per sample and columns for id, strata, and haplotype, suitable for use in df2gtypes.

Author(s)

Eric Archer [email protected]

Examples

hap.freqs <- data.frame(
  haps = c("hap1", "hap2", "hap3"),
  pop1 = rmultinom(1, 50, prob = c(0.1, 0.2, 0.7)),
  pop2 = rmultinom(1, 25, prob = c(0.5, 0.4, 0.1))
)

gen.data <- freq2GenData(hap.freqs, hap.col = 1, freq.col = 2)

x <- df2gtypes(gen.data, ploidy = 1)
summary(x)

Input functions for fastsimcoal parameters

Description

These functions specify and format simulation parameters used to write fastsimcoal2 parameter or template files, parameter estimation files, parameter definition files, and site frequency spectrum files.

Usage

fscDeme(deme.size, sample.size, sample.time = 0, inbreeding = 0, growth = 0)

fscSettingsDemes(..., ploidy = 2)

fscEvent(
  event.time = 0,
  source = 0,
  sink = 0,
  prop.migrants = 1,
  new.size = 1,
  new.growth = 0,
  migr.mat = 0
)

fscSettingsEvents(...)

fscSettingsMigration(...)

fscBlock_dna(
  sequence.length,
  mut.rate,
  recomb.rate = 0,
  transition.rate = 1/3,
  chromosome = 1
)

fscBlock_microsat(
  num.loci,
  mut.rate,
  recomb.rate = 0,
  gsm.param = 0,
  range.constraint = 0,
  chromosome = 1
)

fscBlock_snp(sequence.length, mut.rate, recomb.rate = 0, chromosome = 1)

fscBlock_standard(num.loci, mut.rate, recomb.rate = 0, chromosome = 1)

fscBlock_freq(mut.rate, outexp = TRUE)

fscSettingsGenetics(..., num.chrom = NULL)

fscEstParam(
  name,
  is.int = TRUE,
  distr = c("unif", "logunif"),
  min = NA,
  max = NA,
  value = NA,
  output = TRUE,
  bounded = FALSE,
  reference = FALSE
)

fscSettingsEst(..., obs.sfs, rules = NULL, sfs.type = c("maf", "daf"))

fscSettingsDef(mat)

Arguments

deme.size

the number of individuals in the deme.
sample.size

the number of samples to take.
sample.time

the number of generations in the past at which samples are taken.
inbreeding

the inbreeding coefficient for the deme [0:1].
growth

the growth rate of the deme.
...

a set of comma-separated values for settings. See Notes for more information.
ploidy

the desired ploidy of the final data. deme.size and sample.size will be multiplied by this value in the parameter or template file as fastsimcoal2 generates haploid data.
event.time

the number of generations before present at which the historical event happened.
source

the source deme (the first listed deme has index 0).
sink

the sink deme.
prop.migrants

the expected proportion of migrants to move from the source to the sink deme.
new.size

the new size for the sink deme, relative to its size in the previous (later in time) generation.
new.growth

the new growth rate for the sink deme.
migr.mat

the number of the new migration matrix to be used further back in time. The matrices are those supplied to the fscSettingsMigration function. The first matrix has index 0.
sequence.length

number of base pairs to use for each block.
mut.rate

per base pair or locus mutation rate.
recomb.rate

recombination rate between adjacent markers. No effect for SNPs.
transition.rate

dna: fraction of substitutions that are transitions.
chromosome

number or character identifying which chromosome the marker is on.
num.loci

number of loci to simulate.
gsm.param

value of the geometric parameter for a Generalized Stepwise Mutation (GSM) model. This value represents the proportion of mutations that will change the allele size by more than one step. Values between 0 and 1 are required. A value of 0 is for a strict Stepwise Mutation Model (SMM).
range.constraint

msat: Range constraint (number of different alleles allowed). A value of 0 means no range constraint.
outexp

logical describing if the expected site frequency spectrum given the estimated parameters should be output?
num.chrom

the number of chromosomes to be simulated. If this is specified and not the same as the number of linkage blocks specified by the fscBlock_ functions, then this many chromosomes with duplicated structures will be simulated. If num.chrom = NULL, then the chromosome specification for each block will be used.
name

name of the parameter being specified. Must match a name used in one of the simulation settings functions.
is.int

logical specifying whether or not the parameter is an integer.
distr

a character string giving the distribution to use to select initial values for parameter estimation. Can be "unif" or "logunif".
min, max

minimum and maximum values for the distribution specified in distr.
value

character string giving the value that the complex parameter is to take.
output

logical indicating if estimates for the parameter should be output.
bounded

logical indicating whether to treat the parameter as a bounded estimate.
reference

logical indicating whether the parameter is to be used as a reference.
obs.sfs

vector, matrix, or list containing observed SFS to use for parameter estimation.
rules

character vector giving rules for the parameter estimation.
sfs.type

type of SFS to write. Can be maf or daf.
mat

numeric matrix or data frame with values of parameters to use in place of parameter names in simulation.

Note

All settings must be passed to fscWrite using one of the fscSettingsXXX functions. Most of these functions in turn take as their input comma-separated values which are the result of specific fscXXX functions:

fscSettingsDemes()

comma-separated instances of fscDeme(). If names are given for each deme, these names will be used in the parsed output.

fscSettingsEvents()

comma-separated instances of fscEvent().

fscSettingsMigration()

comma-separated migration matrices.

fscSettingsGenetics()

comma-separated instances of fscBlock_dna(), fscBlock_microsat(), fscBlock_snp(), fscBlock_standard(), or fscBlock_freq(). SNPs are simulated as a DNA sequence with a transiton rate of 1. 'fscBlock_freq()' can only be used by itself and in parameter estimation simulations.

fscSettingsEst()

comma-separated instances of fscEstParam() as well as site frequency spectra (obs.sfs) and parameter rules rules.

fastsimcoal2 is not included with 'strataG' and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. The function fscTutorial() will open a detailed tutorial on the interface in your web browser.

Author(s)

Eric Archer [email protected]

References

Excoffier, L. and Foll, M (2011) fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios Bioinformatics 27: 1332-1334.
Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V.C., and M. Foll (2013) Robust demographic inference from genomic and SNP data. PLOS Genetics, 9(10):e1003905.
http://cmpg.unibe.ch/software/fastsimcoal2/

See Also

fscWrite, fscRun, fscRead

Examples

# three demes with optional names
demes <- fscSettingsDemes(
  Large = fscDeme(10000, 10), 
  Small = fscDeme(2500, 10),
  Medium = fscDeme(5000, 3, 1500)
)

# four historic events
events <- fscSettingsEvents(
  fscEvent(event.time = 2000, source = 1, sink = 2, prop.migrants = 0.05),
  fscEvent(2980, 1, 1, 0, 0.04),
  fscEvent(3000, 1, 0),
  fscEvent(15000, 0, 2, new.size = 3)
 )
 
# four genetic blocks of different types on three chromosomes.  
genetics <- fscSettingsGenetics(
  fscBlock_snp(10, 1e-6, chromosome = 1),
  fscBlock_dna(10, 1e-5, chromosome = 1),
  fscBlock_microsat(3, 1e-4, chromosome = 2),
  fscBlock_standard(5, 1e-3, chromosome = 3)
)

#' same four genetic blocks of different types with same structure repeated three times.  
genetics <- fscSettingsGenetics(
  fscBlock_snp(10, 1e-6),
  fscBlock_dna(10, 1e-5),
  fscBlock_microsat(3, 1e-4),
  fscBlock_standard(5, 1e-3),
  num.chrom = 3
)

Read fastsimcoal output

Description

Read arlequin formatted output or parameter estimation files generated by fastsimcoal

Usage

fscReadArp(
  p,
  sim = c(1, 1),
  marker = c("all", "snp", "microsat", "dna", "standard"),
  chrom = NULL,
  sep.chrom = FALSE,
  drop.mono = FALSE,
  as.genotypes = TRUE,
  one.col = FALSE,
  sep = "/",
  coded.snps = FALSE
)

fscReadParamEst(p)

fscReadSFS(p, sim = 1)

fsc2gtypes(p, marker = c("dna", "snp", "microsat"), concat.dna = TRUE, ...)

Arguments

p

list of fastsimcoal parameters output from fscRun.
sim

one or two-element numberic vector giving the number of the simulation replicate (and sub-replicate) to read. For example, sim = c(3, 5) will attempt to read "<label>_3_5.arp".
marker

type of marker to return.
chrom

numerical vector giving chromosomes to return. If NULL all chromosomes are returned.
sep.chrom

return a list of separate chromosomes?
drop.mono

return only polymorphic loci?
as.genotypes

return data as genotypes? If FALSE, original haploid data is returned. If TRUE, individuals are created by combining sequential haplotypes based on the ploidy used to run the simulation.
one.col

return genotypes with one column per locus? If FALSE, alleles are split into separate columns and designated as ".1", ".2", etc. for each locus.
sep

character to use to separate alleles if one.col = TRUE.
coded.snps

return diploid SNPs coded as 0 (major allele homozygote), 1 (heterozygote), or 2 (minor allele homozygote). If this is TRUE and marker = "snp" (or only SNPs are present) and the data is diploid, genotypes will be returned with one column per locus.
concat.dna

logical. concatenate multiple DNA blocks into single locus?
...

arguments to be passed to fscReadArp.

Value

fscReadArp

Reads and parses Arlequin-formatted .arp output files created by fastsimcoal2. Returns a data frame of genotypes, with individuals created by combining haplotypes based on the stored value of ploidy specified when the simulation was run.

fscReadParamEst

Reads and parses files output from a fastsimcoal2 run conducted for parameter estimation. Returns a list of data frames and vectors containing the data from each file.

fscReadSFS

Reads site frequency spectra generated from fastsimcoal2. Returns a list of the marginal and joint SFS, the polymorphic sites, and the estimated maximum likelihood of the SFS."

fsc2gtypes

Creates a gtypes object from fastsimcoal2 output.

Note

fastsimcoal2 is not included with 'strataG' and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. The function fscTutorial() will open a detailed tutorial on the interface in your web browser.

Author(s)

Eric Archer [email protected]

References

Excoffier, L. and Foll, M (2011) fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios Bioinformatics 27: 1332-1334.
Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V.C., and M. Foll (2013) Robust demographic inference from genomic and SNP data. PLOS Genetics, 9(10):e1003905.
http://cmpg.unibe.ch/software/fastsimcoal2/

See Also

fsc.input, fscWrite, fscRun

Examples

## Not run: 
#' # three demes with optional names
demes <- fscSettingsDemes(
  Large = fscDeme(10000, 10), 
  Small = fscDeme(2500, 10),
  Medium = fscDeme(5000, 3, 1500)
)

# four historic events
events <- fscSettingsEvents(
  fscEvent(event.time = 2000, source = 1, sink = 2, prop.migrants = 0.05),
  fscEvent(2980, 1, 1, 0, 0.04),
  fscEvent(3000, 1, 0),
  fscEvent(15000, 0, 2, new.size = 3)
 )
 
# four genetic blocks of different types on three chromosomes.  
genetics <- fscSettingsGenetics(
  fscBlock_snp(10, 1e-6, chromosome = 1),
  fscBlock_dna(10, 1e-5, chromosome = 1),
  fscBlock_microsat(3, 1e-4, chromosome = 2),
  fscBlock_standard(5, 1e-3, chromosome = 3)
)

params <- fscWrite(demes = demes, events = events, genetics = genetics)

# runs 100 replicates, converting all DNA sequences to 0/1 SNPs
# will also output the MAF site frequency spectra (SFS) for all SNP loci.
params <- fscRun(params, num.sim = 100, dna.to.snp = TRUE, num.cores = 3)

# extracting only microsattelite loci from simulation replicate 1
msats <- fscReadArp(params, marker = "microsat")

# read SNPs from simulation replicate 5 with genotypes coded as 0/1
snp.5 <- fscReadArp(params, sim = 1, marker = "snp", coded.snps = TRUE

# read SFS for simulation 20
sfs.20 <- fscReadSFS(params, sim = 20)

## End(Not run)

Run fastsimcoal

Description

Run a fastsimcoal simulation.

Usage

fscRun(
  p,
  num.sims = 1,
  dna.to.snp = FALSE,
  max.snps = 0,
  sfs.type = c("maf", "daf"),
  nonpar.boot = NULL,
  all.sites = TRUE,
  inf.sites = FALSE,
  no.arl.output = FALSE,
  num.loops = 20,
  min.num.loops = 20,
  brentol = 0.01,
  trees = FALSE,
  num.cores = 1,
  seed = NULL,
  quiet = TRUE,
  exec = "fsc26"
)

fscCleanup(label, folder = ".")

fscTutorial()

Arguments

p

list of fastsimcoal input parameters and output produced by fscWrite.
num.sims

number of simulation replicates to run.
dna.to.snp

convert DNA sequences to numerical SNPs?
max.snps

maximum number of SNPs to retain.
sfs.type

type of site frequency spectrum to compute for each population sample: 'daf' = derived allele frequency (unfolded), 'maf' = minor allele frequency (folded).
nonpar.boot

number of bootstraps to perform on polymorphic sites to extract SFS.
all.sites

retain all sites? If FALSE, only polymorphic DNA sites will be returned. This includes SNP blocks as they are simulated as DNA sequences.
inf.sites

use infinite sites model? If TRUE, all mutations are retained in the output, thus the number of sites for SNPs or DNA sequences will potentially be greater than what was requested.
no.arl.output

do not output arlequin files.
num.loops

number of loops (ECM cycles) to be performed when estimating parameters from SFS. Default is 20.
min.num.loops

number of loops (ECM cycles) for which the likelihood is computed on both monomorphic and polymorphic sites. Default is 20.
brentol

Tolerance level for Brent optimization. Smaller value imply more precise estimations, but require more computation time. Default = 0.01. Value is restricted between 1e-5 and 1e-1.
trees

output NEXUS formatted coalescent trees for all replicates?
num.cores

number of cores to use. If set to NULL, the value will be what is reported by detectCores - 1.
seed

random number seed for simulation.
quiet

logical indicating if fastsimcoal2 should be run in quiet mode.
exec

name of fastsimcoal executable.
label

character string of file run labels prefixes.
folder

character string giving the root working folder where input files and output resides

Value

fscRun

Runs the fastsimcoal2 simulation and returns a list containing run parameters and a data frame used by fscRead to parse the genotypes generated (if Arlequin-formatted output was requested).

fscCleanup

Deletes all files associated with the simulation identified by label.

Note

fastsimcoal2 is not included with 'strataG' and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. The function fscTutorial() will open a detailed tutorial on the interface in your web browser.

Author(s)

Eric Archer [email protected]

References

Excoffier, L. and Foll, M (2011) fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios Bioinformatics 27: 1332-1334.
Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V.C., and M. Foll (2013) Robust demographic inference from genomic and SNP data. PLOS Genetics, 9(10):e1003905.
http://cmpg.unibe.ch/software/fastsimcoal2/

See Also

fsc.input, fscWrite, fscRead

Examples

## Not run: 
#' # three demes with optional names
demes <- fscSettingsDemes(
  Large = fscDeme(10000, 10), 
  Small = fscDeme(2500, 10),
  Medium = fscDeme(5000, 3, 1500)
)

# four historic events
events <- fscSettingsEvents(
  fscEvent(event.time = 2000, source = 1, sink = 2, prop.migrants = 0.05),
  fscEvent(2980, 1, 1, 0, 0.04),
  fscEvent(3000, 1, 0),
  fscEvent(15000, 0, 2, new.size = 3)
 )
 
# four genetic blocks of different types on three chromosomes.  
genetics <- fscSettingsGenetics(
  fscBlock_snp(10, 1e-6, chromosome = 1),
  fscBlock_dna(10, 1e-5, chromosome = 1),
  fscBlock_microsat(3, 1e-4, chromosome = 2),
  fscBlock_standard(5, 1e-3, chromosome = 3)
)

params <- fscWrite(demes = demes, events = events, genetics = genetics)

# runs 100 replicates, converting all DNA sequences to 0/1 SNPs
# will also output the MAF site frequency spectra (SFS) for all SNP loci.
params <- fscRun(params, num.sim = 100, dna.to.snp = TRUE, num.cores = 3)

## End(Not run)

Write fastsimcoal2 input files

Description

Write files necessary to run a fastsimcoal2 simulation.

Usage

fscWrite(
  demes,
  genetics,
  migration = NULL,
  events = NULL,
  est = NULL,
  def = NULL,
  label = "strataG.fsc",
  use.wd = FALSE
)

Arguments

demes

matrix of deme sampling information created by the fscSettingsDemes function.
genetics

data.frame specifying loci to simulate created by the fscSettingsGenetics function.
migration

a list of matrices giving the migration rates between pairs of demes created by the fscSettingsMigration function.
events

matrix of historical events created by the fscSettingsEvents function.
est

list of parameter estimation definitions and rules generated by the fscSettingsEst function.
def

matrix of parameter values to substitute into the model generated by the fscSettingsDef function.
label

character string used to label output files for the simulation.
use.wd

use current working directory for input and output? If FALSE then a temporary directory in the session temporary directory. Note that this directory is deleted when the R session closed. See tempdir for more information.

Value

Writes input files for fastsimcoal2 and returns a list of input parameters, input file, and input filenames. This list is the primary input to fscRun.

Note

fastsimcoal2 is not included with 'strataG' and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. The function fscTutorial() will open a detailed tutorial on the interface in your web browser.

Author(s)

Eric Archer [email protected]

References

Excoffier, L. and Foll, M (2011) fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios Bioinformatics 27: 1332-1334.
Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V.C., and M. Foll (2013) Robust demographic inference from genomic and SNP data. PLOS Genetics, 9(10):e1003905.
http://cmpg.unibe.ch/software/fastsimcoal2/

See Also

fsc.input, fscRun, fscRead

Examples

## Not run: 
#' # three demes with optional names
demes <- fscSettingsDemes(
  Large = fscDeme(10000, 10), 
  Small = fscDeme(2500, 10),
  Medium = fscDeme(5000, 3, 1500)
)

# four historic events
events <- fscSettingsEvents(
  fscEvent(event.time = 2000, source = 1, sink = 2, prop.migrants = 0.05),
  fscEvent(2980, 1, 1, 0, 0.04),
  fscEvent(3000, 1, 0),
  fscEvent(15000, 0, 2, new.size = 3)
 )
 
# four genetic blocks of different types on three chromosomes.  
genetics <- fscSettingsGenetics(
  fscBlock_snp(10, 1e-6, chromosome = 1),
  fscBlock_dna(10, 1e-5, chromosome = 1),
  fscBlock_microsat(3, 1e-4, chromosome = 2),
  fscBlock_standard(5, 1e-3, chromosome = 3)
)

params <- fscWrite(demes = demes, events = events, genetics = genetics)

## End(Not run)

Fu's Fs

Description

Calculate Fu's Fs for a set of sequences to test for selection.

Usage

fusFs(x)

Arguments

x

set of DNA sequences or a haploid gtypes object with sequences.

Note

Currently, this function is limited to calculating Fs for fewer than approximately 172 sequences due to numerical overflow issues. NaN will be returned for larger data sets. Statistical significance (p-values) of Fs must be calculated with case-specific simulations.

Author(s)

Eric Archer [email protected]

References

Fu, Y-X. 1997. Statistical tests of neutrality of mutations against population growth, hitchiking and background selection. Genetics 147:915-925.

Examples

data(dolph.seqs)

fusFs(dolph.seqs)

GELATo - Group ExcLusion and Assignment Test

Description

Run a GELATo test to evaluate assignment likelihoods of groups of samples.

Usage

gelato(g, unknown.strata, nrep = 1000, min.sample.size = 5, num.cores = 1)

gelatoPlot(gelato.result, unknown = NULL, main = NULL)

Arguments

g

a gtypes object.
unknown.strata

a character vector listing to assign. Strata must occur in g.
nrep

number of permutation replicates for Fst distribution.
min.sample.size

minimum number of samples to use to characterize knowns. If the known sample size would be smaller than this after drawing an equivalent number of unknowns for self-assignment, then the comparison is not done.
num.cores

The number of cores to use to distribute replicates over. If set to NULL, the value will be what is reported by detectCores - 1.
gelato.result

the result of a call to gelato.
unknown

the names of the unknown strata in the x$likelihoods element to create plots. If NULL one plot for each stratum is created.
main

main label for top of plot.#'

Value

A list with the following elements:

assign.prob a data.frame of assignment probabilities.
likelihoods a list of likelihoods.

Author(s)

Eric Archer [email protected]

References

O'Corry-Crowe, G., W. Lucey, F.I. Archer, and B. Mahoney. 2015. The genetic ecology and population origins of the beluga whales of Yakutat Bay. Marine Fisheries Review 77(1):47-58

Examples

## Not run: 
data(msats.g)

# Run GELATo analysis
gelato.fine <- gelato(msats.g, unk = "Offshore.South", nrep = 20, num.cores = 2)
gelato.fine

# Plot results
gelatoPlot(gelato.fine, "Offshore.South")

## End(Not run)

Run GENEPOP

Description

Format output files and run GENEPOP. Filenames used are returned so that output files can be viewed or read and parsed into R.

Usage

genepop(
  g,
  output.ext = "",
  show.output = F,
  label = "genepop.run",
  dem = 10000,
  batches = 100,
  iter = 5000,
  other.settings = "",
  input.fname = "loc_data.txt",
  exec = "Genepop"
)

genepopWrite(g, label = NULL)

Arguments

g

a gtypes object.
output.ext

character string to use as extension for output files.
show.output

logical. Show GENEPOP output on console?
label

character string to use to label GENEPOP input and output files.
dem

integer giving the number of MCMC dememorisation or burnin steps.
batches

integer giving number of MCMC batches.
iter

integer giving number of MCMC iterations.
other.settings

character string of optional GENEPOP command line arguments.
input.fname

character string to use for input file name.
exec

name of Genepop executable

Value

genepop

a list with a vector of the locus names and a vector of the input and output filenames.

genepopWrite

a list with the filename written and a vector mapping locus names in the file to the original locus names.

Note

GENEPOP is not included with strataG and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. See the vignette for external.programs for installation instructions.

Author(s)

Eric Archer [email protected]

References

GENEPOP 4.3 (08 July 2014; Rousset, 2008)
http://kimura.univ-montp2.fr/~rousset/Genepop.htm

See Also

hweTest, LDgenepop

Examples

## Not run: 
# Estimate Nm for the microsatellite data
data(msats.g)
# Run Genepop for Option 4
results <- genepop(msats.g, output.ext = ".PRI", other.settings = "MenuOptions=4")
# Locus name mapping and files
results
# Show contents of output file
file.show(results$files["output.fname"])

## End(Not run)

gtypes Class

Description

An S4 class storing multi-allelic locus or sequence data along with a current stratification and option stratification schemes.

Slots

data

a data.table where the first column contains the sample ID (ids). The second column contains the sample stratification (strata). The third column to the end contains the allelic data as one column per locus. Alleles are on multiple rows per column with sample IDs duplicated for all alleles. Column names are unique locus names.

sequences

a multidna object.

ploidy

integer representing the ploidy of the data. There are ploidy * the number of individuals rows in 'data'.

schemes

a data.frame with stratification schemes in each column. The rownames are individual names and must match the 'id' column of the 'data' slot. Each column is a factor.

description

a label for the object (optional).

other

a slot to carry other related information - currently unused in analyses (optional).

Author(s)

Eric Archer [email protected]

See Also

df2gtypes, sequence2gtypes, gtypes.accessors, gtypes.initialize

Examples

#--- create a diploid (microsatellite) gtypes object
data(dolph.msats)
data(dolph.strata)
strata.schemes <- dolph.strata[, c("broad", "fine")]
rownames(strata.schemes) <- dolph.strata$id
msats.g <- new("gtypes", gen.data = dolph.msats[, -1], ploidy = 2,
               ind.names = dolph.msats[, 1], schemes = strata.schemes)
msats.g

#--- create a haploid sequence (mtDNA) gtypes object and label haplotypes
data(dolph.seqs)
dloop.haps <- cbind(dLoop = dolph.strata$id)
rownames(dloop.haps) <- dolph.strata$id
dloop.g <- new("gtypes", gen.data = dloop.haps, ploidy = 1, 
               schemes = strata.schemes, sequences = dolph.seqs, 
               strata = "fine")
dloop.g
labelHaplotypes(dloop.g, "Hap.")

gtypes Accessors

Description

Accessors for slots in gtypes objects.

Usage

## S4 method for signature 'gtypes'
getNumInd(x, by.strata = FALSE, ...)

## S4 method for signature 'gtypes'
getNumLoci(x, ...)

getNumStrata(x, ...)

## S4 method for signature 'gtypes'
getNumStrata(x, ...)

getIndNames(x, ...)

## S4 method for signature 'gtypes'
getIndNames(x, by.strata = FALSE, ...)

getLociNames(x, ...)

## S4 method for signature 'gtypes'
getLociNames(x, ...)

getAlleleNames(x, ...)

## S4 method for signature 'gtypes'
getAlleleNames(x, ...)

getStrataNames(x, ...)

## S4 method for signature 'gtypes'
getStrataNames(x, ...)

getPloidy(x, ...)

## S4 method for signature 'gtypes'
getPloidy(x, ...)

getStrata(x, ...)

## S4 method for signature 'gtypes'
getStrata(x)

setStrata(x) <- value

## S4 replacement method for signature 'gtypes'
setStrata(x) <- value

getSchemes(x, ...)

## S4 method for signature 'gtypes'
getSchemes(x, ...)

setSchemes(x) <- value

## S4 replacement method for signature 'gtypes'
setSchemes(x) <- value

getSequences(x, ...)

## S4 method for signature 'gtypes'
getSequences(
  x,
  as.haplotypes = FALSE,
  seqName = NULL,
  as.multidna = FALSE,
  simplify = TRUE,
  ...
)

getDescription(x, ...)

## S4 method for signature 'gtypes'
getDescription(x, ...)

setDescription(x) <- value

## S4 replacement method for signature 'gtypes'
setDescription(x) <- value

getOther(x, ...)

## S4 method for signature 'gtypes'
getOther(x, value = NULL, ...)

setOther(x, name) <- value

## S4 replacement method for signature 'gtypes'
setOther(x, name) <- value

## S4 method for signature 'gtypes,ANY,ANY,ANY'
x[i, j, k, ..., quiet = TRUE, drop = FALSE]

Arguments

x

a gtypes object.
by.strata

logical - return results by strata?
...

other arguments passed from generics (ignored).
value

value being assigned by accessor.
as.haplotypes

return sequences as haplotypes? If TRUE, contents of @sequences slot are returned. If FALSE, one sequence per individual is returned.
seqName

the name (or number) of a set of sequences from the @sequences slot to return.
as.multidna

return sequences as a multidna object? If FALSE, sequences are returned as a list.
simplify

if 'getSequences()' would return a single locus, return it as a 'DNAbin' object ('TRUE'), or a single element named list ('FALSE').
name

name of the value going into the other list.
i, j, k

subsetting slots for individuals (i), loci (j), or strata (k). See Details for more information.
quiet

suppress warnings about unmatched requested individuals, loci, or strata?
drop

if TRUE the return object will have unused sequences removed.

Details

Indexing a gtypes object with integers, characters, or logicals with the [ operator follows the same rules as normal indexing in R. The order that individuals, loci, and strata are chosen is the order returned by getIndNames, getLocNames, and getStrataNames respectively. If unstratified samples are present, they can be selected as a group either by including NA in the character or numeric vector of the k slot, or by providing a logical vector based on is.na(strata(g)) to the i slot.

Value

nInd

number of individuals

nLoc

number of loci

nStrata

number of strata

indNames

vector of individual/sample names

locNames

vector of locus names

strataNames

vector of strata names for current scheme

ploidy

number of alleles at each locus

other

contents of @other slot

strata

return or modify the current stratification

schemes

return or modify the current stratification schemes

alleleNames

return a list of alleles at each locus

sequences

return the multidna object in the @sequences slot. See getSequences to extract individual genes or sequences from this object

description

return the object's description

Author(s)

Eric Archer [email protected]

Examples

#--- create a diploid (microsatellite) gtypes object
data(msats.g)
msats.g <- stratify(msats.g, "fine")

getNumStrata(msats.g)
getStrataNames(msats.g)
getNumLoci(msats.g)
getLociNames(msats.g)

# reassign all samples to two randomly chosen strata
new.strata <- sample(c("A", "B"), getNumInd(msats.g), rep = TRUE)
names(new.strata) <- getIndNames(msats.g)
setStrata(msats.g) <- new.strata
msats.g


#--- a sequence example
library(ape)
data(woodmouse)
genes <- list(gene1=woodmouse[,1:500], gene2=woodmouse[,501:965])
x <- new("multidna", genes)
wood.g <- sequence2gtypes(x)
new.strata <- sample(c("A", "B"), getNumInd(wood.g), rep = TRUE)
names(new.strata) <- getIndNames(wood.g)
setStrata(wood.g) <- new.strata
wood.g

# get the multidna sequence object
multi.seqs <- getSequences(wood.g, as.multidna = TRUE)
class(multi.seqs) # "multidna"

# get a list of DNAbin objects
dnabin.list <- getSequences(wood.g)
class(dnabin.list) # "list"

# get a DNAbin object of the first locus
dnabin.1 <- getSequences(wood.g)[[1]]
class(dnabin.1) # "DNAbin"

# getting and setting values in the `other` slot:
getOther(dloop.g)

setOther(dloop.g, "timestamp") <- timestamp()
setOther(dloop.g, "Author") <- "Hoban Washburne"

getOther(dloop.g)
getOther(dloop.g, "timestamp")

setOther(dloop.g, "Author") <- NULL
getOther(dloop.g)

Convert Between gtypes And genind objects.

Description

Convert a gtypes object to a genind object and vice-versa.

Usage

gtypes2genind(x, type = c("codom", "PA"))

genind2gtypes(x)

Arguments

x

either a gtypes or genind object to convert from.
type

a character string indicating the type of marker for genind objects: 'codom' stands for 'codominant' (e.g. microstallites, allozymes); 'PA' stands for 'presence/absence' markers (e.g. AFLP, RAPD).

Author(s)

Eric Archer [email protected]

See Also

initialize.gtypes, df2gtypes, sequence2gtypes, as.data.frame.gtypes, gtypes2loci

Examples

data(msats.g)

# Convert to genind
gi <- gtypes2genind(msats.g)
gi

# Convert to gtypes
gt <- genind2gtypes(gi)
gt

Convert Between gtypes And genlight objects.

Description

Convert a gtypes object to a genlight object and vice-versa.

Usage

gtypes2genlight(x)

genlight2gtypes(x)

Arguments

x

either a gtypes or genlight object to convert from.

Author(s)

Eric Archer [email protected]

See Also

initialize.gtypes, df2gtypes, sequence2gtypes, as.data.frame.gtypes, gtypes2loci, gtypes2genind

Examples

data(msats.g)

# Create simple simulated SNPs
gl1 <- adegenet::glSim(n.ind = 100, n.snp.nonstruc = 1000, ploidy = 2)
gl1

# Convert to gtypes
gt <- genlight2gtypes(gl1)
gt

# Convert back to genlight
gl2 <- gtypes2genlight(gt)
gl2

Convert Between gtypes And loci objects.

Description

Convert a gtypes object to a loci object.

Usage

gtypes2loci(x, sep = "/")

loci2gtypes(x, description = NULL, sep = "/")

Arguments

x

a gtypes or loci formatted object.
sep

character used to separate alleles at a locus.
description

a label for the gtypes object (optional).

Author(s)

Eric Archer [email protected]

See Also

initialize.gtypes, df2gtypes, sequence2gtypes, as.data.frame.gtypes, gtypes2genind

Examples

data(msats.g)

# Convert to loci
lc <- gtypes2loci(msats.g)  
lc  

# Convert to gtypes
gt <- loci2gtypes(lc)
gt

Convert Between gtypes And phyDat objects.

Description

Convert a gtypes object to a phyDat object.

Usage

gtypes2phyDat(x, locus = 1)

phyDat2gtypes(x, ...)

Arguments

x

a gtypes or phyDat formatted object.
locus

name or number of locus to convert.
...

optional arguments passed to sequence2gtypes.

Author(s)

Eric Archer [email protected]

See Also

initialize.gtypes, df2gtypes, sequence2gtypes, as.data.frame.gtypes, as.matrix.gtypes, gtypes2genind, gtypes2loci

Examples

data(dloop.g)

# Convert to phDat
pd <- gtypes2phyDat(dloop.g)
pd

# Convert to gtypes
gt <- phyDat2gtypes(pd)
gt

Heterozygosity

Description

Calculate observed and heterozygosity.

Usage

heterozygosity(g, by.strata = FALSE, type = c("expected", "observed"))

Arguments

g

a gtypes object.
by.strata

logical - return results by strata?
type

return expected or observed heterozygosity

Note

If g is a haploid object with sequences, the value for expected heterozygosity (= haplotpyic diversity) will be returned.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)

# Expected heterozygosity
heterozygosity(msats.g, type = "expected")

# Observed heterozygosity by strata
heterozygosity(msats.g, FALSE, "observed")

Hardy-Weinberg Equilibrium

Description

Calculate Hardy-Weinberg equilibrium p-values.

Usage

hweTest(
  g,
  use.genepop = FALSE,
  which = c("Proba", "excess", "deficit"),
  enumeration = FALSE,
  dememorization = 10000,
  batches = 20,
  num.rep = 5000,
  delete.files = TRUE,
  label = NULL
)

Arguments

g

a gtypes object.
use.genepop

logical. Use GENEPOP to calculate HWE p-values? If FALSE then hw.test is used.
which, enumeration, dememorization, batches

parameters for GENEPOP MCMC HWE procedure as defined in test_HW.
num.rep

the number of replicates for the Monte Carlo procedure for hw.test or number of iterations for test_HW.
delete.files

logical. Delete GENEPOP files when done?
label

character string to use to label GENEPOP files.

Value

a vector of p-values for each locus.

Author(s)

Eric Archer [email protected]

See Also

genepop, hw.test

Examples

data(msats.g)
hweTest(msats.g)

gtypes Constructor

Description

Create a new gtypes object using new("gtypes", ...), where '...' are arguments documented below.

Usage

## S4 method for signature 'gtypes'
initialize(
  .Object,
  gen.data,
  ploidy,
  ind.names = NULL,
  sequences = NULL,
  strata = NULL,
  schemes = NULL,
  description = NULL,
  other = NULL,
  remove.sequences = FALSE
)

Arguments

.Object

the object skeleton, automatically generated when calling new.
gen.data

a vector, matrix, or data.frame containing the alleles at each locus. See below for more details.
ploidy

ploidy of the loci.
ind.names

an optional vector of individual sample names.
sequences

an optional multidna object containing sequences represented by each locus.
strata

an optional stratification scheme from schemes.
schemes

an optional data.frame of stratification schemes.
description

an optional description for the object.
other

other optional information to include.
remove.sequences

logical. If TRUE any sequences not referenced in the object will be removed.

Details

For multi-allele loci, the gen.data argument should be formatted such that every consecutive ploidy columns represent alleles of one locus. Locus names are taken from the column names in gen.data and should be formatted with the same root locus name, with unique suffixes representing alleles (e.g., for Locus1234: Locus1234.1 and Locus1234.2, or Locus1234_A and Locus1234_B).
If gen.data is a vector it is assumed to represent haplotypes of a haploid marker. Sample names can be either in the rownames of gen.data or given separately in ind.names. If ind.names are provided, these are used in lieu of rownames in gen.data. If schemes has a column named 'id', it will be used to match to sample names in gen.data. Otherwise, if rownames are present in schemes, a column named 'id' will be created from them. If sequences are provided in sequences, then they should be named and match values in the haplotype column in gen.data. If multiple genes are given as a multidna object, it is assumed that they are in the same order as the columns in gen.data.

Author(s)

Eric Archer [email protected]

See Also

df2gtypes, sequence2gtypes

Test if object is gtypes

Description

Test if object is gtypes

Usage

is.gtypes(x)

Arguments

x

R object to be tested.

Value

Logical stating if 'x' is a gtypes object.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)
is.gtypes(msats.g) # TRUE

data(dolph.msats)
is.gtypes(dolph.msats) # FALSE

IUPAC Codes

Description

Calculate the correct IUPAC code for a vector of nucleotides.

Usage

iupacCode(bases, ignore.gaps = FALSE)

validIupacCodes(bases)

iupacMat()

Arguments

bases

character vector containing valid nucleotides or IUPAC codes.
ignore.gaps

logical. Ignore gaps at a site when creating consensus. If true, then bases with a gap are removed before consensus is calculated. If false and a gap is present, then the result is a gap.

Value

iupacCode a character representing the correct IUPAC code bases.
validIupacCodes a character vector of all valid IUPAC codes for bases.
iupacMat a logical matrix identifying valid IUPAC codes.

Author(s)

Eric Archer [email protected]

See Also

validIupacCodes

Examples

iupacCode(c("a", "a", "g"))

iupacCode(c("t", "c", "g"))

validIupacCodes(c("c", "t", "c", "c"))

validIupacCodes(c("c", "y", "c", "c"))

validIupacCodes(c("a", "g", "t", "a"))

Find and label haplotypes

Description

Identify and group sequences that share the same haplotype.

Usage

labelHaplotypes(x, prefix = NULL, use.indels = TRUE)

## Default S3 method:
labelHaplotypes(x, prefix = NULL, use.indels = TRUE)

## S3 method for class 'list'
labelHaplotypes(x, ...)

## S3 method for class 'character'
labelHaplotypes(x, ...)

## S3 method for class 'gtypes'
labelHaplotypes(x, ...)

Arguments

x

sequences in a character matrix, list, or DNAbin object, or a haploid gtypes object with sequences.
prefix

a character string giving prefix to be applied to numbered haplotypes. If NULL, haplotypes will be labeled with the first label from original sequences.
use.indels

logical. Use indels when comparing sequences?
...

arguments to be passed to labelHaplotypes.default.

Details

If any sequences contain ambiguous bases (N's) they are first removed. Then haplotypes are assigned based on the remaining sequences. The sequences with N's that were removed are then assigned to the new haplotypes if it can be done unambiguously (they match only one haplotype with 0 differences once the N's have been removed). If this can't be done they are assigned NAs and listed in the unassigned element.

Value

For character, list, or DNAbin, a list with the following elements:

haps

named vector (DNAbin) or list of named vectors (multidna) of haplotypes for each sequence in x.

hap.seqs

DNAbin or multidna object containing sequences for each haplotype.

unassigned

data.frame listing closest matching haplotypes for unassignable sequences with N's and the minimum number of substitutions between the two. Will be NULL if no sequences remain unassigned.

For gtypes, a new gtypes object with unassigned individuals stored in the @other slot in an element named 'haps.unassigned' (see getOther).

Author(s)

Eric Archer [email protected]

See Also

expandHaplotypes

Examples

# create 5 example short haplotypes
haps <- c(
  H1 = "ggctagct",
  H2 = "agttagct",
  H3 = "agctggct",
  H4 = "agctggct",
  H5 = "ggttagct"
)
# draw and label 100 samples
sample.seqs <- sample(names(haps), 100, rep = TRUE)
ids <- paste(sample.seqs, 1:length(sample.seqs), sep = "_")
sample.seqs <- lapply(sample.seqs, function(x) strsplit(haps[x], "")[[1]])
names(sample.seqs) <- ids

# add 1-2 random ambiguities
with.error <- sample(1:length(sample.seqs), 10)
for(i in with.error) {
  num.errors <- sample(1:2, 1)
  sites <- sample(1:length(sample.seqs[[i]]), num.errors)
  sample.seqs[[i]][sites] <- "n"
}

hap.assign <- labelHaplotypes(sample.seqs, prefix = "Hap.")
hap.assign

Convert Rmetasim landscape

Description

'landscape2gtypes' creates a gtypes object from an Rmetasim landscape object. 'landscape2df' creates a data.frame.

Usage

landscape2gtypes(Rland)

landscape2df(Rland)

Arguments

Rland

rmetasim landscape object

Author(s)

Eric Archer [email protected]

Linkage Disequlibrium

Description

Calculate linkage disequilibrium p-values using GENEPOP.

Usage

LDgenepop(
  g,
  dememorization = 10000,
  batches = 100,
  iterations = 5000,
  delete.files = TRUE,
  label = NULL
)

Arguments

g

a gtypes object.
dememorization, batches, iterations

parameters for GENEPOP MCMC LD procedure as defined in test_LD.
delete.files

logical. Delete GENEPOP input and output files when done?
label

character string to use to label GENEPOP input and output files.

Value

data.frame of disequilibrium estimates between pairs of loci

Author(s)

Eric Archer [email protected]

See Also

genepop

Examples

## Not run: 
data(msats.g)
msats.ld <- LDgenepop(msats.g)
head(msats.ld)

## End(Not run)

ldNe

Description

Estimate Ne from linkage disequilibrium based on Pearson correlation approximation following Waples et al 2016. Adapted from code by R. Waples and W. Larson.

Usage

ldNe(
  g,
  maf.threshold = 0,
  by.strata = FALSE,
  ci = 0.95,
  drop.missing = FALSE,
  num.cores = 1
)

Arguments

g

a gtypes object.
maf.threshold

smallest minimum allele frequency permitted to include a locus in calculation of Ne.
by.strata

apply the 'maf.threshold' by strata. If 'TRUE' then loci that are below this threshold in any strata will be removed from the calculation of Ne for all strata. Loci below 'maf.threshold' within a stratum are always removed for calculations of Ne for that stratum.
ci

central confidence interval.
drop.missing

drop loci with missing genotypes? If 'FALSE', a slower procedure is used where individuals with missing genotypes are removed in a pairwise fashion.
num.cores

The number of cores to use to distribute computations over. If set to NULL, the value will be what is reported by detectCores - 1.

Value

a data.frame with one row per strata and the following columns:

stratum

stratum being summarized

S

harmonic mean of sample size across pairwise comparisons of loci

num.comp

number of pairwise loci comparisons used

mean.rsq

mean r^2 over all loci

mean.E.rsq

mean expected r^2 over all loci

Ne

estimated Ne

param.lci, param.uci

parametric lower and upper CIs

Author(s)

Eric Archer [email protected]

References

Waples, R.S. 2006. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conservation Genetics 7:167-184.
Waples RK, Larson WA, and Waples RS. 2016. Estimating contemporary effective population size in non-model species using linkage disequilibrium across thousands of loci. Heredity 117:233-240; doi:10.1038/hdy.2016.60

Low Frequency Substitutions

Description

Check nucleotide sites for low frequency substitutions.

Usage

lowFreqSubs(x, min.freq = 3, motif.length = 10, simplify = TRUE)

Arguments

x

a DNAbin object.
min.freq

minimum frequency of base to be flagged.
motif.length

length of motif around low frequency base to output.
simplify

if there is a single locus, return result in a simplified form? If FALSE a list will be returned wth one element per locus.

Value

data.frame listing id, site number, and motif around low frequency base call.

Author(s)

Eric Archer [email protected]

Examples

data(dolph.haps)

lowFreqSubs(dolph.haps)

Minor Allele Frequencies

Description

Calculate minor allele frequencies for each locus.

Usage

maf(g, by.strata = FALSE, maf.within = FALSE)

Arguments

g

a gtypes object.
by.strata

logical - return results grouped by strata?
maf.within

if by.strata = TRUE, identify minor allele within each strata independently? If FALSE minor allele is identified from all individuals.

Value

A vector or matrix of minor allele frequencies at each locus.

Author(s)

Eric Archer [email protected]

See Also

alleleFreqs

Examples

data(msats.g)

maf(msats.g)

# minor allele identified from all indivudals
maf(msats.g, by.strata = TRUE)

# minor allele identified within each strata
maf(msats.g, by.strata = TRUE, maf.within = TRUE)

MAFFT Alignment

Description

Align a set of sequences using the MAFFT executable.

Usage

mafft(
  x,
  op = 3,
  ep = 0.123,
  maxiterate = 0,
  quiet = TRUE,
  num.cores = 1,
  opts = "--auto",
  simplify = TRUE
)

Arguments

x

a list or a matrix of DNA sequences (see write.dna).
op

gap opening penalty.
ep

offset value, which works like gap extension penalty.
maxiterate

number cycles of iterative refinement are performed.
quiet

logical. Run MAFFT quietly?
num.cores

The number of cores to use. If set to NULL, the value will be what is reported by detectCores - 1. Passed to MAFFT argument --thread.
opts

character string other options to provide to command line.
simplify

if TRUE, if x is a single sequence, a single DNAbin object is returned, otherwise, a list of alignments is returned.

Value

a DNAbin object with aligned sequences.

Note

MAFFT is not included with strataG and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. See the vignette for external.programs for installation instructions.

Author(s)

Eric Archer [email protected]

References

Katoh, M., Kumar, M. 2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30:3059-3066.
Available at: http://mafft.cbrc.jp/alignment/software

Examples

## Not run: 
data(dolph.seqs)
dolph.aln <- mafft(dolph.seqs, op = 3, ep = 2)
dolph.aln

## End(Not run)

Run MavericK

Description

Run MavericK clustering algorithm

Usage

maverickRun(
  g,
  params = NULL,
  label = "MavericK_files",
  data_fname = "data.txt",
  param_fname = "parameters.txt",
  exec = "Maverick1.0.5"
)

Arguments

g

a gtypes object.
params

a list specifying parameters for MavericK. All parameters are available and can be specified by partial matching. The function will automatically specify parameters related to data formatting (data, headerRow_on, missingData, ploidy, ploidyCol_on, popCol_on), so those will be ignored. For a full list of available parameters and their definitions, see the MavericK documentation distributed with the program.
label

folder where input and output files will be written to.
data_fname

file name of data input file.
param_fname

file name of parameters file.
exec

name of executable for MavericK.

Note

MavericK is not included with strataG and must be downloaded separately. It can be obtained from http://www.bobverity.com/. Additionally, it must be installed such that it can be run from the command line in the current working directory. See the vignette for external.programs for OS-specific installation instructions.

Author(s)

Eric Archer [email protected]

References

Robert Verity and Richard Nichols. (2016) Estimating the number of subpopulations (K) in structured populations. Genetics
Robert Verity and Richard Nichols. (2016) Documentation for MavericK software: Version 1.0

Read and Write MEGA

Description

Read and write MEGA formatted files.

Usage

read.mega(file)

write.mega(
  g,
  file = NULL,
  label = NULL,
  line.width = 60,
  locus = 1,
  as.haplotypes = TRUE
)

Arguments

file

a MEGA-formatted file of sequences.
g

a gtypes object.
label

label for MEGA filename (.meg). If NULL, the gtypes description is used if present.
line.width

width of sequence lines.
locus

number or name of locus to write.
as.haplotypes

output sequences as haplotypes? If TRUE, contents of @sequences slot are returned - treated as if they were haplotypes. If FALSE, one sequence per individual is returned.

Value

for read.mega, a list of:

title

title of MEGA file

dna.seq

DNA sequences in DNAbin format

Author(s)

Eric Archer [email protected]

References

Sudhir Kumar, Glen Stecher, and Koichiro Tamura (2015) MEGA7: Molecular Evolutionary Genetics Analysis version 7.0. Molecular Biology and Evolution (submitted). Available at: http://www.megasoftware.net

Most Distant Sequences

Description

Finds the set of sequences that are on the edges of the cloud of distances. These are the ones that have the greatest mean pairwise distance and greatest variance in distances.

Usage

mostDistantSequences(
  x,
  num.seqs = NULL,
  model = "raw",
  pairwise.deletion = TRUE,
  as.haplotypes = TRUE,
  simplify = TRUE
)

Arguments

x

a set of sequences or a gtypes object with sequences.
num.seqs

number of sequences to return. If NULL (default), all sequences are returned from most to least distant.
model

a character string specifying the evolutionary model to be used. See dist.dna for more information.
pairwise.deletion

a logical indicating whether to delete sites with missing data. See dist.dna for more information.
as.haplotypes

treat sequences as haplotypes (TRUE) or expand haplotypes to one sequence per individual (FALSE). If the latter, individual frequencies are used in cluster formation.
simplify

if there is a single locus, return result in a simplified form? If FALSE a list will be returned wth one element per locus.

Value

a vector of the num.seqs sequence names that are the most divergent sorted from greatest to least distant.

Author(s)

Eric Archer [email protected]

Examples

data(dolph.haps)

mostDistantSequences(dolph.haps, 5)

Representative Sequences

Description

Finds the set of sequences that represent the requested number of clusters.

Usage

mostRepresentativeSequences(
  x,
  num.seqs = NULL,
  model = "raw",
  pairwise.deletion = TRUE,
  as.haplotypes = TRUE,
  simplify = TRUE
)

Arguments

x

a DNAbin object.
num.seqs

number of sequences to return. If NULL (default), all sequences are returned.
model

a character string specifying the evolutionary model to be used. See dist.dna for more information.
pairwise.deletion

a logical indicating whether to delete sites with missing data. See dist.dna for more information.
as.haplotypes

treat sequences as haplotypes (TRUE) or expand haplotypes to one sequence per individual (FALSE). If the latter, individual frequencies are used in cluster formation.
simplify

if there is a single locus, return result in a simplified form? If FALSE a list will be returned wth one element per locus.

Value

a vector of the sequence names.

Author(s)

Eric Archer [email protected]

Examples

data(dolph.seqs)

mostRepresentativeSequences(dolph.seqs, 5)

mostRepresentativeSequences(dolph.seqs, 3)

M ratio

Description

Calculate Garza-Williamson M ratio (bottleneck) statistic for microsattelite data.

Usage

mRatio(g, by.strata = FALSE, rpt.size = 8:2)

Arguments

g

a gtypes object.
by.strata

calculate ratio for each stratum separately?
rpt.size

set of values to check for allele repeat size. Function will use the largest common denominator found in this vector or return NA.

Note

The function will only compute the metric for microastellite loci, which is defined as loci with allele labels that can be converted to numeric values in their entirety and have a fixed repeat size. NA is returned for all loci that do not have all numeric alleles. NA will also be returned if a locus is monomorphic, the locus has no genotypes, or a minimum repeat size cannot be found for all alleles at a locus.

Author(s)

Eric Archer [email protected]

References

Garza, J.C. and E.G. Williamson. 2001. Detection of reduction in population size using data form microsatellite loci. Molecular Ecology 10(2):305-318.

Examples

data(msats.g)

m.by.strata <- mRatio(msats.g, TRUE)
m.by.strata

m.overall <- mRatio(msats.g, FALSE)
m.overall

Dolphin Microsatellite gtypes Object

Description

A gtypes object of 126 samples and 4 microsatellite loci

Usage

data(msats.g)

Format

gtypes

References

Lowther-Thieleking J.L., F.I. Archer, A.R. Lang, and D.W. Weller. 2015. Genetic variation of coastal and offshore bottlenose dolphins, Tursiops truncatus, in the eastern North Pacific Ocean. Marine Mammal Science 31:1-20

Nei's Da

Description

Calcuate frequency-based Nei's Da for haploid or diploid data.

Usage

neiDa(g)

Arguments

g

a gtypes object.

Details

Returns Nei's Da for each pair of strata.

Author(s)

Eric Archer [email protected]

References

Nei et al 1983 Accuracy of Estimated Phylogenetic Trees from Molecular Data. J Mol Evol 19:153-170 (eqn 7)
Nei, M., and S. Kumar (2000) Molecular Evolution and Phylogenetics. Oxford University Press, Oxford. (pp. 268, eqn 13.6)

Examples

data(msats.g)

neiDa(msats.g)

Nucleotide Divergence

Description

Calculate distributions of between- and within-strata nucleotide divergence (sequence distance), which includes Nei's π\pi (usually referred to as "nucleotide diversity") and Nei's dA between strata.

Usage

nucleotideDivergence(g, probs = c(0, 0.025, 0.5, 0.975, 1), model = "raw", ...)

Arguments

g

a gtypes object.
probs

a numeric vector of probabilities of the pairwise distance distributions with values in 0:1.
model

evolutionary model to be used. see dist.dna for options.
...

other arguments passed to dist.dna.

Value

a list with summaries of the $within and $between strata pairwise distances including Nei's dA (in $between). Nei's π\pi is the mean between-strata divergence.

Author(s)

Eric Archer [email protected]

References

Nei, M., and S. Kumar (2000) Molecular Evolution and Phylogenetics. Oxford University Press, Oxford. (dA: pp. 256, eqn 12.67)

Examples

data(dloop.g)

nd <- nucleotideDivergence(dloop.g)
nd$within
nd$between

Nucleotide Diversity

Description

Calculate nucleotide diversity for set of sequences. Note that this is NOT Nei's nucleotide diversity (usually referred to as π\pi). Nei's π\pi is the mean number of nucleotide differences between sequences. See nucleotideDivergence for this value.

Usage

nucleotideDiversity(x, bases = c("a", "c", "g", "t"), simplify = TRUE)

Arguments

x

a set of sequences or a gtypes object with sequences.
bases

nucleotides to consider when calculating diversity.
simplify

if TRUE and only one loci exists, return a vector, otherwise, a list of vectors with one element per locus will be returned.

Value

Vector of diversity of nucleotides by site.

Author(s)

Eric Archer [email protected]

Examples

data(dloop.g)

nd <- nucleotideDiversity(dloop.g)
quantile(nd)

Number of Alleles

Description

Return the number of alleles for each locus.

Usage

numAlleles(g, by.strata = FALSE)

Arguments

g

a gtypes object.
by.strata

logical - return results grouped by strata?

Value

vector of number of alleles per locus.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)

numAlleles(msats.g)

Number of Individuals Genotyped

Description

Return the number of individuals genotyped for each locus.

Usage

numGenotyped(g, by.strata = FALSE, prop = FALSE)

Arguments

g

a gtypes object.
by.strata

logical - return results grouped by strata?
prop

logical determining whether to return proportion missing.

Value

vector of number of alleles per locus.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)

numGenotyped(msats.g)

Number Missing Data

Description

Calculate the number of individuals with missing data by locus.

Usage

numMissing(g, by.strata = FALSE, prop = FALSE)

Arguments

g

a gtypes object.
by.strata

logical - return results grouped by strata?
prop

logical determining whether to return proportion missing.

Value

a vector of loci with number (or, if prop = TRUE, the proportion) of individuals missing data for at least one allele.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)

numMissing(msats.g)
numMissing(msats.g, prop = TRUE)

Permute strata

Description

Permute the strata slot within a gtypes object.

Usage

permuteStrata(g)

Arguments

g

a gtypes object.

Value

a gtypes object with the strata randomly permuted.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)
msats.g <- stratify(msats.g, "fine")
summary(msats.g)

ran.msats <- permuteStrata(msats.g)
summary(ran.msats)

PHASE

Description

Run PHASE to estimate the phase of loci in diploid data.

Usage

phase(
  g,
  loci,
  positions = NULL,
  type = NULL,
  num.iter = 1e+05,
  thinning = 100,
  burnin = 1e+05,
  model = "new",
  ran.seed = NULL,
  final.run.factor = NULL,
  save.posterior = FALSE,
  in.file = "phase_in",
  out.file = "phase_out",
  delete.files = TRUE
)

phaseReadSample(out.file, type)

phaseReadPair(out.file)

phaseWrite(
  g,
  loci,
  positions = NULL,
  type = rep("S", length(loci)),
  in.file = "phase_in"
)

phasePosterior(ph.res, keep.missing = TRUE)

phaseFilter(ph.res, thresh = 0.5, keep.missing = TRUE)

Arguments

g

a gtypes object.
loci

vector or data.frame of loci in 'g' that are to be phased. If a data.frame, it should have columns named locus (name of locus in 'g'), group (number identifying loci in same linkage group), and position (integer identifying location of each locus in a linkage group).
positions

position along chromosome of each locus.
type

type of each locus.
num.iter

number of PHASE MCMC iterations.
thinning

number of PHASE MCMC iterations to thin by.
burnin

number of PHASE MCMC iterations for burnin.
model

PHASE model type.
ran.seed

PHASE random number seed.
final.run.factor

optional.
save.posterior

logical. Save posterior sample in output list?
in.file

name to use for PHASE input file.
out.file

name to use for PHASE output files.
delete.files

logical. Delete PHASE input and output files when done?
ph.res

result from phase.run.
keep.missing

logical. T = keep missing data from original data set. F = Use estimated genotypes from PHASE.
thresh

minimum probability for a genotype to be selected (0.5 - 1).

Details

phase runs PHASE assuming that the executable is installed properly and available on the command line.
phaseWrite writes a PHASE formatted file.
phaseReadPair reads the '_pair' output file.
phaseReadSample reads the '_sample' output file.
phaseFilter filters the result from phase.run to extract one genotype for each sample.
phasePosterior create a data.frame of all genotypes for each posterior sample.

Value

phase

a list containing:

locus.name new locus name, which is a combination of loci in group.
gtype.probs a data.frame listing the estimated genotype for every sample along with probability.
orig.gtypes the original gtypes object for the composite loci.
posterior a list of num.iter data.frames representing posterior sample of genotypes for each sample.
phaseWrite

a list with the input filename and the gtypes object used.

phaseReadPair

a data.frame of genotype probabilities.

phaseReadSample

a list of data.frames representing the posterior sample of genotypes for one set of loci for each sample.

phaseFilter

a matrix of genotypes for each sample.

phasePosterior

a list of data.frames representing the posterior sample of all genotypes for each sample.

Note

PHASE is not included with strataG and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. See the vignette for external.programs for installation instructions.

Author(s)

Eric Archer [email protected]

References

Stephens, M., and Donnelly, P. (2003). A comparison of Bayesian methods for haplotype reconstruction from population genotype data. American Journal of Human Genetics 73:1162-1169. Available at: http://stephenslab.uchicago.edu/software.html#phase

Examples

## Not run: 
data(bowhead.snps)
data(bowhead.snp.position)
snps <- df2gtypes(bowhead.snps, ploidy = 2, description = "Bowhead SNPS")
summary(snps)

# Run PHASE on all data
phase.results <- phase(snps, bowhead.snp.position, num.iter = 100, 
  save.posterior = FALSE)

# Filter phase results
filtered.results <- phaseFilter(phase.results, thresh = 0.5)

# Convert phased genotypes to gtypes
ids <- rownames(filtered.results)
strata <- bowhead.snps$Stock[match(ids, bowhead.snps$LABID)]
filtered.df <- cbind(id = ids, strata = strata, filtered.results)
phased.snps <- df2gtypes(filtered.df, ploidy = 2, description = "Bowhead phased SNPs")
summary(phased.snps)

## End(Not run)

Population Genetics Equations

Description

Collection of classical population genetics equations.

Usage

wrightFst(Ne, dispersal, gen.time, ploidy)

numGensEq(fst, Ne, gen.time)

fstToNm(fst, ploidy)

expectedNumAlleles(n, theta, ploidy)

Arguments

Ne

Effective population size.
dispersal

Migration rate in terms of probability of an individual migrating in a generation.
gen.time

Number of generations since ancestral population.
ploidy

Ploidy of the locus.
fst

value of Fst at equilibrium.
n

Sample size.
theta

Product of effective population size (Ne) and mutation rate (mu).

Details

wrightFst

Calculate Wright's Fst from Ne, dispersal, and generation time.

numGensEq

Calculate the number of generations to equilibrium based on a an ideal Wright model.

fstToNm

Calculate Nm (number of migrants per generation) for a given value of Fst.

expectedNumAlleles

Calculate the expected number of alleles in a sample of a given size and value of theta.

Value

wrightFst
numGensEq
fstToNm
expectedNumAlleles

a two element vector with the expected number of alleles (num.alleles) and variance (var.num.alleles).

Author(s)

Eric Archer [email protected]

References

Ewens, W. 1972. The sampling theory of selectively neutral alleles. Theoretical Population Biology 3:87-112. Eqns. 11 and 24.

Examples

dispersal <- seq(0.05, 0.8, by = 0.05)
fst <- wrightFst(100, dispersal, 20, 2)
plot(dispersal, fst, type = "l")

numGensEq(0.15, 100, 20)
numGensEq(0.3, 100, 20)
numGensEq(0.15, 50, 20)

fst <- seq(0.001, 0.2, length.out = 100)
Nm <- fstToNm(fst, 2)
plot(fst, Nm, type = "l")

expectedNumAlleles(20, 1, 2)
# double the samples
expectedNumAlleles(40, 1, 2)
# for a haploid locus
expectedNumAlleles(40, 1, 1)
# double theta
expectedNumAlleles(40, 2, 1)

Population Structure Tests

Description

Conduct multiple tests of population structure / differentiation. Overall tests can be conducted for the current stratification scheme (overallTest()), or can be conducted for all unique pairs of strata (pairwiseTest()). All statistics appropriate to the ploidy of the data are estimated at once. See Note for a description of each statistic.

Usage

overallTest(
  g,
  nrep = 1000,
  by.locus = FALSE,
  hap.locus = 1,
  quietly = FALSE,
  max.cores = 1,
  ...
)

pairwiseTest(
  g,
  nrep = 1000,
  by.locus = FALSE,
  hap.locus = 1,
  quietly = FALSE,
  max.cores = 1,
  ...
)

pairwiseMatrix(pws, stat, locus = "All")

pairwiseSummary(pws, locus = "All")

Arguments

g

a gtypes object.
nrep

number specifying number of permutation replicates to use for permutation test.
by.locus

return by-locus values of statistics? If TRUE the overall value will be contained in the first row, labelled "All". Only applies if the ploidy of g is > 1 (non-haploid).
hap.locus

which locus to use if g is haploid. Can be specified by number or name.
quietly

logical. print progress to screen?
max.cores

the maximum number of cores to use to distribute replicates for permutation tests over. If set to NULL, the value will be what is reported by detectCores - 1. If detectCores reports NA, max.cores will be set to 1 and parallel processing will not be done.
...

parameters passed to dist.dna for computation of pairwise distance matrix for AMOVA PHIst statistic.
pws

a list returned from a call to pairwiseTest().
stat

the name of a statistic in the $result element for pairwise comparisons returned by pairwiseTest().
locus

the name of a single locus. If "All", the overall result from all loci is returned. See by.locus.

Value

overallTest()

a list containing:

strata.freq

a table of the sample sizes for each stratum

result

an array with the statistic estimate and p-value for each statistic. If by.locus = FALSE or g is a haploid dataset, this is a two-dimensional array, with one row per statistic, statistic estimate in the first column and permutation test p-value in the second column. If by.locus = TRUE and g has ploidy > 1, then this is a three-dimensional array where the first dimension is loci, second dimension is statistics, and third dimension is statistic estimate and p-value.

pairwiseTest()

a list containing a list of results as described above for overallTest() for each pairwise comparison.

pairwiseMatrix()

a matrix summarizing a chosen statistic (stat) for a chosen locus (locus) between pairs of strata with the statistic estimate in the lower left and the p-value in the upper right.

pairwiseSummary()

a data frame summarizing all pairwise statistics and p-values along with strata sample sizes.

Note

The computed statistics are:

CHIsq chi-squared estimate measuring random allele frequency distribution distributed across strata (haploid and diploid)
Ho, Hs, Ht Nei and Kumar 2002 : observed heterozygosity (Ho), within population diversity (Hs), overall diversity (Ht)
Ht_prime description
Dst description
Dst_prime description
Fst For haploid data, equivalent to PHIst with pairwise distances set to 1. For diploid data,
Fst_prime description
Fis description
Gst_prime description
Gst_dbl_prime description
Dest, Dest_Chao population differentiation (Jost 2008)
wcFit, wcFst, wcFit (Weir and Cockerham 1984)
PHIst Haploid AMOVA estimate of differentiation derived from matrix of pairwise distances between sequences. See dist.dna for details on distance computation. (Excoffier et al 1992)

Author(s)

Eric Archer [email protected]

References

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. Jost, L. 2008. GST and its relatives do not measure differentiation. Molecular Ecology 17:4015-4026. Nei M. and Chesser R. 1983. Estimation of fixation indexes and gene diversities. Annals of Human Genetics 47:253-259. Nei M. 1987. Molecular Evolutionary Genetics. Columbia University Press 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.

See Also

basic.stats, Fst, amova

Examples

# An overall test with microsatellite data
data(msats.g)
ovl <- overallTest(msats.g, nrep = 100)
ovl

#' A pairwise test with control region sequences
data(dloop.g)
pws <- pairwiseTest(dloop.g, nrep = 100)
pws

Private Alleles

Description

The number of private alleles in each strata and locus.

Usage

privateAlleles(g)

Arguments

g

a gtypes object.

Value

matrix with the number of private alleles in each strata at each locus. This is the number of alleles only present in one stratum.

Author(s)

Eric Archer [email protected]

See Also

propUniqueAlleles

Examples

data(msats.g)

privateAlleles(msats.g)

Proportion Unique Alleles

Description

Calculate the proportion of alleles that are unique.

Usage

propUniqueAlleles(g, by.strata = FALSE)

Arguments

g

a gtypes object.
by.strata

logical - return results grouped by strata?

Value

a vector of the proportion of unique (occuring only in one individual) alleles for each locus.

Author(s)

Eric Archer [email protected]

See Also

privateAlleles

Examples

data(msats.g)

propUniqueAlleles(msats.g)

Read Genetic Data

Description

A wrapper for fread that sets common values for missing data and removes blank lines.

Usage

readGenData(file, na.strings = c("NA", "", "?", "."), ...)

Arguments

file

filename of .csv file.
na.strings

see fread.
...

other arguments passed to fread.

Value

a data.frame.

Author(s)

Eric Archer [email protected]

Remove Unused Sequences

Description

Remove sequences not used by samples.

Usage

removeUnusedSequences(g)

Arguments

g

a gtypes object.

Value

a new gtypes object with unused sequences removed.

Author(s)

Eric Archer [email protected]

Convert Sequences To gtypes

Description

Create a gtypes object from sequence data.

Usage

sequence2gtypes(
  x,
  strata = NULL,
  seq.names = NULL,
  schemes = NULL,
  description = NULL,
  other = NULL
)

Arguments

x

DNA sequences as a character matrix, a DNAbin object, or multidna object.
strata

a vector or factor giving stratification for each sequence. If not provided all individuals are assigned to the same stratum (Default).
seq.names

names for each set of sequences. If not provided default names are generated.
schemes

an optional data.frame of stratification schemes.
description

an optional label for the object.
other

a list to carry other related information (optional).

Value

a gtypes object.

Author(s)

Eric Archer [email protected]

See Also

gtypes.initialize, as.matrix.gtypes, as.data.frame.gtypes, gtypes2genind, gtypes2loci, gtypes2phyDat

Examples

#--- create a haploid sequence (mtDNA) gtypes object
data(dolph.strata)
data(dolph.seqs)
strata <- dolph.strata$fine
names(strata) <- dolph.strata$ids
dloop.fine <- sequence2gtypes(dolph.seqs, strata, seq.names = "dLoop", 
description = "dLoop: fine-scale stratification")

Sequence Likelihoods

Description

Calculate likelihood of each sequence based on gamma distribution of pairwise distances.

Usage

sequenceLikelihoods(
  x,
  model = "N",
  pairwise.deletion = FALSE,
  n = NULL,
  plot = TRUE,
  simplify = TRUE,
  ...
)

Arguments

x

a DNAbin object.
model

a character string specifying the evolutionary model to be used. Passed to dist.dna.
pairwise.deletion

a logical indicating whether to delete the sites with missing data in a pairwise way. Passed to dist.dna.
n

number of sequences with lowest delta(log-likelihoods) to plot. Defaults to all sequences Set to 0 to supress plotting.
plot

generate a plot of top n most unlikely sequences.
simplify

if there is a single locus, return result in a simplified form? If FALSE a list will be returned wth one element per locus.
...

arguments passed from other functions (ignored).

Details

Fits a Gamma distribution to the pairwise distances of sequences and calculates the log-likelihood for each (sum of all pairwise log-likelihoods for that sequence). Sequences that are extremely different from all others will have low log-likelihoods. Values returned as delta(log-likelhoods) = difference of log-likelihoods from maximum observed values.

Value

vector of delta(log-Likelihoods) for each sequence, sorted from smallest to largest, and a plot of their distributions.

Author(s)

Eric Archer [email protected]

Examples

data(dolph.haps)

sequenceLikelihoods(dolph.haps)

Site Frequency Spectrum

Description

Calculate the SFS from a data frame of SNP genotypes

Usage

sfs(
  x,
  strata.col = 2,
  locus.col = 3,
  fsc.dimnames = TRUE,
  sort.strata = TRUE,
  na.action = c("fail", "filter")
)

Arguments

x

a data frame of SNP genotypes where the first two columns are id and strata designations and SNPs start on the third column. SNP genotypes are coded as integers where 0 and 2 are the major and minor homozygotes and 1 is the heterozygote.
strata.col

column number that strata designations are in.
locus.col

column number that loci start in. All columns after this are assumed to be loci.
fsc.dimnames

format matrix dimnames for fastsimcoal2? If TRUE, then row and column names will be prefixed with the deme number (e.g., "d0_") that they represent.
sort.strata

if joint = TRUE, are strata to be sorted alphabetically? If FALSE then strata are taken in the order found in strata.col.
na.action

action to take if genotypes are missing for some samples. If "fail", an error is thrown if any genotypes are missing. If "filter", SNPs with missing genotypes are removed.

Value

A list of the marginal (1D) and joint (2D) site frequency spectra. If only one stratum is present, then $marginal will be NULL.

Author(s)

Eric Archer [email protected]

Shared Loci

Description

Calculate proportion of alleles and number of loci shared between pairs of individuals or strata.

Usage

propSharedLoci(g, type = c("strata", "ids"))

sharedAlleles(g, smry = c("num", "which"))

Arguments

g

a gtypes object.
type

a character vector determining type of pairwise comparsion. Can be "strata" for strata or "ids" for individuals.
smry

a character vector determining type of summary for sharedAlleles. "which" returns the names of the alleles shared. "num" returns the number of alleles shared.

Value

data.frame summary of pairwise shared loci.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)
msats.g <- stratify(msats.g, "fine")

sharedAlleles(msats.g)

## Not run: 
propSharedLoci(msats.g)

## End(Not run)

Simulate Haplotypes

Description

Simulate a haplotypic frequency distribution based on a specified gamma distribution.

Usage

simGammaHaps(pop.size, num.haps, shape, scale, return.freq = TRUE, plot = TRUE)

Arguments

pop.size

size of population.
num.haps

number of haplotypes to generate.
shape, scale

parameters of Gamma distribution (see dgamma).
return.freq

logical. Return frequency table of haplotypes? If FALSE return vector of haplotypes.
plot

logical. Show plot of haplotypic frequency distribution?

Value

Frequency table of haplotypes.

Author(s)

Eric Archer [email protected]

Examples

haps <- simGammaHaps(1000, 15, 1, 2.5)
print(haps)

Split Strata

Description

Return a list of gtypes for each stratum.

Usage

strataSplit(g, strata = NULL, remove.sequences = FALSE)

Arguments

g

a gtypes object.
strata

a character vector giving a subset of strata to select. If NULL then a list with all strata is created.
remove.sequences

logical. If TRUE any sequences not referenced in selected samples will not be in the returned object.

Value

A named list where each element is a gtypes object for a single stratum in g.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)

# Proportion of unique alleles in each stratum
msats.list <- strataSplit(msats.g)
lapply(msats.list, propUniqueAlleles)

Stratify gtypes

Description

Choose a new stratification scheme from the schemes slot in a gtypes object.

Usage

stratify(g, scheme = NULL, drop = TRUE)

Arguments

g

a gtypes object.
scheme

either the column name of a stratification scheme stored in the data.frame of the schemes slot of g, or a vector or factor identifying which stratum each sample belongs to. If NULL, all individuals are assigned to a single stratum named "Default".
drop

remove samples not assigned to a stratum? (those assigned NA in stratification scheme)

Value

A new gtypes object with an updated strata slot.

Note

If scheme is a vector or factor and has names, then the they will be used to match with getIndNames of g. Otherwise scheme should be the same length as the number of samples in g or values in scheme will be recycled as necessary.

Author(s)

Eric Archer [email protected]

See Also

getSchemes

Examples

data(msats.g)
msats.g

broad.msats <- stratify(msats.g, "broad")
broad.msats

STRUCTURE

Description

Run STRUCTURE to assess group membership of samples.

Usage

structureRun(
  g,
  k.range = NULL,
  num.k.rep = 1,
  label = NULL,
  delete.files = TRUE,
  exec = "structure",
  ...
)

structureWrite(
  g,
  label = NULL,
  maxpops = getNumStrata(g),
  burnin = 1000,
  numreps = 1000,
  noadmix = TRUE,
  freqscorr = FALSE,
  randomize = TRUE,
  seed = 0,
  pop.prior = NULL,
  locpriorinit = 1,
  maxlocprior = 20,
  gensback = 2,
  migrprior = 0.05,
  pfrompopflagonly = TRUE,
  popflag = NULL,
  inferalpha = FALSE,
  alpha = 1,
  unifprioralpha = TRUE,
  alphamax = 20,
  alphapriora = 0.05,
  alphapriorb = 0.001,
  ...
)

structureRead(file, pops = NULL)

Arguments

g

a gtypes object.
k.range

vector of values to for maxpop in multiple runs. If set to NULL, a single STRUCTURE run is conducted with maxpops groups. If specified, do not also specify maxpops.
num.k.rep

number of replicates for each value in k.range.
label

label to use for input and output files
delete.files

logical. Delete all files when STRUCTURE is finished?
exec

name of executable for STRUCTURE. Defaults to "structure".
...

arguments to be passed to structureWrite.
maxpops

number of groups.
burnin

number of iterations for MCMC burnin.
numreps

number of MCMC replicates.
noadmix

logical. No admixture?
freqscorr

logical. Correlated frequencies?
randomize

randomize.
seed

set random seed.
pop.prior

a character specifying which population prior model to use: "locprior" or "usepopinfo".
locpriorinit

parameterizes locprior parameter r - how informative the populations are. Only used when pop.prior = "locprior".
maxlocprior

specifies range of locprior parameter r. Only used when pop.prior = "locprior".
gensback

integer defining the number of generations back to test for immigrant ancestry. Only used when pop.prior = "usepopinfo".
migrprior

numeric between 0 and 1 listing migration prior. Only used when pop.prior = "usepopinfo".
pfrompopflagonly

logical. update allele frequencies from individuals specified by popflag. Only used when pop.prior = "usepopinfo".
popflag

a vector of integers (0, 1) or logicals identifiying whether or not to use strata information. Only used when pop.prior = "usepopinfo".
inferalpha

logical. Infer the value of the model parameter # from the data; otherwise is fixed at the value alpha which is chosen by the user. This option is ignored under the NOADMIX model. Small alpha implies that most individuals are essentially from one population or another, while alpha > 1 implies that most individuals are admixed.)
alpha

Dirichlet parameter for degree of admixture. This is the initial value if inferalpha = TRUE.
unifprioralpha

logical. Assume a uniform prior for alpha which runs between 0 and alphamax. This model seems to work fine; the alternative model (when unfprioralpha = 0) is to take alpha as having a Gamma prior, with mean alphapriora × alphapriorb, and variance alphapriora × alphapriorb^2.
alphamax

maximum for uniform prior on alpha when unifprioralpha = TRUE.
alphapriora, alphapriorb

parameters of Gamma prior on alpha when unifprioralpha = FALSE.
file

name of the output file from STRUCTURE.
pops

vector of population labels to be used in place of numbers in STRUCTURE file.

Value

structureRun

a list where each element is a list with results from structureRead and a vector of the filenames used

structureWrite

a vector of the filenames used by STRUCTURE

structureRead

a list containing:

summary

new locus name, which is a combination of loci in group

q.mat

data.frame of assignment probabilities for each id

prior.anc

list of prior ancestry estimates for each individual where population priors were used

files

vector of input and output files used by STRUCTURE

label

label for the run

Note

STRUCTURE is not included with strataG and must be downloaded separately. Additionally, it must be installed such that it can be run from the command line in the current working directory. See the vignette for external.programs for installation instructions.

Author(s)

Eric Archer [email protected]

References

Pritchard, J.K., M. Stephens, P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945-959.
http://web.stanford.edu/group/pritchardlab/structure.html

See Also

structurePlot, evanno, clumpp

Examples

## Not run: 
data(msats.g)

# Run STRUCTURE
sr <- structureRun(msats.g, k.range = 1:4, num.k.rep = 10)

# Calculate Evanno metrics
evno <- evanno(sr)
evno

# Run CLUMPP to combine runs for K = 2
q.mat <- clumpp(sr, k = 3)
q.mat

# Plot CLUMPP results
structurePlot(q.mat)

## End(Not run)

Plot STRUCTURE Results

Description

Plot Q-matrix from a call to structure or clumpp.

Usage

structurePlot(
  q.mat,
  pop.col = 3,
  prob.col = 4,
  sort.probs = TRUE,
  label.pops = TRUE,
  col = NULL,
  horiz = TRUE,
  type = NULL,
  legend.position = c("top", "left", "right", "bottom", "none"),
  plot = TRUE
)

Arguments

q.mat

matrix or data.frame of assignment probabilities.
pop.col

column number identifying original population designations.
prob.col

column number of first assignment probabilities to first group. It is assumed that the remainder of columns (prob.col:ncol(q.mat)) contain all assignment probabilities.
sort.probs

logical. Sort individuals by probabilities within populations? If FALSE individuals will be plotted as in q.mat.
label.pops

logical. Label the populations on the plot?
col

colors to use for each group.
horiz

logical. Plot bars horizontally.
type

either "area" for stacked continuous area plot or "bar" for discrete stacked bar chart. The latter is prefered for small numbers of samples. If not specified, a bar chart will be used if there are <= 100 samples.
legend.position

the position of the legend ("top", "left", "right", "bottom", or two-element numeric vector).
plot

display plot?

Value

invisibly, the ggplot object

Author(s)

Eric Archer [email protected]

See Also

structure, clumpp

Summarize Genotypes and Sequences

Description

Conducts a suite of data summaries. Summarizes missing data and homozygosity by individual and locus, and looks for duplicate genotypes (see dupGenotypes). For sequence data, identifies low frequency substitutions (see lowFreqSubs), and computes sequence likelihoods (see sequenceLikelihoods).

Usage

summarizeAll(g, write.files = FALSE, label = NULL, ...)

Arguments

g

a gtypes object.
write.files

logical determining whether to write .csv files of summaries
label

optional label for output folder and prefix for files.
...

optional arguments to pass on to summary functions.

Value

If write.files = TRUE, files are written for by-sample and by-locus summaries, and duplicate genotypes if any are found. If sequences are present, files are written identifying low frequency substitutions and sequence likelihoods.
The return value is a list with the following elements:

by.sample

data.frame of by-sample summaries

by.locus

data.frame of by-locus summaries

dup.df

data.frame identifying potential duplicates

by.seq

list of low frequency substitutions and haplotype likelihoods for each gene

Author(s)

Eric Archer [email protected]

See Also

summarizeInds, summarizeLoci, dupGenotypes, lowFreqSubs, sequenceLikelihoods

Individual Summaries

Description

Compile standard by-individual summaries.

Usage

summarizeInds(g)

Arguments

g

a gtypes object.

Value

A data.frame with rows for each sample and columns containing:

id

The individual id

stratum

The stratum of the individual

num.loci.missing.genotypes

The number of genotypes missing

pct.loci.missing.genotypes

The proportion of genotypes missing

pct.loci.homozygous

The proportion of loci homozygous

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)

summarizeInds(msats.g)

Locus Summaries

Description

Compile standard by-locus summaries.

Usage

summarizeLoci(g, by.strata = FALSE)

Arguments

g

a gtypes object.
by.strata

logical. If TRUE, return a list of summary matrices for each stratum.

Value

A matrix with rows for each locus and columns containing summaries of:

num.genotyped

The number of samples genotyped

prop.genotyped

The proportion of samples genotyped

num.alleles

The number of alleles in the locus

allelic.richness

The allelic richness of the locus

prop.unique.alleles

Proportion of alleles found in a single sample

expt.heterozygosity

Expected heterozygosity

obsvd.heterozygosity

Observed heterozygosity

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)
msats.g <- stratify(msats.g, "fine")

summarizeLoci(msats.g)

Sequence Summaries

Description

Summaries for each sequence.

Usage

summarizeSeqs(x)

Arguments

x

a DNAbin object.

Value

a matrix listing the start and end positions of each sequence (excluding beginning and trailing N's), the length, the number of N's, and the number of indels.

Author(s)

Eric Archer [email protected]

Examples

library(apex)
data(woodmouse)

summarizeSeqs(woodmouse)

Summarize gtypes Object

Description

Generate a summary of a gtypes object.

Usage

## S4 method for signature 'gtypes'
summary(object, ...)

Arguments

object

a gtypes object.
...

other arguments (ignored).

Value

a list with the following elements:

num.ind

number of individuals

num.loc

number of loci

num.strata

number of strata

unstratified

number of unstratified samples

schemes

names of stratification schemes

allele.freqs

a list with tables of allele frequencies by strata

strata.smry

a by-strata data.frame summarizing haplotypes or loci

locus.smry

a data.frame summarizing each locus for non-haploid objects, NULL for haploid objects

seq.smry

a summary of the sequence length and base frequencies

Author(s)

Eric Archer [email protected]

Tajima's D

Description

Calculate Tajima's D for a set of sequences to test for selection.

Usage

tajimasD(x, CI = 0.95)

Arguments

x

set of DNA sequences or a haploid gtypes object with sequences.
CI

desired central confidence interval.

Value

A named vector with the estimate for D and the p.value that it is different from 0.

Author(s)

Eric Archer [email protected]

References

Tajima, F. 1989. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123:585-595.

Examples

data(dolph.seqs)

tajimasD(dolph.seqs)

Theta

Description

Calculate theta from heterozygosity of each locus.

Usage

theta(g, by.strata = FALSE)

Arguments

g

a gtypes object.
by.strata

logical - return results grouped by strata?

Details

Calculates theta for each locus using the theta.h function.

Value

vector of theta values for each locus.

Author(s)

Eric Archer [email protected]

Examples

data(msats.g)

theta(msats.g)

Transition / Transversion Ratio

Description

Calculate transition/transversion ratio. Test substitution type of two bases.

Usage

TiTvRatio(x)

subType(b1, b2)

isTi(b1, b2)

isTv(b1, b2)

Arguments

x

a gtypes object with aligned sequences or a list of aligned DNA sequences.
b1, b2

two bases to be compared.

Value

TiTvRatio: a vector providing the number of transitions (Ti), transversions (Tv), and the transition/transversion ratio (Ti.Tv.ratio).
subType: either "ti" for transition, or "tv" for transversion.
isTi and isTv: a logical identifying whether the b1 to b2 is a transition or transversion.

Author(s)

Eric Archer [email protected]

Examples

data(dolph.seqs)

TiTvRatio(dolph.seqs)

subType("a", "c")

isTi("a", "c")

isTv("a", "c")

Trim N's From Sequences

Description

Removes N's from beginning and end of sequences.

Usage

trimNs(x)

Arguments

x

a DNAbin object or list or matrix that can be coerced into one.

Value

sequences with beginning and trailing N's removed.

Author(s)

Eric Archer [email protected]

Examples

test.seqs <- list(
   A = c(rep("n", 5), "a", "c", "g", "t", rep("n", 3)),
   B = c(rep("n", 3), "a", "c", "g", "t", rep("n", 5)),
   C = c("a", "c", "g", "t", rep("n", 8))
 )

test.seqs
trimmed <- trimNs(test.seqs)  
as.character(trimmed)

Variable Sites

Description

Identify variable sites among sequences.

Usage

variableSites(x, bases = c("a", "c", "g", "t", "-"), simplify = TRUE)

Arguments

x

a gtypes object with sequences, a DNAbin object, or a list of sequences.
bases

character vector of bases to consider.
simplify

if there is a single locus, return result in a simplified form? If FALSE a list will be returned wth one element per locus.

Value

A list with:

site

a DNAbin object composed of variable sites.

site.freqs

a matrix of base pair frequencies by site.

Author(s)

Eric Archer [email protected]

See Also

fixedSites

Examples

data(dolph.haps)

variableSites(dolph.haps)

Write NEXUS File for SNAPP

Description

Write NEXUS File for SNAPP

Usage

write.nexus.snapp(g, file = "snapp.data.nex")

Arguments

g

a gtypes object.
file

the filename the NEXUS file to output.

Author(s)

Eric Archer [email protected]

Write gtypes

Description

Write a gtypes object to file(s).

Usage

writeGtypes(
  g,
  label = NULL,
  folder = NULL,
  by.strata = TRUE,
  as.frequency = FALSE,
  freq.type = c("freq", "prop"),
  as.haplotypes = TRUE,
  ...
)

Arguments

g

a gtypes object.
label

label for filename(s). Default is the gtypes description if present.
folder

folder where file(s) should be written to. If NULL, files are written to current working directory.
by.strata

if as.frequency == TRUE, calculate frequencies by strata?
as.frequency

logical indicating if haploid data should be output as frequency tables.
freq.type

if as.frequency == TRUE, write absolute frequencies ("freq") or proportions ("prop").
as.haplotypes

write sequences as haplotypes (TRUE) or individual sequences (FALSE).
...

optional arguments controlling what information is included in the genotype file and how it is formatted passed to as.matrix.

Details

Writes a comma-delimited (.csv) file of genotypes and if sequences are present, a .fasta file for each locus. If haploid and as.frequency is TRUE, then frequency tables for each locus are written to separate files.

Author(s)

Eric Archer [email protected]

Examples

## Not run: 
# Write microsatellites with one column per locus
data(msats.g)
writeGtypes(msats.g, one.col = TRUE)

# Write control region data as frequency tables
data(dloop.g)
writeGtypes(dloop.g, as.frequency = TRUE)

## End(Not run)