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indexing dartR objects correctly...

Description

indexing dartR objects correctly...

Usage

## S4 method for signature 'dartR,ANY,ANY,ANY'x[i, j, ..., pop = NULL, treatOther = TRUE, quiet = TRUE, drop = FALSE]

Arguments

A genlight object created via the read.dart functions

Description

This a test data set to test the validity of functions within dartR and is based on a DArT SNP data set of simulated bandicoots across Australia. It contains 96 individuals and 1000 SNPs.

Usage

bandicoot.gl

Format

genlight object

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr

adjust cbind for dartR

Description

cbind is a bit lazy and does not take care for the metadata (so data in theother slot is lost). You can get most of the loci metadata back usinggl.compliance.check.

Usage

## S3 method for class 'dartR'cbind(...)

Arguments

Value

A genlight object

Examples

t1 <- platypus.glclass(t1) <- "dartR"t2 <- cbind(t1[,1:10],t1[,11:20])

Converts a genind object into a genlight object

Description

Converts a genind object into a genlight object

Usage

gi2gl(gi, parallel = FALSE, verbose = NULL)

Arguments

Details

Be aware due to ambiguity which one is the reference allele a combination ofgi2gl(gl2gi(gl)) does not return an identical object (but in terms ofanalysis this conversions are equivalent)

Value

A genlight object, with all slots filled.

Author(s)

Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

Estimates expected Heterozygosity

Description

Estimates expected Heterozygosity

Usage

gl.He(gl)

Arguments

Value

A simple vector whit Ho for each loci

Author(s)

Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

Estimates observed Heterozygosity

Description

Estimates observed Heterozygosity

Usage

gl.Ho(gl)

Arguments

Value

A simple vector whit Ho for each loci

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr)

Estimates effective population size using the Linkage Disequilibriummethod based on NeEstimator (V2)

Description

This function is basically a convenience function that runs the LD Neestimator using Neestimator2within R using the provided genlight object.To be able to do so, the software has to be downloaded from their website and the appropriate executable Ne2-1 has to be copied into the path as specified in the function. (see example below).

Usage

gl.LDNe(  x,  outfile = "genepopLD.txt",  outpath = tempdir(),  neest.path = getwd(),  critical = 0,  singleton.rm = TRUE,  mating = "random",  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors_pop = discrete_palette,  save2tmp = FALSE,  verbose = NULL)

Arguments

Value

Dataframe with the results as table

Author(s)

Custodian: Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

Examples

## Not run: # SNP data (use two populations and only the first 100 SNPs)pops <- possums.gl[1:60,1:100]nes <- gl.LDNe(pops, outfile="popsLD.txt", outpath=tempdir(),neest.path = "./path_to Ne-21",critical=c(0,0.05), singleton.rm=TRUE, mating='random')nes## End(Not run)

Calculates allele frequency of the first and second allele for each lociA very simple function to report allele frequencies

Description

Calculates allele frequency of the first and second allele for each lociA very simple function to report allele frequencies

Usage

gl.alf(x)

Arguments

Value

A simple data.frame with alf1, alf2.

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr)

Examples

#for the first 10 loci onlygl.alf(possums.gl[,1:10])barplot(t(as.matrix(gl.alf(possums.gl[,1:10]))))

Generates percentage allele frequencies by locus and population

Description

This is a support script, to take SNP data or SilicoDArT presence/absencedata grouped into populations in a genlight object {adegenet} and generatea table of allele frequencies for each population and locus

Usage

gl.allele.freq(x, percent = FALSE, by = "pop", simple = FALSE, verbose = NULL)

Arguments

Value

A matrix with allele (SNP data) or presence/absence frequencies(Tag P/A data) broken down by population and locus

Author(s)

Custodian: Arthur Georges (Post tohttps://groups.google.com/d/forum/dartr)

See Also

Other unmatched report:gl.report.heterozygosity()

Examples

gl.allele.freq(testset.gl,percent=FALSE,by='pop')gl.allele.freq(testset.gl,percent=FALSE,by="loc")gl.allele.freq(testset.gl,percent=FALSE,by="popxloc")gl.allele.freq(testset.gl,simple=TRUE)

Performs AMOVA using genlight data

Description

This script performs an AMOVA based on the genetic distance matrix fromstamppNeisD() [package StAMPP] using the amova() function from the packagePEGAS for exploring within and between population variation. For detailedinformation use their help pages: ?pegas::amova, ?StAMPP::stamppAmova. Beaware due to a conflict of the amova functions from various packages I hadto 'hack' StAMPP::stamppAmova to avoid a namespace conflict.

Usage

gl.amova(x, distance = NULL, permutations = 100, verbose = NULL)

Arguments

Value

An object of class 'amova' which is a list with a table of sums ofsquare deviations (SSD), mean square deviations (MSD), and the number ofdegrees of freedom, and a vector of variance components.

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr)

Examples

#permutations should be higher, here set to 1 because of speedout <- gl.amova(bandicoot.gl, permutations=1)

Population assignment using grm

Description

This function takes one individual and estimatestheir probability of coming from individual populationsfrom multilocus genotype frequencies.

Usage

gl.assign.grm(x, unknown, verbose = NULL)

Arguments

Details

This function is a re-implementation of the function multilocus_assignmentfrom package gstudio.Description of the method used in this function can be found at:https://dyerlab.github.io/applied_population_genetics/population-assignment.html

Value

Adata.frame consisting of assignment probabilities for eachpopulation.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

Examples

require("dartR.data")if ((requireNamespace("rrBLUP", quietly = TRUE)) &(requireNamespace("gplots", quietly = TRUE)) ) {res <- gl.assign.grm(platypus.gl,unknown="T27")}

Assign an individual of unknown provenance to population based on Mahalanobis Distance

Description

This script assigns an individual of unknown provenance to one or more targetpopulations based on the unknown individual's proximity to population centroids; proximity is estimated using Mahalanobis Distance.

The following process is followed:

1. An ordination is undertaken on the populations to again yield aseries of orthogonal (independent) axes.

2. A workable subset of dimensions is chosen, that specified, orequal to the number of dimensions with substantive eigenvalues, whichever isthe smaller.

3. The Mahalobalis Distance is calculated for the unknown against eachpopulation and probability of membership of each population is calculated.The assignment probabilities are listed in support of a decision.

Usage

gl.assign.mahalanobis(  x,  dim.limit = 2,  plevel = 0.999,  plot.out = TRUE,  unknown,  verbose = NULL)

Arguments

Details

There are three considerations to assignment. First, consider only thosepopulations for which the unknown has no private alleles. Private alleles arean indication that the unknown does not belong to a target population(provided that the sample size is adequate, say >=10). This can be evaluatedwith gl.assign.pa().

A next step is to consider the PCoA plot for populations where no privatealleles have been detected. The position of the unknown in relation to theconfidence ellipses is plotted by this script as a basis for narrowing downthe list of putative source populations. This can be evaluated with gl.assign.pca().

The third step (delivered by this script) is to consider the assignment probabilities based on the squared Generalised Linear Distance (Mahalanobis distance) of the unknown from the centroid for each population, then to consider the probability associated with its quantile using the Chisquare approximation. In effect, this index takes into account position of the unknown in relation to the confidence envelope in all selected dimensions of the ordination. The larger the assignment probability, the greater the confidence in the assignment.

If dim.limit is set to 2, to correspond with the dimensions used ingl.assign.pa(), then the output provides a ranking of the final setof putative source populations.

If dim.limit is set to be > 2, then this script provides a basis forfurther narrowing the set of putative populations.If the unknown individualis an extreme outlier, say at less than 0.001 probability of population membership (0.999 confidence envelope), then the associated population can be eliminated from further consideration.

Warning: gl.assign.mahal() treats each specified dimension equally, withoutregard to the percentage variation explained after ordination. If the unknown is an outlier in a lower dimension with an explanatory variance of,say, 0.1dimensions from the ordination.

Each of these above approaches provides evidence, none are 100They need to be interpreted cautiously.

In deciding the assignment, the script considers an individual to be anoutlier with respect to a particular population at alpha = 0.001 as default

Value

A data frame with the results of the assignment analysis.

Author(s)

Custodian: Arthur Georges –Post tohttps://groups.google.com/d/forum/dartr

Examples

## Not run: #Test run with a focal individual from the Macleay River (EmmacMaclGeor) test <- gl.assign.pa(testset.gl, unknown='UC_01044', nmin=10, threshold=1,verbose=3) test_2  <- gl.assign.pca(test, unknown='UC_01044', plevel=0.95, verbose=3)df <- gl.assign.mahalanobis(test_2, unknown='UC_01044', verbose=3)## End(Not run)

Eliminates populations as possible source populations for an individual of unknown provenance, using private alleles

Description

This script eliminates from consideration as putative source populations,those populations for which the individual has too many private alleles. Thepopulations that remain are putative source populations, subject to furtherconsideration.

The algorithm identifies those target populations for which the individualhas no private alleles or for which the number of private alleles does notexceed a user specified threshold.

An excessive count of private alleles is an indication that the unknown doesnot belong to a target population (provided that the sample size isadequate, say >=10).

Usage

gl.assign.pa(  x,  unknown,  nmin = 10,  threshold = 0,  n.best = NULL,  verbose = NULL)

Arguments

Value

A genlight object containing the focal individual (assigned topopulation 'unknown') and populations for which the focal individual is notdistinctive (number of loci with private alleles less than or equal to thethreshold). If no such populations, the genlight object contains only datafor the unknown individual.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.assign.pca

Examples

# Test run with a focal individual from the Macleay River (EmmacMaclGeor)  test <- gl.assign.pa(testset.gl, unknown='UC_00146', nmin=10, threshold=1,  verbose=3)

Assign an individual of unknown provenance to population based on PCA

Description

This script assigns an individual of unknown provenance to one or more targetpopulations based on its proximity to each population defined by aconfidence ellipse in ordinated space of two dimensions.

The following process is followed:

1. The space defined by the loci is ordinated to yield a series oforthogonal axes (independent), and the top two dimensions are considered.Populations for which the unknown lies outside the specified confidencelimits are no longer removed from the dataset.

Usage

gl.assign.pca(x, unknown, plevel = 0.999, plot.out = TRUE, verbose = NULL)

Arguments

Details

There are three considerations to assignment. First, consider only thosepopulations for which the unknown has no private alleles. Private alleles arean indication that the unknown does not belong to a target population(provided that the sample size is adequate, say >=10). This can be evaluatedwith gl.assign.pa().

A next step is to consider the PCoA plot for populations where no privatealleles have been detected and the position of the unknown in relation to theconfidence ellipses as is plotted by this script. Note, this plot isconsidering only the top two dimensions of the ordination, and so an unknownlying outside the confidence ellipse can be unambiguously interpreted as it lying outside the confidence envelope. However, if the unknown lies inside the confidence ellipse in two dimensions, then it may still lie outside the confidence envelope in deeper dimensions. This second step is good for eliminating populations from consideration, but does not provide confidence in assignment.

The third step is to consider the assignment probabilities, using the scriptgl.assign.mahalanobis(). This approach calculates the squared Generalised Linear Distance (Mahalanobis distance) of the unknown from the centroid for each population, and calculates the probability associated with its quantile under the zero truncated normal distribution. This index takes into account position of the unknown in relation to the confidence envelope in all selected dimensions of the ordination.

Each of these approaches provides evidence, none are 100need to be interpreted cautiously. They are best applied sequentially.

In deciding the assignment, the script considers an individual to be anoutlier with respect to a particular population at alpha = 0.001 as default.

Value

A genlight object containing only those populations that areputative source populations for the unknown individual.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

## Not run: #Test run with a focal individual from the Macleay River (EmmacMaclGeor) test <- gl.assign.pa(testset.gl, unknown='UC_00146', nmin=10, threshold=1,verbose=3) test_2 <- gl.assign.pca(test, unknown='UC_00146', plevel=0.95, verbose=3)## End(Not run)

Calculates basic statistics for each loci (Hs, Ho, Fis etc.)

Description

Based on functionbasic.stats. Check ?basic.statsfor help.

Usage

gl.basic.stats(x, digits = 4, verbose = NULL)

Arguments

Value

Several tables and lists with all basic stats.basic.stats for details.

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr)

Examples

if (!(requireNamespace("hierfstat", quietly = TRUE))) {out <- gl.basic.stats(possums.gl[1:10,1:100])}

Aligns nucleotides sequences against those present in a target database using blastn

Description

Basic Local Alignment Search Tool (BLAST; Altschul et al., 1990 &1997) is a sequence comparison algorithm optimized for speed used to searchsequence databases for optimal local alignments to a query. This functioncreates fasta files, creates databases to run BLAST, runs blastn and filtersthese results to obtain the best hit per sequence.

This function can be used to run BLAST alignment of short-read (DArTseqdata) and long-read sequences (Illumina, PacBio... etc). You can usereference genomes from NCBI, genomes from your private collection, contigs,scaffolds or any other genetic sequence that you would like to use asreference.

Usage

gl.blast(  x,  ref_genome,  task = "megablast",  Percentage_identity = 70,  Percentage_overlap = 0.8,  bitscore = 50,  number_of_threads = 2,  verbose = NULL)

Arguments

Details

Installing BLAST

You can download the BLAST installs from:https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/

It is important to install BLAST in a path that does not contain spaces forthis function to work.

Running BLAST

Four different tasks are supported:

• “megablast”, for verysimilar sequences (e.g, sequencing errors)

• “dc-megablast”, typicallyused for inter-species comparisons

• “blastn”, the traditional programused for inter-species comparisons

• “blastn-short”, optimized forsequences less than 30 nucleotides

If you are running a BLAST alignment of similar sequences, forexample Turtle Genome Vs Turtle Sequences, the recommended parametersare: task = “megablast”, Percentage_identity = 70, Percentage_overlap = 0.8and bitscore = 50.

If you are running a BLAST alignment of highly dissimilar sequences becauseyou are probably looking for sex linked hits in a distantly relatedspecies, and you are aligning for example sequences of Chicken Genome VsBassiana, the recommended parameters are: task = “dc-megablast”,Percentage_identity = 50, Percentage_overlap = 0.01 and bitscore = 30.

Be aware that running BLAST might take a long time (i.e. days) depending ofthe size of your query, the size of your database and the number of threadsselected for your computer.

BLAST output

The BLAST output is formatted as a table using output format 6, with columnsdefined in the following order:

• qseqid - Query Seq-id

• sacc - Subject accession

• stitle - Subject Title

• qseq - Alignedpart of query sequence

• sseq - Aligned part of subject sequence

• nident - Number of identical matches

• mismatch - Number of mismatches

• pident - Percentage of identical matches

• length - Alignmentlength

• evalue - Expect value

• bitscore - Bit score

• qstart -Start of alignment in query

• qend - End of alignment in query

• sstart - Start of alignment in subject

• send - End of alignment insubject

• gapopen - Number of gap openings

• gaps - Total number ofgaps

• qlen - Query sequence length

• slen - Subject sequence length

• PercentageOverlap - length / min(qlen,slen)

Databases containing unfiltered aligned sequences, filtered alignedsequences and one hit per sequence are saved to the temporal directory(tempdir) and can be accessed with the functiongl.print.reports and listed with the functiongl.list.reports. Note that they can be accessed only in thecurrent R session because tempdir is cleared each time that the R session isclosed.

BLAST filtering

BLAST output is filtered by ordering the hits of each sequence first by thehighest percentage identity, then the highest percentage overlap and thenthe highest bitscore. Only one hit per sequence is kept based on theseselection criteria.

Value

If the input is a genlight object: returns a genlight object with onehit per sequence merged to the slot $other$loc.metrics. If the input is afasta file: returns a dataframe with one hit per sequence.

Author(s)

Berenice Talamantes Becerra & Luis Mijangos (Post tohttps://groups.google.com/d/forum/dartr)

References

• Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D.J. (1990). Basic local alignment search tool. Journal of molecular biology,215(3), 403-410.

• Altschul, S. F., Madden, T. L., Schäffer, A. A., Zhang, J., Zhang,Z., Miller, W., & Lipman, D. J. (1997). Gapped BLAST and PSI-BLAST: a newgeneration of protein database search programs. Nucleic acids research,25(17), 3389-3402.

• Pearson, W. R. (2013). An introduction to sequence similarity(“homology”) searching. Current protocols in bioinformatics, 42(1), 3-1.

See Also

gl.print.history

Examples

## Not run: res <- gl.blast(x= testset.gl,ref_genome = 'sequence.fasta')# display of reports saved in the temporal directorygl.list.reports()# open the reports saved in the temporal directoryblast_databases <- gl.print.reports(1)## End(Not run)

Checks the current global verbosity

Description

The verbosity can be set in one of two ways – (a) explicitly by the user bypassing a value using the parameter verbose in a function, or (b) by settingthe verbosity globally as part of the r environment (gl.set.verbosity).

Usage

gl.check.verbosity(x = NULL)

Arguments

Value

The verbosity, in variable verbose

Author(s)

Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

Examples

gl.check.verbosity()

Checks the global working directory

Description

The working directory can be set in one of two ways – (a) explicitly by the user bypassing a value using the parameter plot.dir in a function, or (b) by settingthe working directory globally as part of the r environment (gl.setwd). The default is in acccordance to CRAN set to tempdir().

Usage

gl.check.wd(wd = NULL, verbose = NULL)

Arguments

Value

the working directory

Author(s)

Custodian: Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

Examples

gl.check.wd()

Collapses a distance matrix by amalgamating populations with pairwisefixed difference count less that a threshold

Description

This script takes a file generated by gl.fixed.diff and amalgamatespopulations with distance less than or equal to a specified threshold. Thedistance matrix is generated by gl.fixed.diff().

The script then applies the new population assignments to the genlight objectand recalculates the distance and associated matrices.

Usage

gl.collapse(fd, tpop = 0, tloc = 0, pb = FALSE, verbose = NULL)

Arguments

Value

A list containing the gl object x and the following square matrices:

1. $gl – the new genlight object with populations collapsed;

2. $fd – raw fixed differences;

3. $pcfd – percent fixed differences;

4. $nobs – mean no. of individuals used in each comparison;

5. $nloc – total number of loci used in each comparison;

6. $expfpos – NA's, populated by gl.fixed.diff [by simulation]

7. $expfpos – NA's, populated by gl.fixed.diff [by simulation]

8. $prob – NA's, populated by gl.fixed.diff [by simulation]

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

 fd <- gl.fixed.diff(testset.gl,tloc=0.05)fdfd2 <- gl.collapse(fd,tpop=1)fd2fd3 <- gl.collapse(fd2,tpop=1)fd3 fd <- gl.fixed.diff(testset.gl,tloc=0.05) fd2 <- gl.collapse(fd)

This is a helper function that supports the creation of color palettes for all plotting functions.

Description

This is a helper function that supports the creation of color palettes for all plotting functions.

Usage

gl.colors(type = 2)

Arguments

Examples

gl.colors(2)gl.colors("2")gl.colors("2c")#five discrete colorsgl.colors(type="dis")(5)#seven divergent colorsgl.colors("div")(7)

Checks a genlight object to see if it complies with dartRexpectations and amends it to comply if necessary

Description

This function will check to see that the genlight object conforms toexpectation in regard to dartR requirements (see details), and if it doesnot, will rectify it.

Usage

gl.compliance.check(x, verbose = NULL)

Arguments

Details

A genlight object used by dartR has a number of requirements that allowfunctions within the package to operate correctly. The genlight objectcomprises:

1. The SNP genotypes or Tag Presence/Absence data (SilicoDArT);

2. An associated dataframe (gl@other$loc.metrics) containing the locusmetrics (e.g. Call Rate, Repeatability, etc);

3. An associated dataframe (gl@other$ind.metrics) containing theindividual/sample metrics (e.g. sex, latitude (=lat), longitude(=lon), etc);

4. A specimen identity field (indNames(gl)) with the unique labels appliedto each individual/sample;

5. A population assignment (popNames) for each individual/specimen;

6. Flags that indicate whether or not calculable locus metrics have beenupdated.

Value

A genlight object that conforms to the expectations of dartR

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

Examples

x <- gl.compliance.check(testset.gl)x <- gl.compliance.check(testset.gs)

Calculates cost distances for a given landscape (resistance matrix)

Description

Calculates a cost distance matrix, to be used with run.popgensim.

Usage

gl.costdistances(landscape, locs, method, NN, verbose = NULL)

Arguments

Value

A costdistance matrix between all pairs of locs.

Examples

## Not run: data(possums.gl)library(raster)  #needed for that examplelandscape.sim <- readRDS(system.file('extdata','landscape.sim.rdata', package='dartR'))#calculate mean centers of individuals per populationxy <- apply(possums.gl@other$xy, 2, function(x) tapply(x, pop(possums.gl), mean))cd <- gl.costdistances(landscape.sim, xy, method='leastcost', NN=8)round(cd,3)## End(Not run)

Defines a new population in a genlight object for specified individuals

Description

The script reassigns existing individuals to a new population and removestheir existing population assignment.

The script returns a genlight object with the new population assignment.

Usage

gl.define.pop(x, ind.list, new, verbose = NULL)

Arguments

Value

A genlight object with the redefined population structure.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

popNames(testset.gl)gl <- gl.define.pop(testset.gl, ind.list=c('AA019073','AA004859'), new='newguys')popNames(gl)indNames(gl)[pop(gl)=='newguys']

Provides descriptive stats and plots to diagnose potential problemswith Hardy-Weinberg proportions

Description

Different causes may be responsible for lack of Hardy-Weinbergproportions. This function helps diagnose potential problems.

Usage

gl.diagnostics.hwe(  x,  alpha_val = 0.05,  bins = 20,  stdErr = TRUE,  colors_hist = two_colors,  colors_barplot = two_colors_contrast,  plot_theme = theme_dartR(),  save2tmp = FALSE,  n.cores = "auto",  verbose = NULL)

Arguments

Details

This function initially runsgl.report.hwe and reportsthe ternary plots. The remaining outputs follow the recommendations fromWaples(2015) paper and De Meeûs 2018. These include:

1. A histogramwith the distribution of p-values of the HWE tests. The distribution shouldbe roughly uniform across equal-sized bins.

2. A bar plot with observedand expected (null expectation) number of significant HWE tests for the samelocus in multiple populations (that is, the x-axis shows whether a locusresults significant in 1, 2, ..., n populations. The y axis is the count ofthese occurrences. The zero value on x-axis shows the number ofnon-significant tests). If HWE tests are significant by chance alone,observed and expected number of HWE tests should have roughly a similardistribution.

3. A scatter plot with a linear regression between Fst and Fis,averaged across subpopulations. De Meeûs 2018 suggests that in the case ofNull alleles, a strong positive relationship is expected (together with theFis standard error much larger than the Fst standard error, see below).Note, this is not the scatter plot that Waples 2015 presents in hispaper. In the lower right corner of the plot, the Pearson correlationcoefficient is reported.

4. The Fis and Fst (averaged over loci andsubpopulations) standard errors are also printed on screen and reported inthe returned list (ifstdErr=TRUE). These are computed with the Jackknife method over loci (See De Meeûs 2007 for details on how this is computed) and it may take some time for these computations to complete. De Meeûs 2018 suggests that under a global significant heterozygosity deficit:

   - if thecorrelation between Fis and Fst is strongly positive, and StdErrFis >>StdErrFst, Null alleles are likely to be the cause.

   - if the correlationbetween Fis and Fst is ~0 or mildly positive, and StdErrFis > StdErrFst,Wahlund may be the cause.

   - if the correlation between Fis and Fst is ~0, andStdErrFis ~ StdErrFst, selfing or sib mating could to be the cause.

   It isimportant to realise that these statistics only suggest a pattern (pointers).Their absence is not conclusive evidence of the absence of the problem, as their presence does not confirm the cause of the problem.

5. A table where thenumber of observed and expected significant HWE tests are reported by eachpopulation, indicating whether these are due to heterozygosity excess ordeficiency. These can be used to have a clue of potential problems (e.g.deficiency might be due to a Wahlund effect, presence of null alleles ornon-random sampling; excess might be due to sex linkage or differentselection between sexes, demographic changes or small Ne. See Table 1 inWapples 2015). The last two columns of the table generated by this functionreport chisquare values and their associated p-values. Chisquare is computedfollowing Fisher's procedure for a global test (Fisher 1970). This basicallytests whether there is at least one test that is truly significant in theseries of tests conducted (De Meeûs et al 2009).

Value

A list with the table with the summary of the HWE tests and (if stdErr=TRUE) a named vector with the StdErrFis and StdErrFst.

Author(s)

Custodian: Carlo Pacioni – Post tohttps://groups.google.com/d/forum/dartr

References

• de Meeûs, T., McCoy, K.D., Prugnolle, F.,Chevillon, C., Durand, P., Hurtrez-Boussès, S., Renaud, F., 2007. Populationgenetics and molecular epidemiology or how to “débusquer la bête”. Infection,Genetics and Evolution 7, 308-332.

• De Meeûs, T., Guégan, J.-F., Teriokhin, A.T., 2009. MultiTest V.1.2, a program to binomially combine independent tests and performance comparison with other related methods onproportional data. BMC Bioinformatics 10, 443-443.

• De Meeûs, T., 2018. Revisiting FIS, FST, Wahlund Effects, and Null Alleles. Journal of Heredity 109, 446-456.

• Fisher, R., 1970.Statistical methods for research workers Edinburgh: Oliver and Boyd.

• Waples, R. S. (2015). Testing for Hardy–Weinberg proportions: have we lostthe plot?. Journal of heredity, 106(1), 1-19.

See Also

gl.report.hwe

Examples

## Not run: require("dartR.data")res <- gl.diagnostics.hwe(x = gl.filter.allna(platypus.gl[,1:50]), stdErr=FALSE, n.cores=1)## End(Not run)

Comparing simulations against theoretical expectations

Description

Comparing simulations against theoretical expectations

Usage

gl.diagnostics.sim(  x,  Ne,  iteration = 1,  pop_he = 1,  pops_fst = c(1, 2),  plot_theme = theme_dartR(),  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

Two plots are presented comparing the simulations against theoretical expectations:

1. Expected heterozygosity under neutrality (Crow & Kimura, 1970, p. 329) is calculated as:

   Het = He0(1-(1/2Ne))^t,

   where Ne is effective population size, He0 is heterozygosity at generation 0and t is the number of generations.

2. Expected FST under neutrality (Takahata, 1983) is calculated as:

   FST=1/(4Nem(n/(n-1))^2+1),

   where Ne is effective populations size of each individual subpopulation, m isdispersal rate and n the number of subpopulations (always 2).

Value

Returns plots comparing simulations against theoretical expectations

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

References

• Crow JF, Kimura M. An introduction to population genetics theory. An introduction to population genetics theory. 1970.

• Takahata N. Gene identity and genetic differentiation of populations in the finite island model. Genetics. 1983;104(3):497-512.

See Also

gl.filter.callrate

Examples

## Not run: ref_table <- gl.sim.WF.table(file_var=system.file('extdata', 'ref_variables.csv', package = 'dartR'),interactive_vars = FALSE)res_sim <- gl.sim.WF.run(file_var = system.file('extdata', 'sim_variables.csv', package ='dartR'),ref_table=ref_table,interactive_vars = FALSE,number_pops_phase2=2,population_size_phase2="50 50")res <- gl.diagnostics.sim(x=res_sim,Ne=50)## End(Not run)

Calculates a distance matrix for individuals defined in a genlight object

Description

This script calculates various distances between individuals based on allelefrequencies or presence-absence data

Usage

gl.dist.ind(  x,  method = NULL,  scale = FALSE,  swap = FALSE,  output = "dist",  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The distance measure for SNP genotypes can be one of:

• Euclidean Distance [method = "Euclidean"]

• Scaled Euclidean Distance [method='Euclidean", scale=TRUE]

• Simple Mismatch Distance [method="Simple"]

• Absolute Mismatch Distance [method="Absolute"]

• Czekanowski (Manhattan) Distance [method="Manhattan"]

The distance measure for Sequence Tag Presence/Absence data (binary) can be one of:

• Euclidean Distance [method = "Euclidean"]

• Scaled Euclidean Distance [method='Euclidean", scale=TRUE]

• Simple Matching Distance [method="Simple"]

• Jaccard Distance [method="Jaccard"]

• Bray-Curtis Distance [method="Bray-Curtis"]

Refer to the dartR Technical Note on Distances in Genetics.

Value

An object of class 'matrix' or dist' giving distances between individuals

Author(s)

Author(s): Arthur Georges. Custodian: Arthur Georges – Post to #'https://groups.google.com/d/forum/dartr

Examples

 D <- gl.dist.ind(testset.gl[1:20,], method='manhattan')D <- gl.dist.ind(testset.gs[1:20,], method='Jaccard',swap=TRUE)D <- gl.dist.ind(testset.gl[1:20,], method='euclidean',scale=TRUE)

Calculates a distance matrix for populations with SNP genotypes in agenlight object

Description

This script calculates various distances between populations based on allelefrequencies (SNP genotypes) or frequency of presences in presence-absence data (Euclidean and Fixed-diff distances only).

Usage

gl.dist.pop(  x,  method = "euclidean",  plot.out = TRUE,  scale = FALSE,  output = "dist",  plot_theme = theme_dartR(),  plot_colors = two_colors,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The distance measure can be one of 'euclidean', 'fixed-diff', 'reynolds','nei' and 'chord'. Refer to the documentation of functionsdescribed in the the dartR Distance Analysis tutorial for algorithmsand definitions.

Value

An object of class 'dist' giving distances between populations

Author(s)

author(s): Arthur Georges. Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

 ## Not run: # SNP genotypesD <- gl.dist.pop(possums.gl[1:90,1:100], method='euclidean')D <- gl.dist.pop(possums.gl[1:90,1:100], method='euclidean',scale=TRUE)#D <- gl.dist.pop(possums.gl, method='nei')#D <- gl.dist.pop(possums.gl, method='reynolds')#D <- gl.dist.pop(possums.gl, method='chord')#D <- gl.dist.pop(possums.gl, method='fixed-diff')#Presence-Absence data [only 10 individuals due to speed]D <- gl.dist.pop(testset.gs[1:10,], method='euclidean')## End(Not run)res <- gl.dist.pop(platypus.gl)

Removes specified individuals from a dartR genlight object

Description

This function deletes individuals and their associated metadata.Monomorphic loci and loci that are scored all NA are optionally deleted (mono.rm=TRUE). The script also optionally recalculates locus metatdata statistics to accommodatethe deletion of individuals from the dataset (recalc=TRUE).

The script returns a dartR genlight object with the retained individuals and the recalculated locus metadata. The script works with both genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

Usage

gl.drop.ind(x, ind.list, recalc = FALSE, mono.rm = FALSE, verbose = NULL)

Arguments

Value

A reduced dartR genlight object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.keep.ind to keep rather than drop specifiedindividuals

Other dartR-base:gl.drop.loc(),gl.drop.pop(),gl.edit.recode.ind(),gl.edit.recode.pop(),gl.keep.loc(),gl.make.recode.ind(),gl.read.dart(),gl.recode.ind(),gl.recode.pop(),gl.set.verbosity()

Examples

 # SNP data   gl2 <- gl.drop.ind(testset.gl,   ind.list=c('AA019073','AA004859')) # Tag P/A data   gs2 <- gl.drop.ind(testset.gs,   ind.list=c('AA020656','AA19077','AA004859'))   gs2 <- gl.drop.ind(testset.gs, ind.list=c('AA020656'   ,'AA19077','AA004859'),mono.rm=TRUE, recalc=TRUE)

Removes specified loci from a dartR genlight object

Description

This function deletes individuals and their associated metadata.

The script returns a dartR genlight object with the retained loci. The script works with both genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

Usage

gl.drop.loc(  x,  loc.list = NULL,  first_tmp = NULL,  last_tmp = NULL,  verbose = NULL)

Arguments

Value

A reduced dartR genlight object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.keep.loc to keep rather than drop specified loci

Other dartR-base:gl.drop.ind(),gl.drop.pop(),gl.edit.recode.ind(),gl.edit.recode.pop(),gl.keep.loc(),gl.make.recode.ind(),gl.read.dart(),gl.recode.ind(),gl.recode.pop(),gl.set.verbosity()

Examples

# SNP data  gl2 <- gl.drop.loc(testset.gl, loc.list=c('100051468|42-A/T', '100049816-51-A/G'),verbose=3)# Tag P/A data  gs2 <- gl.drop.loc(testset.gs, loc.list=c('20134188','19249144'),verbose=3)

Removes specified populations from a dartR genlight object

Description

Individuals are assigned to populations based on associated specimen metadatastored in the dartR genlight object. This function deletes all individuals in the nominated populations (pop.list).Monomorphic loci and loci that are scored all NA are optionally deleted (mono.rm=TRUE). The script also optionally recalculates locus metatdata statistics to accommodatethe deletion of individuals from the dataset (recalc=TRUE).

The script returns a dartR genlight object with the retained populations and the recalculated locus metadata. The script works with both genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

Usage

gl.drop.pop(  x,  pop.list,  as.pop = NULL,  recalc = FALSE,  mono.rm = FALSE,  verbose = NULL)

Arguments

Value

A reduced dartR genlight object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.keep.pop to keep rather than drop specified populations

Other dartR-base:gl.drop.ind(),gl.drop.loc(),gl.edit.recode.ind(),gl.edit.recode.pop(),gl.keep.loc(),gl.make.recode.ind(),gl.read.dart(),gl.recode.ind(),gl.recode.pop(),gl.set.verbosity()

Examples

 # SNP data   gl2 <- gl.drop.pop(testset.gl,   pop.list=c('EmsubRopeMata','EmvicVictJasp'),verbose=3)   gl2 <- gl.drop.pop(testset.gl, pop.list=c('EmsubRopeMata','EmvicVictJasp'),   mono.rm=TRUE,recalc=TRUE)   gl2 <- gl.drop.pop(testset.gl,as.pop='sex',pop.list=c('Male','Unknown'),verbose=3) # Tag P/A data   gs2 <- gl.drop.pop(testset.gs, pop.list=c('EmsubRopeMata','EmvicVictJasp'))

Creates or edits individual (=specimen) names, creates a recode_indfile and applies the changes to a genlight object

Description

A function to edit names of individual in a dartR genlight object, or to create areassignment table taking the individual labels from a genlight object, or toedit existing individual labels in an existing recode_ind file. The amended recode table is then applied to the genlight object.

Usage

gl.edit.recode.ind(  x,  out.recode.file = NULL,  outpath = tempdir(),  recalc = FALSE,  mono.rm = FALSE,  verbose = NULL)

Arguments

Details

Renaming individuals may be required when there have been errors in labelingarising in the passage of samples to sequencing. There may be occasionswhere renaming individuals is required for preparation of figures.

This function will input an existing recode table for editing and optionallysave it as a new table, or if the name of an input table is not supplied,will generate a table using the individual labels in the parent genlightobject.

When caution needs to be exercised because of the potential for breaking the'chain of evidence' associated with the samples, recoding individuals usinga recode table (csv) can provide a durable record of the changes.

For SNP genotype data, the function, having deleted individuals, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them. The script also optionally recalculates thelocus metadata as appropriate. The optional deletion of monomorphic lociand the optional recalculation of locus statistics is not available forTag P/A data (SilicoDArT).

Use outpath=getwd() when calling this function to directoutput files to your working directory.

The function returns a dartR genlight object with the new population assignments and the recalculated locus metadata.

Value

An object of class ('genlight') with the revised individual labels.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.recode.ind,gl.drop.ind,gl.keep.ind

Other dartR-base:gl.drop.ind(),gl.drop.loc(),gl.drop.pop(),gl.edit.recode.pop(),gl.keep.loc(),gl.make.recode.ind(),gl.read.dart(),gl.recode.ind(),gl.recode.pop(),gl.set.verbosity()

Examples

## Not run: gl <- gl.edit.recode.ind(testset.gl)gl <- gl.edit.recode.ind(testset.gl, out.recode.file='ind.recode.table.csv')## End(Not run)

Creates or edits and applies a population re-assignment table

Description

A function to edit population assignments in a dartR genlight object, or tocreate a reassignment table taking the population assignmentsfrom a genlight object, or to edit existing population assignments ina pop.recode.table. The amended recode table is then applied to the genlightobject.

Usage

gl.edit.recode.pop(  x,  pop.recode = NULL,  out.recode.file = NULL,  outpath = tempdir(),  recalc = FALSE,  mono.rm = FALSE,  verbose = NULL)

Arguments

Details

Genlight objects assign specimens to populations based on information in theind.metadata file provided when the genlight object is first generated.Often one wishes to subset the data by deleting populations or to amalgamatepopulations. This can be done with a pop.recode table with two columns. Thefirst column is the population assignment in the genlight object, the secondcolumn provides the new assignment.

This function will input an existing reassignment table for editing andoptionally save it as a new table, or if the name of an input table is notsupplied, will generate a table using the population assignments in theparent genlight object. It will then apply the recodings to the genlight object.

When caution needs to be exercised because of the potential for breaking the'chain of evidence' associated with the samples, recoding individuals usinga recode table (csv) can provide a durable record of the changes.

For SNP genotype data, the function, having deleted populations, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them. The script also optionally recalculates thelocus metadata as appropriate. The optional deletion of monomorphic lociand the optional recalculation of locus statistics is not available forTag P/A data (SilicoDArT).

Use outpath=getwd() when calling this function to directoutput files to your working directory.

The function returns a dartR genlight object with the new population assignments and the recalculated locus metadata.

Value

A genlight object with the revised population assignments

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.recode.pop,gl.drop.pop,gl.keep.pop,gl.merge.pop,gl.reassign.pop

Other dartR-base:gl.drop.ind(),gl.drop.loc(),gl.drop.pop(),gl.edit.recode.ind(),gl.keep.loc(),gl.make.recode.ind(),gl.read.dart(),gl.recode.ind(),gl.recode.pop(),gl.set.verbosity()

Examples

## Not run: gl <- gl.edit.recode.pop(testset.gl)gs <- gl.edit.recode.pop(testset.gs)## End(Not run)# See also -------------------

Creates an Evanno plot from a STRUCTURE run object

Description

This function takes a genlight object and runs a STRUCTURE analysis based onfunctions fromstrataG

Usage

gl.evanno(sr, plot.out = TRUE)

Arguments

Details

The function is basically a convenient wrapper around the beautifulstrataG functionevanno (Archer et al. 2016). For a detaileddescription please refer to this package (see references below).

Value

An Evanno plot is created and a list of all four plots is returned.

Author(s)

Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

References

• Pritchard, J.K., Stephens, M., Donnelly, P. (2000) Inference ofpopulation structure using multilocus genotype data. Genetics 155, 945-959.

• Archer, F. I., Adams, P. E. and Schneiders, B. B. (2016) strataG: An Rpackage for manipulating, summarizing and analysing population genetic data.Mol Ecol Resour. doi:10.1111/1755-0998.12559

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

See Also

gl.run.structure,clumpp,

Examples

## Not run: #CLUMPP and STRUCTURE need to be installed to be able to run the example#bc <- bandicoot.gl[,1:100]#sr <- gl.run.structure(bc, k.range = 2:5, num.k.rep = 3, exec = './structure.exe')#ev <- gl.evanno(sr)#ev#qmat <- gl.plot.structure(sr, k=3, CLUMPP='d:/structure/')#head(qmat)#gl.map.structure(qmat, bc, scalex=1, scaley=0.5)## End(Not run)

Estimates the rate of false positives in a fixed difference analysis

Description

This function takes two populations and generates allele frequency profilesfor them. It then samples an allele frequency for each, at random, andestimates a sampling distribution for those two allele frequencies. Drawingtwo samples from those sampling distributions, it calculates whether or notthey represent a fixed difference. This is applied to all loci, and thenumber of fixed differences so generated are counted, as an expectation. Thescript distinguished between true fixed differences (with a tolerance ofdelta), and false positives. The simulation is repeated a given number oftimes (default=1000) to provide an expectation of the number of falsepositives, given the observed allele frequency profiles and the sample sizes.The probability of the observed count of fixed differences is greater thanthe expected number of false positives is calculated.

Usage

gl.fdsim(  x,  poppair,  obs = NULL,  sympatric = FALSE,  reps = 1000,  delta = 0.02,  verbose = NULL)

Arguments

Value

A list containing the following square matrices[[1]] observed fixed differences;[[2]] mean expected number of false positives for each comparison;[[3]] standard deviation of the no. of false positives for eachcomparison;[[4]] probability the observed fixed differences arose by chance foreach comparison.

Author(s)

Custodian: Arthur Georges (Post tohttps://groups.google.com/d/forum/dartr)

Examples

fd <- gl.fdsim(testset.gl[,1:100],poppair=c('EmsubRopeMata','EmmacBurnBara'),sympatric=TRUE,verbose=3)

Filters loci that are all NA across individuals and/or populations with all NA across loci

Description

This script deletes deletes loci or individuals with all calls missing (NA),from a genlight object

A DArT dataset will not have loci for which the calls are scored all asmissing (NA) for a particular individual, but such loci can arise rarely whenpopulations or individuals are deleted. Similarly, a DArT dataset will nothave individuals for which the calls are scored all as missing (NA) acrossall loci, but such individuals may sneak in to the dataset when loci aredeleted. Retaining individual or loci with all NAs can cause issues forseveral functions.

Also, on occasion an analysis will require that there are some loci scoredin each population. Setting by.pop=TRUE will result in removal of loci whenthey are all missing in any one population.

Note that loci that are missing for all individuals in a population arenot imputed with method 'frequency' or 'HW'. Consider using the functiongl.filter.allna with by.pop=TRUE.

Usage

gl.filter.allna(x, by.pop = FALSE, recalc = FALSE, verbose = NULL)

Arguments

Value

A genlight object having removed individuals that are scored NAacross all loci, or loci that are scored NA across all individuals.

Author(s)

Author(s): Arthur Georges. Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other filter functions:gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

# SNP data  result <- gl.filter.allna(testset.gl, verbose=3)# Tag P/A data  result <- gl.filter.allna(testset.gs, verbose=3)

Filters loci or specimens in a genlight {adegenet} object based oncall rate

Description

SNP datasets generated by DArT have missing values primarily arising fromfailure to call a SNP because of a mutation at one or both of the restrictionenzyme recognition sites. The script gl.filter.callrate() will filter out theloci with call rates below a specified threshold.

Tag Presence/Absence datasets (SilicoDArT) have missing values where it isnot possible to determine reliably if there the sequence tag can be called ata particular locus.

Usage

gl.filter.callrate(  x,  method = "loc",  threshold = 0.95,  mono.rm = FALSE,  recalc = FALSE,  recursive = FALSE,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  bins = 25,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

Because this filter operates on call rate, this function recalculates CallRate, if necessary, before filtering. If individuals are removed usingmethod='ind', then the call rate stored in the genlight object is, optionally,recalculated after filtering.

Note that when filtering individuals on call rate, the initial call rate iscalculated and compared against the threshold. After filtering, ifmono.rm=TRUE, the removal of monomorphic loci will alter the call rates.Some individuals with a call rate initially greater than the nominatedthreshold, and so retained, may come to have a call rate lower than thethreshold. If this is a problem, repeated iterations of this function willresolve the issue. This is done by setting mono.rm=TRUE and recursive=TRUE,or it can be done manually.

Callrate is summarized by locus or by individual to allow sensible decisionson thresholds for filtering taking into consideration consequential loss ofdata. The summary is in the form of a tabulation and plots.

Plot themes can be obtained from

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Resultant ggplot(s) and the tabulation(s) are saved to the session'stemporary directory.

Value

The reduced genlight or genind object, plus a summary

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.report.callrate

Other filter functions:gl.filter.allna(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

 # SNP data  result <- gl.filter.callrate(testset.gl[1:10], method='loc', threshold=0.8,   verbose=3)  result <- gl.filter.callrate(testset.gl[1:10], method='ind', threshold=0.8,   verbose=3)  result <- gl.filter.callrate(testset.gl[1:10], method='pop', threshold=0.8,   verbose=3)# Tag P/A data  result <- gl.filter.callrate(testset.gs[1:10], method='loc',   threshold=0.95, verbose=3)  result <- gl.filter.callrate(testset.gs[1:10], method='ind',   threshold=0.8, verbose=3)  result <- gl.filter.callrate(testset.gs[1:10], method='pop',   threshold=0.8, verbose=3)    res <- gl.filter.callrate(platypus.gl)

Filters loci based on pairwise Hamming distance between sequence tags

Description

Hamming distance is calculated as the number of base differences between twosequences which can be expressed as a count or a proportion. Typically, it iscalculated between two sequences of equal length. In the context of DArTtrimmed sequences, which differ in length but which are anchored to the leftby the restriction enzyme recognition sequence, it is sensible to compare thetwo trimmed sequences starting from immediately after the common recognitionsequence and terminating at the last base of the shorter sequence.

Usage

gl.filter.hamming(  x,  threshold = 0.2,  rs = 5,  taglength = 69,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  pb = FALSE,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

Hamming distance can be computedby exploiting the fact that the dot product of two binary vectors x and (1-y)counts the corresponding elements that are different between x and y.This approach can also be used for vectors that contain more than two possible values at each position (e.g. A, C, T or G).

If a pair of DNA sequences are of differing length, the longer is truncated.

The algorithm is that of Johann de Jonghttps://johanndejong.wordpress.com/2015/10/02/faster-hamming-distance-in-r-2/as implemented inutils.hamming.

Only one of two loci are retained if their Hamming distance is less that a specifiedpercentage. 5 base differences out of 100 bases is a 20

Value

A genlight object filtered on Hamming distance.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

# SNP datatest <- platypus.gltest <- gl.subsample.loci(platypus.gl,n=50)result <- gl.filter.hamming(test, threshold=0.25, verbose=3)

Filters individuals with average heterozygosity greater than aspecified upper threshold or less than a specified lower threshold

Description

Calculates the observed heterozygosity for each individual in a genlightobject and filters individuals based on specified threshold values.Use gl.report.heterozygosity to determine the appropriate thresholds.

Usage

gl.filter.heterozygosity(x, t.upper = 0.7, t.lower = 0, verbose = NULL)

Arguments

Value

The filtered genlight object.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

 result <- gl.filter.heterozygosity(testset.gl,t.upper=0.06,verbose=3) tmp <- gl.report.heterozygosity(result,method='ind')

Filters loci that show significant departure from Hardy-WeinbergEquilibrium

Description

This function filters out loci showing significant departure from H-Wproportions based on observed frequencies of reference homozygotes,heterozygotes and alternate homozygotes.

Loci are filtered out if they show HWE departure either in any one population (n.pop.threshold =1) or in at least X number of populations (n.pop.threshold > 1).

Usage

gl.filter.hwe(  x,  subset = "each",  n.pop.threshold = 1,  method_sig = "Exact",  multi_comp = FALSE,  multi_comp_method = "BY",  alpha_val = 0.05,  pvalue_type = "midp",  cc_val = 0.5,  min_sample_size = 5,  verbose = NULL)

Arguments

Details

There are several factors that can cause deviations from Hardy-Weinbergproportions including: mutation, finite population size, selection,population structure, age structure, assortative mating, sex linkage,nonrandom sampling and genotyping errors. Therefore, testing forHardy-Weinberg proportions should be a process that involves a carefulevaluation of the results, a good place to start is Waples (2015).

Note that tests for H-W proportions are only valid if there is no populationsubstructure (assuming random mating) and have sufficient power only whenthere is sufficient sample size (n individuals > 15).

Populations can be defined in three ways:

• Merging all populations in the dataset using subset = 'all'.

• Within each population separately using: subset = 'each'.

• Within selected populations using for example: subset = c('pop1','pop2').

Two different statistical methods to test for deviations from Hardy Weinbergproportions:

• The classical chi-square test (method_sig='ChiSquare') based on thefunctionHWChisq of the R package HardyWeinberg.By default a continuity correction is applied (cc_val=0.5). Thecontinuity correction can be turned off (by specifying cc_val=0), for examplein cases of extreme allele frequencies in which the continuity correction canlead to excessive type 1 error rates.

• The exact test (method_sig='Exact') based on the exact calculationscontained in the functionHWExactStats of the Rpackage HardyWeinberg, and described in Wigginton et al. (2005). The exacttest is recommended in most cases (Wigginton et al., 2005).Three different methods to estimate p-values (pvalue_type) in the Exact testcan be used:

  • 'dost' p-value is computed as twice the tail area of a one-sided test.

  • 'selome' p-value is computed as the sum of the probabilities of allsamples less or equally likely as the current sample.

  • 'midp', p-value is computed as half the probability of the currentsample + the probabilities of all samples that are more extreme.

  The standard exact p-value is overly conservative, in particularfor small minor allele frequencies. The mid p-value ameliorates this problemby bringing the rejection rate closer to the nominal level, at the price ofoccasionally exceeding the nominal level (Graffelman & Moreno, 2013).

Correction for multiple tests can be applied using the following methodsbased on the functionp.adjust:

• 'holm' is also known as the sequential Bonferroni technique (Rice, 1989).This method has a greater statistical power than the standard Bonferroni test,however this method becomes very stringent when many tests are performed andmany real deviations from the null hypothesis can go undetected (Waples, 2015).

• 'hochberg' based on Hochberg, 1988.

• 'hommel' based on Hommel, 1988. This method is more powerful thanHochberg's, but the difference is usually small.

• 'bonferroni' in which p-values are multiplied by the number of tests.This method is very stringent and therefore has reduced power to detectmultiple departures from the null hypothesis.

• 'BH' based on Benjamini & Hochberg, 1995.

• 'BY' based on Benjamini & Yekutieli, 2001.

The first four methods are designed to give strong control of the family-wiseerror rate. The last two methods control the false discovery rate (FDR),the expected proportion of false discoveries among the rejected hypotheses.The false discovery rate is a less stringent condition than the family-wiseerror rate, so these methods are more powerful than the others, especiallywhen number of tests is large.The number of tests on which the adjustment for multiple comparisons isthe number of populations times the number of loci.

From v2.1gl.filter.hwe takes the argumentn.pop.threshold.ifn.pop.threshold > 1 loci will be removed only if they are concurrently significant (after adjustment if applied) out of hwe in >=n.pop.threshold > 1.

Value

A genlight object with the loci departing significantly from H-Wproportions removed.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

References

• Benjamini, Y., and Yekutieli, D. (2001). The control of the falsediscovery rate in multiple testing under dependency. Annals of Statistics,29, 1165–1188.

• Graffelman, J. (2015). Exploring Diallelic Genetic Markers: The HardyWeinberg Package. Journal of Statistical Software 64:1-23.

• Graffelman, J. & Morales-Camarena, J. (2008). Graphical tests forHardy-Weinberg equilibrium based on the ternary plot. Human Heredity 65:77-84.

• Graffelman, J., & Moreno, V. (2013). The mid p-value in exact tests forHardy-Weinberg equilibrium. Statistical applications in genetics andmolecularbiology, 12(4), 433-448.

• Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple testsof significance. Biometrika, 75, 800–803.

• Hommel, G. (1988). A stagewise rejective multiple test procedure basedon a modified Bonferroni test. Biometrika, 75, 383–386.

• Rice, W. R. (1989). Analyzing tables of statistical tests. Evolution,43(1), 223-225.

• Waples, R. S. (2015). Testing for Hardy–Weinberg proportions: have welost the plot?. Journal of heredity, 106(1), 1-19.

• Wigginton, J.E., Cutler, D.J., & Abecasis, G.R. (2005). A Note on ExactTests of Hardy-Weinberg Equilibrium. American Journal of Human Genetics76:887-893.

See Also

gl.report.hwe

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

result <- gl.filter.hwe(x = bandicoot.gl)

Filters loci on the basis of numeric information stored inother$loc.metrics in a genlight {adegenet} object

Description

This script uses any field with numeric values stored in $other$loc.metricsto filter loci. The loci to keep can be within the upper and lower thresholds('within') or outside of the upper and lower thresholds ('outside').

Usage

gl.filter.locmetric(x, metric, upper, lower, keep = "within", verbose = NULL)

Arguments

Details

The fields that are included in dartR, and a short description, are foundbelow. Optionally, the user can also set his/her own filter by adding avector into $other$loc.metrics as shown in the example.

1. SnpPosition - position (zero is position 1) in the sequence tag of thedefined SNP variant base.

2. CallRate - proportion of samples for which the genotype call isnon-missing (that is, not '-' ).

3. OneRatioRef - proportion of samples for which the genotype score is 0.

4. OneRatioSnp - proportion of samples for which the genotype score is 2.

5. FreqHomRef - proportion of samples homozygous for the Reference allele.

6. FreqHomSnp - proportion of samples homozygous for the Alternate (SNP)allele.

7. FreqHets - proportion of samples which score as heterozygous, that is,scored as 1.

8. PICRef - polymorphism information content (PIC) for the Referenceallele.

9. PICSnp - polymorphism information content (PIC) for the SNP.

10. AvgPIC - average of the polymorphism information content (PIC) of theReference and SNP alleles.

11. AvgCountRef - sum of the tag read counts for all samples, divided bythe number of samples with non-zero tag read counts, for the Reference allelerow.

12. AvgCountSnp - sum of the tag read counts for all samples, divided bythe number of samples with non-zero tag read counts, for the Alternate (SNP)allele row.

13. RepAvg - proportion of technical replicate assay pairs for which themarker score is consistent.

Value

The reduced genlight dataset.

Author(s)

Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

# adding dummy datatest <- testset.gltest$other$loc.metrics$test <- 1:nLoc(test)result <- gl.filter.locmetric(x=test, metric= 'test', upper=255,lower=200, keep= 'within', verbose=3)

Filters loci on the basis of minor allele frequency (MAF) in a genlightadegenet object

Description

This script calculates the minor allele frequency for each locus and updatesthe locus metadata for FreqHomRef, FreqHomSnp, FreqHets and MAF (if itexists). It then uses the updated metadata for MAF to filter loci.

Usage

gl.filter.maf(  x,  threshold = 0.01,  by.pop = FALSE,  pop.limit = ceiling(nPop(x)/2),  ind.limit = 10,  recalc = FALSE,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors_pop = discrete_palette,  plot_colors_all = two_colors,  bins = 25,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

Careful consideration needs to be given to the settings to be used for this fucntion. When the filter is applied globally (i.e.by.pop=FALSE) but the data include multiple population, there is the risk to remove markers because the allele frequencies is low (at global level) but the allele frequenciesfor the same markers may be high within some of the populations (especially if the per-population sample size is small). Similarly, not always it is a sensible choice to run this function usingby.pop=TRUE because allele that are rare in a population may be very common in other, but the (possible) allele frequencies will depend on the sample size within each population. Where the purpose of filtering for MAF is to remove possible spurious alleles (i.e. sequencing errors), it is perhaps better to filter based on the number of times an allele is observed (MAC, Minimum Allele Count), under the assumption that if an allele is observed >MAC, it is fairly rare to be an error.From v2.1 The threshold can take values > 1. In this case, these are interpreted as a threshold for MAC.

Value

The reduced genlight dataset

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

result <- gl.filter.monomorphs(testset.gl)result <- gl.filter.maf(result, threshold=0.05, verbose=3)

Filters monomorphic loci, including those with all NAs

Description

This script deletes monomorphic loci from a genlight {adegenet} object

A DArT dataset will not have monomorphic loci, but they can arise, along withloci that are scored all NA, when populations or individuals are deleted.

Retaining monomorphic loci unnecessarily increases the size of the datasetand will affect some calculations.

Note that for SNP data, NAs likely represent null alleles; in tagpresence/absence data, NAs represent missing values (presence/absence couldnot be reliably scored)

Usage

gl.filter.monomorphs(x, verbose = NULL)

Arguments

Value

A genlight object with monomorphic (and all NA) loci removed.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

# SNP data  result <- gl.filter.monomorphs(testset.gl, verbose=3)# Tag P/A data  result <- gl.filter.monomorphs(testset.gs, verbose=3)

Filters loci for which the SNP has been trimmed from the sequence tagalong with the adaptor

Description

This function checks the position of the SNP within the trimmed sequence tagand identifies those for which the SNP position is outside the trimmedsequence tag. This can happen, rarely, when the sequence containing the SNPresembles the adaptor.

The SNP genotype can still be used in most analyses, but functions likegl2fasta() will present challenges if the SNP has been trimmed from thesequence tag.

Not fatal, but should apply this filter before gl.filter.secondaries, forobvious reasons.

Usage

gl.filter.overshoot(x, save2tmp = FALSE, verbose = NULL)

Arguments

Value

A new genlight object with the recalcitrant loci deleted

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

result <- gl.filter.overshoot(testset.gl, verbose=3)

Filters loci that contain private (and fixed alleles) between two populations

Description

This script is meant to be used prior togl.nhybrids to maximise theinformation content of the SNPs used to identify hybrids (currentlynewhybrids does allow only 200 SNPs). The idea is to use first all loci thathave fixed alleles between the potential source populations and then 'fillup' to 200 loci using loci that have private alleles between those. Thefunctions filters for those loci (if invers is set to TRUE, the oppositeis returned (all loci that are not fixed and have no private alleles - notsure why yet, but maybe useful.)

Usage

gl.filter.pa(x, pop1, pop2, invers = FALSE, verbose = NULL)

Arguments

Value

The reduced genlight dataset, containing now only fixed and privatealleles.

Author(s)

Authors: Bernd Gruber & Ella Kelly (University of Melbourne);Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

result <- gl.filter.pa(testset.gl, pop1=pop(testset.gl)[1], pop2=pop(testset.gl)[2],verbose=3)

Filters putative parent offspring within a population

Description

This script removes individuals suspected of being related asparent-offspring,using the output of the functiongl.report.parent.offspring, which examines the frequency ofpedigree inconsistent loci, that is, those loci that are homozygotes in theparent for the reference allele, and homozygous in the offspring for thealternate allele. This condition is not consistent with any pedigree,regardless of the (unknown) genotype of the other parent.The pedigree inconsistent loci are counted as an indication of whether or notit is reasonable to propose the two individuals are in a parent-offspringrelationship.

Usage

gl.filter.parent.offspring(  x,  min.rdepth = 12,  min.reproducibility = 1,  range = 1.5,  method = "best",  rm.monomorphs = FALSE,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

If two individuals are in a parent offspring relationship, the true number ofpedigree inconsistent loci should be zero, but SNP calling is not infallible.Some loci will be miss-called. The problem thus becomes one of determining ifthe two focal individuals have a count of pedigree inconsistent loci lessthan would be expected of typical unrelated individuals. There are some quitesophisticated software packages available to formally apply likelihoods tothe decision, but we use a simple outlier comparison.

To reduce the frequency of miss-calls, and so emphasize the differencebetween true parent-offspring pairs and unrelated pairs, the data can befiltered on read depth. Typically minimum read depth is set to 5x, but youcan examine the distribution of read depths with the functiongl.report.rdepth and push this up with an acceptable loss ofloci. 12x might be a good minimum for this particular analysis. It issensible also to push the minimum reproducibility up to 1, if that does notresult in an unacceptable loss of loci. Reproducibility is stored in the slot@other$loc.metrics$RepAvg and is defined as the proportion oftechnical replicate assay pairs for which the marker score is consistent.You can examine the distribution of reproducibility with the functiongl.report.reproducibility.

Note that the null expectation is not well defined, and the power reduced, ifthe population from which the putative parent-offspring pairs are drawncontains many sibs. Note also that if an individual has been genotyped twicein the dataset, the replicate pair will be assessed by this script as beingin a parent-offspring relationship.

You should rungl.report.parent.offspring before filtering. Usethis report to decide min.rdepth and min.reproducibility and assess impact onyour dataset.

Note that if your dataset does not contain RepAvg or rdepth among the locusmetrics, the filters for reproducibility and read depth are no used.

Function's output

Plots and table are saved to the temporal directory (tempdir) and can beaccessed with the functiongl.print.reports and listed withthe functiongl.list.reports. Note that they can be accessedonly in the current R session because tempdir is cleared each time that theR session is closed.

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

the filtered genlight object without A set of individuals inparent-offspring relationship. NULL if no parent-offspring relationships werefound.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.list.reports,gl.report.rdepth ,gl.print.reports,gl.report.reproducibility,gl.report.parent.offspring

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

out <- gl.filter.parent.offspring(testset.gl[1:10,1:50])

Filters loci based on counts of sequence tags scored at a locus (readdepth)

Description

SNP datasets generated by DArT report AvgCountRef and AvgCountSnp as countsof sequence tags for the reference and alternate alleles respectively. Thesecan be used to back calculate Read Depth. Fragment presence/absence datasetsas provided by DArT (SilicoDArT) provide Average Read Depth and StandardDeviation of Read Depth as standard columns in their report.

Filtering on Read Depth using the companion script gl.filter.rdepth can be onthe basis of loci with exceptionally low counts,or loci with exceptionally high counts.

Usage

gl.filter.rdepth(  x,  lower = 5,  upper = 50,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

For examples of themes, see:

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

Returns a genlight object retaining loci with a Read Depth in therange specified by the lower and upper threshold.

Author(s)

Custodian: Arthur Georges (Post tohttps://groups.google.com/d/forum/dartr)

See Also

gl.filter.rdepth

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

 # SNP data  gl.report.rdepth(testset.gl)  result <- gl.filter.rdepth(testset.gl, lower=8, upper=50, verbose=3)# Tag P/A data  result <- gl.filter.rdepth(testset.gs, lower=8, upper=50, verbose=3)    res <- gl.filter.rdepth(platypus.gl)

Filters loci in a genlight {adegenet} object based on averagerepeatability of alleles at a locus

Description

SNP datasets generated by DArT have an index, RepAvg, generated byreproducing the data independently for 30of alleles that give a repeatable result, averaged over both alleles for eachlocus.

SilicoDArT datasets generated by DArT have a similar index, Reproducibility.For these fragment presence/absence data, repeatability is the percentage ofscores that are repeated in the technical replicate dataset.

Usage

gl.filter.reproducibility(  x,  threshold = 0.99,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  save2tmp = FALSE,  verbose = NULL)

Arguments

Value

Returns a genlight object retaining loci with repeatability (Repavgor Reproducibility) greater than the specified threshold.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.report.reproducibility

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.secondaries(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

 # SNP data  gl.report.reproducibility(testset.gl)  result <- gl.filter.reproducibility(testset.gl, threshold=0.99, verbose=3)# Tag P/A data  gl.report.reproducibility(testset.gs)  result <- gl.filter.reproducibility(testset.gs, threshold=0.99)      test <- gl.subsample.loci(platypus.gl,n=100)  res <- gl.filter.reproducibility(test)

Filters loci that represent secondary SNPs in a genlight object

Description

SNP datasets generated by DArT include fragments with more than one SNP andrecord them separately with the same CloneID (=AlleleID). These multiple SNPloci within a fragment (secondaries) are likely to be linked, and so you maywish to remove secondaries.

This script filters out all but the first sequence tag with the same CloneIDafter ordering the genlight object on based on repeatability, avgPIC in thatorder (method='best') or at random (method='random').

The filter has not been implemented for tag presence/absence data.

Usage

gl.filter.secondaries(x, method = "random", verbose = NULL)

Arguments

Value

The genlight object, with the secondary SNP loci removed.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.sexlinked(),gl.filter.taglength()

Examples

gl.report.secondaries(testset.gl)result <- gl.filter.secondaries(testset.gl)

Filters loci that are sex linked

Description

Alleles unique to the Y or W chromosome and monomorphic on the X chromosomeswill appear in the SNP dataset as genotypes that are heterozygotic in allindividuals of the heterogametic sex and homozygous in all individuals of thehomogametic sex. This function keeps or drops loci with alleles that behavein this way, as putative sex specific SNP markers.

Usage

gl.filter.sexlinked(  x,  sex = NULL,  filter = NULL,  read.depth = 0,  t.het = 0.1,  t.hom = 0.1,  t.pres = 0.1,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = three_colors,  verbose = NULL)

Arguments

Details

Sex of the individuals for which sex is known with certainty can be providedvia a factor (equal to the length of the number of individuals) or to be heldin the variablex@other$ind.metrics$sex.Coding is: M for male, F for female, U or NA for unknown/missing.The script abbreviates the entries here to the first character. So, coding of'Female' and 'Male' works as well. Character are also converted to upper cases.

' Function's output

This function creates also a plot that shows the heterozygosity of males andfemales at each loci for SNP data or percentage of present/absent in the case of SilicoDArT data.

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

The filtered genlight object (filter = 'keep': sex linked loci,filter='drop', everything except sex linked loci).

Author(s)

Arthur Georges, Bernd Gruber & Floriaan Devloo-Delva (Post tohttps://groups.google.com/d/forum/dartr)

See Also

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.taglength()

Examples

  out <- gl.filter.sexlinked(testset.gl, filter='drop')out <- gl.filter.sexlinked(testset.gs, filter='drop')

Filters loci in a genlight {adegenet} object based on sequence taglength

Description

SNP datasets generated by DArT typically have sequence tag lengths rangingfrom 20 to 69 base pairs.

Usage

gl.filter.taglength(x, lower = 20, upper = 69, verbose = NULL)

Arguments

Value

Returns a genlight object retaining loci with a sequence tag lengthin the range specified by the lower and upper threshold.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other filter functions:gl.filter.allna(),gl.filter.callrate(),gl.filter.heterozygosity(),gl.filter.hwe(),gl.filter.locmetric(),gl.filter.maf(),gl.filter.monomorphs(),gl.filter.overshoot(),gl.filter.pa(),gl.filter.parent.offspring(),gl.filter.rdepth(),gl.filter.reproducibility(),gl.filter.secondaries(),gl.filter.sexlinked()

Examples

 # SNP data  gl.report.taglength(testset.gl)  result <- gl.filter.taglength(testset.gl,lower=60)  gl.report.taglength(result)# Tag P/A data  gl.report.taglength(testset.gs)  result <- gl.filter.taglength(testset.gs,lower=60)  gl.report.taglength(result)    test <- gl.subsample.loci(platypus.gl, n =100)  res <- gl.report.taglength(test)

Generates a matrix of fixed differences and associated statistics forpopulations taken pairwise

Description

This script takes SNP data or sequence tag P/A data grouped into populationsin a genlight object (DArTSeq) and generates a matrix of fixed differencesbetween populations taken pairwise

Usage

gl.fixed.diff(  x,  tloc = 0,  test = FALSE,  delta = 0.02,  alpha = 0.05,  reps = 1000,  mono.rm = TRUE,  pb = FALSE,  verbose = NULL)

Arguments

Details

A fixed difference at a locus occurs when two populations share no alleles orwhere all members of one population has a sequence tag scored, and allmembers of the other population has the sequence tag absent. The challengewith this approach is that when sample sizes are finite, fixed differenceswill occur through sampling error, compounded when many loci are examined.Simulations suggest that sample sizes of n1=5 and n2=5 are adequate to reducethe probability of [experiment-wide] type 1 error to negligible levels[ploidy=2]. A warning is issued if comparison between two populationsinvolves sample sizes less than 5, taking into account allele drop-out.

Optionally, if test=TRUE, the script will test the fixed differences betweenfinal OTUs for statistical significance, using simulation, and then furtheramalgamate populations that for which there are no significant fixeddifferences at a specified level of significance (alpha). To avoid conflationof true fixed differences with false positives in the simulations, it isnecessary to decide a threshold value (delta) for extreme true allelefrequencies that will be considered fixed for practical purposes. That is,fixed differences in the sample set will be considered to be positives (notfalse positives) if they arise from true allele frequencies of less than1-delta in one or both populations. The parameter delta is typically set tobe small (e.g. delta = 0.02).

NOTE: The above test will only be calculated if tloc=0, that is, for analysesof absolute fixed differences. The test applies in comparisons of allopatricpopulations only. For sympatric populations, use gl.pval.sympatry().

An absolute fixed difference is as defined above. However, one might wish toscore fixed differences at some lower level of allele frequency difference,say where percent allele frequencies are 95,5 and 5,95 rather than 100:0 and0:100. This adjustment can be done with the tloc parameter. For example,tloc=0.05 means that SNP allele frequencies of 95,5 and 5,95 percent will beregarded as fixed when comparing two populations at a locus.

Value

A list of Class 'fd' containing the gl object and square matrices,as follows:

1. $gl – the output genlight object;

2. $fd – raw fixed differences;

3. $pcfd – percent fixed differences;

4. $nobs – mean no. of individuals used in each comparison;

5. $nloc – total number of loci used in each comparison;

6. $expfpos – if test=TRUE, the expected count of false positivesfor each comparison [by simulation];

7. $sdfpos – if test=TRUE, the standard deviation of the count offalse positives for each comparison [by simulation];

8. $pval – if test=TRUE, the significance of the count of fixeddifferences [by simulation])

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

is.fixed

Examples

fd <- gl.fixed.diff(testset.gl, tloc=0, verbose=3 )fd <- gl.fixed.diff(testset.gl, tloc=0, test=TRUE, delta=0.02, reps=100, verbose=3 )

Calculates a pairwise Fst values for populations in a genlight object

Description

This script calculates pairwise Fst values based on the implementation in theStAMPP package (?stamppFst). It allows to run bootstrap to estimateprobability of Fst values to be different from zero. For detailed informationplease check the help pages (?stamppFst).

Usage

gl.fst.pop(x, nboots = 1, percent = 95, nclusters = 1, verbose = NULL)

Arguments

Value

A matrix of distances between populations (class dist), if nboots =1,otherwise a list with Fsts (in a matrix), Pvalues (a matrix of pvalues),Bootstraps results (data frame of all runs). Hint: Useas.matrix(as.dist(fsts)) if you want to have a squared matrix withsymmetric entries returned, instead of a dist object.

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr)

Examples

test <- gl.filter.callrate(platypus.gl,threshold = 1)test <- gl.filter.monomorphs(test)out <- gl.fst.pop(test, nboots=1)

Performs least-cost path analysis based on a friction matrix

Description

This function calculates the pairwise distances (Euclidean, cost pathdistances and genetic distances) of populations using a friction matrix anda spatial genind object. The genind object needs to have coordinates in thesame projected coordinate system as the friction matrix. The frictionmatrix can be either a single raster of a stack of several layers. If astack is provided the specified cost distance is calculated for each layerin the stack. The output of this function can be used with the functionswassermann orlgrMMRR to test for the significance of alayer on the genetic structure.

Usage

gl.genleastcost(  x,  fric.raster,  gen.distance,  NN = NULL,  pathtype = "leastcost",  plotpath = TRUE,  theta = 1,  verbose = NULL)

Arguments

Value

Returns a list that consists of four pairwise distance matrices(Euclidean, Cost, length of path and genetic) and the actual paths as spatialline objects.

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr)

References

• Cushman, S., Wasserman, T., Landguth, E. and Shirk, A. (2013).Re-Evaluating Causal Modeling with Mantel Tests in Landscape Genetics.Diversity, 5(1), 51-72.

• Landguth, E. L., Cushman, S. A., Schwartz, M. K., McKelvey, K. S.,Murphy, M. and Luikart, G. (2010). Quantifying the lag time to detectbarriers in landscape genetics. Molecular ecology, 4179-4191.

• Wasserman, T. N., Cushman, S. A., Schwartz, M. K. and Wallin, D. O.(2010). Spatial scaling and multi-model inference in landscape genetics:Martes americana in northern Idaho. Landscape Ecology, 25(10), 1601-1612.

See Also

landgenreport,popgenreport,wassermann,lgrMMRR

Examples

## Not run: data(possums.gl)library(raster)  #needed for that examplelandscape.sim <- readRDS(system.file('extdata','landscape.sim.rdata', package='dartR'))glc <- gl.genleastcost(x=possums.gl,fric.raster=landscape.sim ,gen.distance = 'D', NN=8, pathtype = 'leastcost',plotpath = TRUE)library(PopGenReport)PopGenReport::wassermann(eucl.mat = glc$eucl.mat, cost.mat = glc$cost.mats,  gen.mat = glc$gen.mat)lgrMMRR(gen.mat = glc$gen.mat, cost.mats = glc$cost.mats,  eucl.mat = glc$eucl.mat)## End(Not run)

Calculates an identity by descent matrix

Description

This function calculates the mean probability of identity by state (IBS)across loci that would result from all the possible crosses of theindividuals analyzed. IBD is calculated by an additive relationship matrixapproach developed by Endelman and Jannink (2012) as implemented in thefunctionA.mat (package rrBLUP).

Usage

gl.grm(  x,  plotheatmap = TRUE,  palette_discrete = discrete_palette,  palette_convergent = convergent_palette,  legendx = 0,  legendy = 0.5,  verbose = NULL,  ...)

Arguments

Details

Two or more alleles are identical by descent (IBD) if they are identicalcopies of the same ancestral allele in a base population. The additiverelationship matrix is a theoretical framework for estimating a relationshipmatrix that is consistent with an approach to estimate the probability thatthe alleles at a random locus are identical in state (IBS).

This function also plots a heatmap, and a dendrogram, of IBD values whereeach diagonal element has a mean that equals 1+f, where f is the inbreedingcoefficient (i.e. the probability that the two alleles at a randomly chosenlocus are IBD from the base population). As this probability lies between 0and 1, the diagonal elements range from 1 to 2. Because the inbreedingcoefficients are expressed relative to the current population, the mean ofthe off-diagonal elements is -(1+f)/n, where n is the number of loci.Individual names are shown in the margins of the heatmap and colorsrepresent different populations.

Value

An identity by descent matrix

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

References

• Endelman, J. B. (2011). Ridge regression and other kernels for genomicselection with r package rrblup. The Plant Genome 4, 250.

• Endelman, J. B. , Jannink, J.-L. (2012). Shrinkage estimation of therealized relationship matrix. G3: Genes, Genomics, Genetics 2, 1405.

See Also

gl.grm.network

Other inbreeding functions:gl.grm.network()

Examples

gl.grm(platypus.gl[1:10,1:100])

Represents a genomic relationship matrix (GRM) as a network

Description

This script takes a G matrix generated bygl.grm and representsthe relationship among the specimens as a network diagram. In order to usethis script, a decision is required on a threshold for relatedness to berepresented as link in the network, and on the layout used to create thediagram.

Usage

gl.grm.network(  G,  x,  method = "fr",  node.size = 8,  node.label = TRUE,  node.label.size = 2,  node.label.color = "black",  link.color = NULL,  link.size = 2,  relatedness_factor = 0.125,  title = "Network based on a genomic relationship matrix",  palette_discrete = NULL,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The gl.grm.network function takes a genomic relationship matrix (GRM) generated by the gl.grm function to represent the relationship among individuals in the dataset as a network diagram. To generate the GRM, the function gl.grm uses the function A.mat from package rrBLUP, which implementsthe approach developed by Endelman and Jannink (2012).

The GRM is an estimate of the proportion of alleles that two individuals havein common. It is generated by estimating the covariance of the genotypes between two individuals, i.e. how much genotypes in the two individualscorrespond with each other. This covariance depends on the probability thatalleles at a random locus are identical by state (IBS). Two alleles are IBS if they represent the same allele. Two alleles are identical by descent (IBD) if one is a physical copy of the other or if they are both physical copies of the same ancestral allele. Note that IBD is complicatedto determine. IBD implies IBS, but not conversely. However, as the numberof SNPs in a dataset increases, the mean probability of IBS approaches the mean probability of IBD.

It follows that the off-diagonal elements of the GRM are two times the kinship coefficient, i.e. the probability that two alleles at a random locusdrawn from two individuals are IBD. Additionally, the diagonal elements ofthe GRM are 1+f, where f is the inbreeding coefficient of each individual,i.e. the probability that the two alleles at a random locus are IBD.

Choosing a meaningful threshold to represent the relationship between individuals is tricky because IBD is not an absolute state but is relative toa reference population for which there is generally little information so that we can estimate the kinship of a pair of individuals only relative to some other quantity. To deal with this, we can use the average inbreeding coefficient of the diagonal elements as the reference value. For this, the function subtracts 1 from the mean of the diagonal elements of the GRM. In asecond step, the off-diagonal elements are divided by 2, and finally, the mean of the diagonal elements is subtracted from each off-diagonal element after dividing them by 2. This approach is similar to the one used by Goudet et al. (2018).

Below is a table modified from Speed & Balding (2015) showing kinship values,and their confidence intervals (CI), for different relationships that could be used to guide the choosing of the relatedness threshold in the function.

|Relationship |Kinship | 95

|Identical twins/clones/same individual | 0.5 | - |

|Sibling/Parent-Offspring | 0.25 | (0.204, 0.296)|

|Half-sibling | 0.125 | (0.092, 0.158)|

|First cousin | 0.062 | (0.038, 0.089)|

|Half-cousin | 0.031 | (0.012, 0.055)|

|Second cousin | 0.016 | (0.004, 0.031)|

|Half-second cousin | 0.008 | (0.001, 0.020)|

|Third cousin | 0.004 | (0.000, 0.012)|

|Unrelated | 0 | - |

Four layout options are implemented in this function:

• 'fr' Fruchterman-Reingold layoutlayout_with_fr(package igraph)

• 'kk' Kamada-Kawai layoutlayout_with_kk (package igraph)

• 'gh' Graphopt layoutlayout_with_graphopt(package igraph)

• 'mds' Multidimensional scaling layoutlayout_with_mds(package igraph)

Value

A network plot showing relatedness between individuals

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

References

• Endelman, J. B. , Jannink, J.-L. (2012). Shrinkage estimation of the realized relationship matrix. G3: Genes, Genomics, Genetics 2, 1405.

• Goudet, J., Kay, T., & Weir, B. S. (2018). How to estimate kinship.Molecular Ecology, 27(20), 4121-4135.

• Speed, D., & Balding, D. J. (2015). Relatedness in the post-genomic era: is it still useful?. Nature Reviews Genetics, 16(1), 33-44.

See Also

gl.grm

Other inbreeding functions:gl.grm()

Examples

if (requireNamespace("igraph", quietly = TRUE) & requireNamespace("rrBLUP",quietly = TRUE) & requireNamespace("fields", quietly=TRUE)) {t1 <- possums.gl# filtering on call rate t1 <- gl.filter.callrate(t1)t1 <- gl.subsample.loci(t1,n = 100)# relatedness matrixres <- gl.grm(t1,plotheatmap = FALSE)# relatedness networkres2 <- gl.grm.network(res,t1,relatedness_factor = 0.125)}

Performs Hardy-Weinberg tests over loci and populations

Description

Hardy-Weinberg tests are performed for each loci in each of the populationsas defined by the pop slot in a genlight object.

Usage

gl.hwe.pop(  x,  alpha_val = 0.05,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = c("gray90", "deeppink"),  HWformat = FALSE,  verbose = NULL)

Arguments

Details

This function employs theHardyWeinberg package, which needs to beinstalled. The function that is used isHWExactStats, but there are several other greatfunctions implemented in the package regarding HWE. Therefore, this functioncan return the data in the format expected by the HWE package expects, viaHWformat=TRUE and then use this to run other functions of the package.

This functions performs a HWE test for every population (rows) and loci(columns) and returns a true false matrix. True is reported if the p-value ofan HWE-test for a particular loci and population was below the specifiedthreshold (alpha_val, default=0.05). The thinking behind this approach isthat loci that are not in HWE in several populations have most likely to betreated (e.g. filtered if loci under selection are of interest). If plot=TRUEa barplot on the loci and the sum of deviation over all population isreturned. Loci that deviate in the majority of populations can be identifiedvia colSums on the resulting matrix.

Plot themes can be obtained from

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Resultant ggplots and the tabulation are saved to the session's temporarydirectory.

Value

The function returns a list with up to three components:

• 'HWE' is the matrix over loci and populations

• 'plot' is a plot (ggplot) which shows the significant resultsfor population and loci (can be amended further using ggplot syntax)

• 'HWEformat=TRUE' the 'HWformat' entails SNP data for each populationin 'HardyWeinberg'-format to be used with other functions of the package(e.gHWPerm orHWExactPrevious).

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

out <- gl.hwe.pop(bandicoot.gl[,1:33], alpha_val=0.05, plot.out=TRUE, HWformat=FALSE)

Performs isolation by distance analysis

Description

This function performs an isolation by distance analysis based on a Manteltest and also produces an isolation by distance plot. If a genlight objectwith coordinates is provided, then an Euclidean and genetic distance matricesare calculated.'

Usage

gl.ibd(  x = NULL,  distance = "Fst",  coordinates = "latlon",  Dgen = NULL,  Dgeo = NULL,  Dgeo_trans = "Dgeo",  Dgen_trans = "Dgen",  permutations = 999,  plot.out = TRUE,  paircols = NULL,  plot_theme = theme_dartR(),  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

Currently pairwise Fst and D between populations and1-propShared and Euclidean distance between individuals areimplemented. Coordinates are expected as lat long and converted to GoogleEarth Mercator projection. If coordinates are already projected, provide themat the x@other$xy slot.

You can provide also your own genetic and Euclidean distance matrices. Thefunction is based on the code provided by the adegenet tutorial(http://adegenet.r-forge.r-project.org/files/tutorial-basics.pdf),using the functionsmantel (package vegan),stamppFst,stamppNeisD (package StAMPP) andgl.propShared or gl.dist.ind. For transformation you need to have the dismopackage installed. As a new feature you can plot pairwise relationship usingdouble colored points (paircols=TRUE). Pairwise relationship can bevisualised via populations or individuals, depending which distance iscalculated. Please note: Often a problem arises, if an individual based distance is calculated (e.g. propShared) and some individuals have identicalcoordinates as this results in distances of zero between those pairs of individuals.

If the standard transformation [log(Dgeo)] is used, this results in an infinite value, because of trying to calculate'log(0)'. To avoid this, the easiest fix is to change the transformation from log(Dgeo) to log(Dgeo+1) or you could add some "noise" to the coordinates of the individuals (e.g. +- 1m,but be aware if you use lat lon then you rather want to add +0.00001 degreesor so).

Value

Returns a list of the following components: Dgen (the geneticdistance matrix), Dgeo (the Euclidean distance matrix), Mantel (thestatistics of the Mantel test).

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr)

References

Rousset, F. (1997). Genetic differentiation and estimation of gene flow fromF-statistics under isolation by distance. Genetics, 145(4), 1219-1228.

See Also

mantel,stamppFst

Examples

 #because of speed only the first 100 lociibd <- gl.ibd(bandicoot.gl[,1:100], Dgeo_trans='log(Dgeo)' ,Dgen_trans='Dgen/(1-Dgen)')#because of speed only the first 10 individuals)ibd <- gl.ibd(bandicoot.gl[1:10,], distance='euclidean', paircols='pop', Dgeo_trans='Dgeo')#only first 100 lociibd <- gl.ibd(bandicoot.gl[,1:100])

Imputates missing data

Description

This function imputes genotypes on a population-by-population basis, wherepopulations can be considered panmictic, or imputes the state forpresence-absence data.

Usage

gl.impute(  x,  method = "neighbour",  fill.residual = TRUE,  parallel = FALSE,  verbose = NULL)

Arguments

Details

We recommend that imputation be performed on sampling locations, beforeany aggregation. Imputation is achieved by replacing missing values usingeither of two methods:

• If "frequency", genotypes scored as missing at a locus in an individualare imputed using the average allele frequencies at that locus in the population from which the individual was drawn.

• If "HW", genotypes scored as missing at a locus in an individual are imputed by sampling at random assuming Hardy-Weinberg equilibrium. Applies only to genotype data.

• If "neighbour", substitute the missing values for the focal individualwith the values taken from the nearest neighbour. Repeat with next nearestand so on until all missing values are replaced.

• if "random", missing data are substituted by random values (0, 1 or 2).

The nearest neighbour is the one with the smallest Euclidean distance in all the dataset.

The advantage of this approach is that it works regardless of how manyindividuals are in the population to which the focal individual belongs,and the displacement of the individual is haphazard as opposed to:

(a) Drawing the individual toward the population centroid (HW and Frequency).

(b) Drawing the individual toward the global centroid (glPCA).

Note that loci that are missing for all individuals in a population are not imputed with method 'frequency' or 'HW'. Consider using the functiongl.filter.allna with by.pop=TRUE to remove them first.

Value

A genlight object with the missing data imputed.

Author(s)

Custodian: Luis Mijangos (Post tohttps://groups.google.com/d/forum/dartr)

Examples

 require("dartR.data")# SNP genotype datagl <- gl.filter.callrate(platypus.gl,threshold=0.95)gl <- gl.filter.allna(gl)gl <- gl.impute(gl,method="neighbour")# Sequence Tag presence-absence datags <- gl.filter.callrate(testset.gs,threshold=0.95)gl <- gl.filter.allna(gl)gs <- gl.impute(gs, method="neighbour")gs <- gl.impute(platypus.gl,method ="random")

Installs all required packages for using all functionsavailable in dartR

Description

The function compares the installed packages with the the currently availableones on CRAN. Be aware this function only works if a version of dartR isalready installed on your system. You can choose if you also want to have aspecific version of dartR installed ('CRAN', 'master', 'beta' or 'dev' ). 'master', 'beta' and 'dev' are installed from Github. Be aware that the dev version from github isnot fully tested and most certainly will contain untested functions.

Usage

gl.install.vanilla.dartR(flavour = NULL, verbose = NULL)

Arguments

Value

Returns a message if the installation was successful/required.

Author(s)

Custodian: Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

Combines two genlight objects

Description

This function combines two genlight objects and their associated metadata.The history associated with the two genlight objects is cleared from the newgenlight object. The individuals/samples must be the same in each genlightobject.

The function is typically used to combine datasets from the same servicewhere the files have been split because of size limitations. The data is readin from multiple csv files, then the resultant genlight objects are combined.

Usage

gl.join(x1, x2, verbose = NULL)

Arguments

Value

A new genlight object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

x1 <- testset.gl[,1:100]x1@other$loc.metrics <-  testset.gl@other$loc.metrics[1:100,]nLoc(x1)x2 <- testset.gl[,101:150]x2@other$loc.metrics <-  testset.gl@other$loc.metrics[101:150,]nLoc(x2)gl <- gl.join(x1, x2, verbose=2)nLoc(gl)

Removes all but the specified individuals from a dartR genlight object

Description

This script deletes all individuals apart from those listed (ind.list).Monomorphic loci and loci that are scored all NA are optionally deleted (mono.rm=TRUE). The script also optionally recalculates locus metatdata statistics to accommodatethe deletion of individuals from the dataset (recalc=TRUE).

The script returns a dartR genlight object with the retained individuals and the recalculated locus metadata. The script works with both genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

Usage

gl.keep.ind(x, ind.list, recalc = FALSE, mono.rm = FALSE, verbose = NULL)

Arguments

Value

A reduced dartR genlight object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.drop.pop to drop rather than keep specified populations

Examples

  # SNP data    gl2 <- gl.keep.ind(testset.gl, ind.list=c('AA019073','AA004859'))  # Tag P/A data   gs2 <- gl.keep.ind(testset.gs, ind.list=c('AA020656','AA19077','AA004859'))

Removes all but the specified loci from a genlight object

Description

This function deletes loci that are not specified to keep, and their associated metadata.

The script returns a dartR genlight object with the retained loci. The script works with both genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

Usage

gl.keep.loc(x, loc.list = NULL, first = NULL, last = NULL, verbose = NULL)

Arguments

Value

A genlight object with the reduced data

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.drop.loc to drop rather than keep specified loci

Other dartR-base:gl.drop.ind(),gl.drop.loc(),gl.drop.pop(),gl.edit.recode.ind(),gl.edit.recode.pop(),gl.make.recode.ind(),gl.read.dart(),gl.recode.ind(),gl.recode.pop(),gl.set.verbosity()

Examples

# SNP data  gl2 <- gl.keep.loc(testset.gl, loc.list=c('100051468|42-A/T', '100049816-51-A/G'))# Tag P/A data  gs2 <- gl.keep.loc(testset.gs, loc.list=c('20134188','19249144'))

Removes all but the specified populations from a dartR genlight object

Description

Individuals are assigned to populations based on associated specimen metadatastored in the dartR genlight object.

This script deletes all individuals apart from those in listed populations (pop.list).Monomorphic loci and loci that are scored all NA are optionally deleted (mono.rm=TRUE). The script also optionally recalculates locus metatdata statistics to accommodatethe deletion of individuals from the dataset (recalc=TRUE).

The script returns a dartR genlight object with the retained populations and the recalculated locus metadata. The script works with both genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

Usage

gl.keep.pop(  x,  pop.list,  as.pop = NULL,  recalc = FALSE,  mono.rm = FALSE,  verbose = NULL)

Arguments

Value

A reduced dartR genlight object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.drop.pop to drop rather than keep specified populations

Examples

 # SNP data   gl2 <- gl.keep.pop(testset.gl, pop.list=c('EmsubRopeMata', 'EmvicVictJasp'))   gl2 <- gl.keep.pop(testset.gl, pop.list=c('EmsubRopeMata', 'EmvicVictJasp'),   mono.rm=TRUE,recalc=TRUE)   gl2 <- gl.keep.pop(testset.gl, pop.list=c('Female'),as.pop='sex') # Tag P/A data   gs2 <- gl.keep.pop(testset.gs, pop.list=c('EmsubRopeMata','EmvicVictJasp'))

Plots linkage disequilibrium against distance by population disequilibrium patterns

Description

The function creates a plot showingthe pairwise LD measure against distance in number of base pairs pooled overall the chromosomes and a red line representing the threshold (R.squared = 0.2) that is commonly used to imply that two loci are unlinked (Delourme etal., 2013; Li et al., 2014).

Usage

gl.ld.distance(  ld_report,  ld_resolution = 1e+05,  pop_colors = NULL,  plot_theme = NULL,  plot.out = TRUE,  save2tmp = FALSE,  plot_title = " ",  verbose = NULL)

Arguments

Value

A dataframe with information of LD against distance by population.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

References

• Delourme, R., Falentin, C., Fomeju, B. F., Boillot, M., Lassalle, G., André, I., . . . Marty, A. (2013). High-density SNP-based genetic map development and linkage disequilibrium assessment in Brassica napusL. BMC genomics, 14(1), 120.

• Li, X., Han, Y., Wei, Y., Acharya, A., Farmer, A. D., Ho, J., . . . Brummer, E. C. (2014). Development of an alfalfa SNP array and its use to evaluate patterns of population structure and linkage disequilibrium. PLoS One, 9(1), e84329.

See Also

Other ld functions:gl.ld.haplotype()

Examples

if ((requireNamespace("snpStats", quietly = TRUE)) & (requireNamespace("fields", quietly = TRUE))) {require("dartR.data")x <- platypus.glx <- gl.filter.callrate(x,threshold = 1)x <- gl.filter.monomorphs(x)x$position <- x$other$loc.metrics$ChromPos_Platypus_Chrom_NCBIv1x$chromosome <- as.factor(x$other$loc.metrics$Chrom_Platypus_Chrom_NCBIv1)ld_res <- gl.report.ld.map(x,ld_max_pairwise = 10000000)ld_res_2 <- gl.ld.distance(ld_res,ld_resolution= 1000000)}

Visualize patterns of linkage disequilibrium and identification of haplotypes

Description

This function plots a Linkage disequilibrium (LD) heatmap, where the colour shading indicates the strength of LD. Chromosome positions (Mbp) are shown onthe horizontal axis, and haplotypes appear as triangles and delimited by dark yellow vertical lines. Numbers identifying each haplotype are shown in the upper part of the plot.

The heatmap also shows heterozygosity for each SNP.

The function identifies haplotypes based on contiguous SNPs that are in linkage disequilibrium using as thresholdld_threshold_haplo andcontaining more thanmin_snps SNPs.

Usage

gl.ld.haplotype(  x,  pop_name = NULL,  chrom_name = NULL,  ld_max_pairwise = 1e+07,  maf = 0.05,  ld_stat = "R.squared",  ind.limit = 10,  min_snps = 10,  ld_threshold_haplo = 0.5,  coordinates = NULL,  color_haplo = "viridis",  color_het = "deeppink",  plot.out = TRUE,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The information for SNP's position should be stored in the genlight accessor"@position" and the SNP's chromosome name in the accessor "@chromosome"(see examples). The function will then calculate LD within each chromosome.

The output of the function includes a table with the haplotypesthat were identified and their location.

Colors of the heatmap (color_haplo) are based on the functionscale_fill_viridis from packageviridis. Other color palettes options are "magma", "inferno", "plasma", "viridis","cividis", "rocket", "mako" and "turbo".

Value

A table with the haplotypes that were identified.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other ld functions:gl.ld.distance()

Examples

require("dartR.data")x <- platypus.glx <- gl.filter.callrate(x,threshold = 1)x <- gl.keep.pop(x, pop.list = "TENTERFIELD")x$chromosome <- as.factor(x$other$loc.metrics$Chrom_Platypus_Chrom_NCBIv1)x$position <- x$other$loc.metrics$ChromPos_Platypus_Chrom_NCBIv1ld_res <- gl.ld.haplotype(x,chrom_name = "NC_041728.1_chromosome_1",                          ld_max_pairwise = 10000000 )

Prints dartR reports saved in tempdir

Description

Prints dartR reports saved in tempdir

Usage

gl.list.reports()

Value

Prints a table with all reports saved in tempdir. Currently the stylecannot be changed.

Author(s)

Bernd Gruber & Luis Mijangos (bugs? Post tohttps://groups.google.com/d/forum/dartr)

See Also

gl.print.reports

Examples

## Not run: gl.report.callrate(testset.gl,save2tmp=TRUE)gl.list.reports()## End(Not run)

Loads an object from compressed binary format produced by gl.save()

Description

This is a wrapper for readRDS()

Usage

gl.load(file, verbose = NULL)

Arguments

Details

The script loads the object from the current workspace and returns thegl object.

Value

The loaded object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.save

Examples

gl.save(testset.gl,file.path(tempdir(),'testset.rds'))gl <- gl.load(file.path(tempdir(),'testset.rds'))

Creates a proforma recode_ind file for reassigning individual(=specimen) names

Description

Renaming individuals may be required when there have been errors in labelingarising in the process from sample to sequencing files. There may be occasionswhere renaming individuals is required for preparation of figures.

Usage

gl.make.recode.ind(  x,  out.recode.file = "default_recode_ind.csv",  outpath = tempdir(),  verbose = NULL)

Arguments

Details

This function facilitates the construction of a recode table by producing aproforma file with current individual (=specimen) names in two identicalcolumns. Edit the second column to reassign individual names. Use keyword'Delete' to delete an individual.

When caution needs to be exercised because of the potential for breaking the'chain of evidence' associated with the samples, recoding individuals usinga recode table (csv) can provide a clear record of the changes.

Use outpath=getwd() or when calling this function to direct output files to your working directory.

The function works with both genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

Apply the recoding using gl.recode.ind().

Value

A vector containing the new individual names.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other dartR-base:gl.drop.ind(),gl.drop.loc(),gl.drop.pop(),gl.edit.recode.ind(),gl.edit.recode.pop(),gl.keep.loc(),gl.read.dart(),gl.recode.ind(),gl.recode.pop(),gl.set.verbosity()

Examples

result <- gl.make.recode.ind(testset.gl, out.recode.file ='Emmac_recode_ind.csv',outpath=tempdir())

Creates a proforma recode_pop_table file for reassigning populationnames

Description

Renaming populations may be required when there have been errors inassignment arising in the process from sample to sequence files or when onewishes to amalgamate populations, or delete populations. Recoding populationscan also be done with a recode table (csv).

Usage

gl.make.recode.pop(  x,  out.recode.file = "recode_pop_table.csv",  outpath = tempdir(),  verbose = NULL)

Arguments

Details

This function facilitates the construction of a recode table by producing aproforma file with current population names in two identical columns. Editthe second column to reassign populations. Use keyword 'Delete' to delete apopulation.

When caution needs to be exercised because of the potential for breaking the'chain of evidence' associated with the samples, recoding individuals usinga recode table (csv) can provide a clear record of the changes.

Use outpath=getwd() or when calling this function to direct output files to your working directory.

The function works with both genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

Apply the recoding using gl.recode.pop().

Value

A vector containing the new population names.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

result <- gl.make.recode.pop(testset.gl,out.recode.file='test.csv',outpath=tempdir(),verbose=2)

Creates an interactive map (based on latlon) from a genlight object

Description

Creates an interactive map (based on latlon) from a genlight object

Usage

gl.map.interactive(  x,  matrix = NULL,  standard = TRUE,  symmetric = TRUE,  pop.labels = TRUE,  pop.labels.cex = 12,  ind.circles = TRUE,  ind.circle.cols = NULL,  ind.circle.cex = 10,  ind.circle.transparency = 0.8,  palette_links = NULL,  leg_title = NULL,  provider = "Esri.NatGeoWorldMap",  verbose = NULL)

Arguments

Details

A wrapper around theleaflet package. For possible background maps check as specified via the provider:http://leaflet-extras.github.io/leaflet-providers/preview/index.html

The palette_links argument can be any of the following:A character vector of RGB or named colors. Examples: palette(), c("#000000", "#0000FF", "#FFFFFF"), topo.colors(10)

The name of an RColorBrewer palette, e.g. "BuPu" or "Greens".

The full name of a viridis palette: "viridis", "magma", "inferno", or "plasma".

A function that receives a single value between 0 and 1 and returns a color.Examples: colorRamp(c("#000000", "#FFFFFF"), interpolate = "spline").

Value

plots a map

Author(s)

Bernd Gruber – Post tohttps://groups.google.com/d/forum/dartr

Examples

require("dartR.data")gl.map.interactive(bandicoot.gl)cols <- c("red","blue","yellow")[as.numeric(pop(platypus.gl))]gl.map.interactive(platypus.gl, ind.circle.cols=cols, ind.circle.cex=10, ind.circle.transparency=0.5)

Maps a STRUCTURE plot using a genlight object

Description

This function takes the output of plotstructure (the q matrix) and maps theq-matrix across using the population centers from the genlight object thatwas used to run the structure analysis viagl.run.structure)and plots the typical structure bar plots on a spatial map, providing abarplot for each subpopulation. Therefore it requires coordinates from agenlight object. This kind of plots should support the interpretation of thespatial structure of a population, but in principle is not different fromgl.plot.structure

Usage

gl.map.structure(  qmat,  x,  K,  provider = "Esri.NatGeoWorldMap",  scalex = 1,  scaley = 1,  movepops = NULL,  pop.labels = TRUE,  pop.labels.cex = 12)

Arguments

Details

Creates a mapped version of structure plots. For possible background mapscheck as specified via the provider:http://leaflet-extras.github.io/leaflet-providers/preview/index.html.You may need to adjust scalex and scaley values [default 1], as the sizedepends on the scale of the map and the position of the populations.

Value

An interactive map that shows the structure plots broken down by population.

returns the map and a list of the qmat split into sorted matrices perpopulation. This can be used to create your own map.

Author(s)

Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

References

• Pritchard, J.K., Stephens, M., Donnelly, P. (2000) Inference ofpopulation structure using multilocus genotype data. Genetics 155, 945-959.

• Archer, F. I., Adams, P. E. and Schneiders, B. B. (2016) strataG: An Rpackage for manipulating, summarizing and analysing population genetic data.Mol Ecol Resour. doi:10.1111/1755-0998.12559

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

• Mattias Jakobsson and Noah A. Rosenberg. 2007. CLUMPP: a clustermatching and permutation program for dealing with label switching andmultimodality in analysis of population structure. Bioinformatics23(14):1801-1806. Available atclumpp

See Also

gl.run.structure,clumpp,gl.plot.structure

Examples

## Not run: #bc <- bandicoot.gl[,1:100]#sr <- gl.run.structure(bc, k.range = 2:5, num.k.rep = 3, exec = './structure.exe')#ev <- gl.evanno(sr)#ev#qmat <- gl.plot.structure(sr, k=2:4)#' #head(qmat)#gl.map.structure(qmat, bc,K=3)#gl.map.structure(qmat, bc,K=4)#move population 4 (out of 5) 0.5 degrees to the right and populations 1#0.3 degree to the north of the map.#mp <- data.frame(lon=c(0,0,0,0.5,0), lat=c(-0.3,0,0,0,0))#gl.map.structure(qmat, bc,K=4, movepops=mp)## End(Not run)

Merges two or more populations in a genlight object into one population

Description

Individuals are assigned to populations based on the specimen metadata datafile (csv) used with gl.read.dart().

This script assigns individuals from two nominated populations into a newsingle population. It can also be used to rename populations.

The script returns a genlight object with the new population assignments.

Usage

gl.merge.pop(x, old = NULL, new = NULL, verbose = NULL)

Arguments

Value

A genlight object with the new population assignments.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

   gl <- gl.merge.pop(testset.gl, old=c('EmsubRopeMata','EmvicVictJasp'), new='Outgroup')

Creates an input file for the program NewHybrids and runs it if NewHybrids is installed

Description

This function compares two sets of parental populations to identify loci thatexhibit a fixed difference, returns an genlight object with the reduceddata, and creates an input file for the program NewHybrids using the top 200(or user-specified lower loc.limit) loci. In the absence of two identifiedparental populations, the script will select a random set of up to 200 loci only(method='random') or up to the first 200 loci ranked on information content(method='AvgPIC').

A fixed difference occurs when a SNP allele is present in all individualsof one population and absent in the other. There is provision for settinga level of tolerance, e.g. threshold = 0.05 which considers alleles presentat greater than 95a fixed difference. Only up to 200 loci are retained, because of limitationsof NewHybids.

If you specify a directory for the NewHybrids executable file, then thescript will create the input file from the SNP data then run NewHybrids. Ifthe directory is set to NULL, the execution will stop once the input file(default='nhyb.txt') has been written to disk. Note: the executable optionwill not work on a Mac; Mac users should generate the NewHybrids input fileand run this on their local installation of NewHybrids.

Refer to the New Hybrids manual for further information on the parameters toset– http://ib.berkeley.edu/labs/slatkin/eriq/software/new_hybs_doc1_1Beta3.pdf

It is important to stringently filter the data on RepAvg and CallRate ifusing the random option. One might elect to repeat the analysis(method='random') and combine the resultant posterior probabilities shouldthe maximum of 200 loci be considered insufficient.

The F1 individuals should be homozygous at all loci for which the parentalpopulations are fixed and different, assuming parental populations have beenspecified. Sampling errors can result in this not being the case, especiallywhere the sample sizes for the parental populations are small. Alternatively,the threshold for posterior probabilities used to determine assignment(pprob) or the definition of a fixed difference (threshold) may be too lax.To assess the error rate in the determination of assignment of F1individuals, a plot of the frequency of homozygous reference, heterozygotesand homozygous alternate (SNP) can be produced by setting plot=TRUE (thedefault).

Usage

gl.nhybrids(  gl,  outpath = tempdir(),  p0 = NULL,  p1 = NULL,  threshold = 0,  method = "random",  loc.limit = 200,  plot = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  pprob = 0.95,  nhyb.directory = NULL,  BurnIn = 10000,  sweeps = 10000,  GtypFile = "TwoGensGtypFreq.txt",  AFPriorFile = NULL,  PiPrior = "Jeffreys",  ThetaPrior = "Jeffreys",  verbose = NULL)

Arguments

Value

The reduced genlight object, if parentals are provided; output ofNewHybrids is saved to the working directory.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

References

Anderson, E.C. and Thompson, E.A.(2002). A model-based method for identifying species hybrids using multilocus genetic data. Genetics. 160:1217-1229.

Examples

## Not run: m <- gl.nhybrids(testset.gl, p0=NULL, p1=NULL,nhyb.directory='D:/workspace/R/NewHybsPC', # Specify as necessaryoutpath="D:/workspace",  # Specify as necessary, usually getwd() [= workspace]BurnIn=100,sweeps=100,verbose=3)## End(Not run)

Identifies loci under selection per population using the outflankmethod of Whitlock and Lotterhos (2015)

Description

Identifies loci under selection per population using the outflankmethod of Whitlock and Lotterhos (2015)

Usage

gl.outflank(  gi,  plot = TRUE,  LeftTrimFraction = 0.05,  RightTrimFraction = 0.05,  Hmin = 0.1,  qthreshold = 0.05,  ...)

Arguments

Details

This function is a wrapper around the outflank function provided byWhitlock and Lotterhos. To be able to run this function the packages qvalue(from bioconductor) and outflank (from github) needs to be installed. To doso see example below.

Value

Returns an index of outliers and the full outflank list

References

Whitlock, M.C. and Lotterhos K.J. (2015) Reliable detection of lociresponsible for local adaptation: inference of a neutral model throughtrimming the distribution of Fst. The American Naturalist 186: 24 - 36.

Github repository: Whitlock & Lotterhos:https://github.com/whitlock/OutFLANK (Check the readme.pdf within therepository for an explanation. Be aware you now can run OufFLANK from agenlight object)

See Also

utils.outflank,utils.outflank.plotter,utils.outflank.MakeDiploidFSTMat

Examples

gl.outflank(bandicoot.gl, plot = TRUE)

Ordination applied to genotypes in a genlight object (PCA), in an fdobject, or to a distance matrix (PCoA)

Description

This function takes the genotypes for individuals and undertakes a PearsonPrincipal Component analysis (PCA) on SNP or Tag P/A (SilicoDArT) data; itundertakes a Gower Principal Coordinate analysis (PCoA) if supplied with adistance matrix. Technically, any distance matrix can be represented in anordinated space using PCoA.

Usage

gl.pcoa(  x,  nfactors = 5,  correction = NULL,  mono.rm = TRUE,  parallel = FALSE,  n.cores = 16,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The function is essentially a wrapper for glPcaadegenet or pcoa {ape}with default settings apart from those specified as parameters in thisfunction. Sources of stress in the visual representation

While, technically, any distance matrix can be represented in an ordinatedspace, the representation will not typically be exact.There are three majorsources of stress in a reduced-representation of distances or dissimilaritiesamong entities using PCA or PCoA. By far the greatest source comes from thedecision to select only the top two or three axes from the ordinated set ofaxes derived from the PCA or PCoA. The representation of the entities such aheavily reduced space will not faithfully represent the distances in theinput distance matrix simply because of the loss of information in deeperinformative dimensions. For this reason, it is not sensible to be tooprecious about managing the other two sources of stress in the visualrepresentation.

The measure of distance between entities in a PCA is the Pearson CorrelationCoefficient, essentially a standardized Euclidean distance. This is both ametric distance and a Euclidean distance. In PCoA, the second source ofstress is the choice of distance measure or dissimilarity measure. While anydistance or dissimilarity matrix can be represented in an ordinated space,the distances between entities can be faithfully represented in that space(that is, without stress) only if the distances are metric. Furthermore, fordistances between entities to be faithfully represented in a rigid Cartesianspace, the distance measure needs to be Euclidean. If this is not the case,the distances between the entities in the ordinated visualized space will notexactly represent the distances in the input matrix (stress will be non-zero).This source of stress will be evident as negative eigenvalues in the deeperdimensions.

A third source of stress arises from having a sparse dataset, one withmissing values. This affects both PCA and PCoA. If the original data matrixis not fully populated, that is, if there are missing values, then even aEuclidean distance matrix will not necessarily be 'positive definite'. Itfollows that some of the eigenvalues may be negative, even though thedistance metric is Euclidean. This issue is exacerbated when the number ofloci greatly exceeds the number of individuals, as is typically the case whenworking with SNP data. The impact of missing values can be minimized bystringently filtering on Call Rate, albeit with loss of data. An alternativeis given in a paper 'Honey, I shrunk the sample covariance matrix' and morerecently by Ledoit and Wolf (2018), but their approach has not beenimplemented here.

The good news is that, unless the sum of the negative eigenvalues, arisingfrom a non-Euclidean distance measure or from missing values, approachesthose of the final PCA or PCoA axes to be displayed, the distortion isprobably of no practical consequence and certainly not comparable to thestress arising from selecting only two or three final dimensions out ofseveral informative dimensions for the visual representation.

Function's output

Two diagnostic plots are produced. The first is a Scree Plot, showing thepercentage variation explained by each of the PCA or PCoA axes, for thoseaxes that explain more than the original variables (loci) on average. Thatis, only informative axes are displayed. The scree plot informs the number ofdimensions to be retained in the visual summaries. As a rule of thumb, axeswith more than 10

The second graph shows the distribution of eigenvalues for the remaininguninformative (noise) axes, including those with negative eigenvalues.

Action is recommended (verbose >= 2) if the negative eigenvalues aredominant, their sum approaching in magnitude the eigenvalues for axesselected for the final visual solution.

Output is a glPca object conforming to adegenet::glPca but with only thefollowing retained.

• $call - The call that generated the PCA/PCoA

• $eig - Eigenvalues – All eigenvalues (positive, null, negative).

• $scores - Scores (coefficients) for each individual

• $loadings - Loadings of each SNP for each principal component

Plots and table were saved to the temporal directory (tempdir) and can beaccessed with the functiongl.print.reports and listed withthe functiongl.list.reports. Note that they can be accessedonly in the current R session because tempdir is cleared each time that the Rsession is closed.

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

PCA was developed by Pearson (1901) and Hotelling (1933), whilst the bestmodern reference is Jolliffe (2002). PCoA was developed by Gower (1966) whilethe best modern reference is Legendre & Legendre (1998).

Value

An object of class pcoa containing the eigenvalues and factor scores

Author(s)

Author(s): Arthur Georges. Custodian: Arthur Georges (Post tohttps://groups.google.com/d/forum/dartr)

References

• Cailliez, F. (1983) The analytical solution of the additive constantproblem. Psychometrika, 48, 305-308.

• Gower, J. C. (1966) Some distance properties of latent root and vectormethods used in multivariate analysis. Biometrika, 53, 325-338.

• Hotelling, H., 1933. Analysis of a complex of statistical variables intoPrincipal Components. Journal of Educational Psychology 24:417-441, 498-520.

• Jolliffe, I. (2002) Principal Component Analysis. 2nd Edition, Springer,New York.

• Ledoit, O. and Wolf, M. (2018). Analytical nonlinear shrinkage oflarge-dimensional covariance matrices. University of Zurich, Department ofEconomics, Working Paper No. 264, Revised version. Available at SSRN:https://ssrn.com/abstract=3047302 or http://dx.doi.org/10.2139/ssrn.3047302

• Legendre, P. and Legendre, L. (1998). Numerical Ecology, Volume 24, 2ndEdition. Elsevier Science, NY.

• Lingoes, J. C. (1971) Some boundary conditions for a monotone analysisof symmetric matrices. Psychometrika, 36, 195-203.

• Pearson, K. (1901). On lines and planes of closest fit to systems ofpoints in space. Philosophical Magazine. Series 6, vol. 2, no. 11, pp.559-572.

See Also

gl.pcoa.plot

Examples

## Not run: gl <- possums.gl# PCA (using SNP genlight object)pca <- gl.pcoa(possums.gl[1:50,],verbose=2)gl.pcoa.plot(pca,gl)gs <- testset.gslevels(pop(gs))<-c(rep('Coast',5),rep('Cooper',3),rep('Coast',5),rep('MDB',8),rep('Coast',6),'Em.subglobosa','Em.victoriae')# PCA (using SilicoDArT genlight object)pca <- gl.pcoa(gs)gl.pcoa.plot(pca,gs)# Collapsing pops to OTUs using Fixed Difference Analysis (using fd object)fd <- gl.fixed.diff(testset.gl)fd <- gl.collapse(fd)pca <- gl.pcoa(fd)gl.pcoa.plot(pca,fd$gl)# Using a distance matrixD <- gl.dist.ind(testset.gs, method='jaccard')pcoa <- gl.pcoa(D,correction="cailliez")gl.pcoa.plot(pcoa,gs)## End(Not run)

Bivariate or trivariate plot of the results of an ordination generatedusing gl.pcoa()

Description

This script takes output from the ordination generated by gl.pcoa() and plotsthe individuals classified by population.

Usage

gl.pcoa.plot(  glPca,  x,  scale = FALSE,  ellipse = FALSE,  plevel = 0.95,  pop.labels = "pop",  interactive = FALSE,  as.pop = NULL,  hadjust = 1.5,  vadjust = 1,  xaxis = 1,  yaxis = 2,  zaxis = NULL,  pt.size = 2,  pt.colors = NULL,  pt.shapes = NULL,  label.size = 1,  axis.label.size = 1.5,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The factor scores are taken from the output of gl.pcoa() and the populationassignments are taken from from the original data file. In the bivariateplots, the specimens are shown optionally with adjacent labels and enclosingellipses. Population labels on the plot are shuffled so as not to overlap(using package {directlabels}).This can be a bit clunky, as the labels may be some distance from the pointsto which they refer, but it provides the opportunity for moving labels aroundusing graphics software (e.g. Adobe Illustrator).

3D plotting is activated by specifying a zaxis.

Any pair or trio of axes can be specified from the ordination, provided theyare within the range of the nfactors value provided to gl.pcoa().In the 2D plots, axes can be scaled to represent the proportion of variationexplained. In any case, the proportion of variation explained by each axis isprovided in the axis label.

Colors and shapes of the points can be altered by passing a vector of shapesand/or a vector of colors. These vectors can be created withgl.select.shapes() and gl.select.colors() and passed to this script using thept.shapes and pt.colors parameters.

Points displayed in the ordination can be identified if the optioninteractive=TRUE is chosen, in which case the resultant plot is ggplotly()friendly. Identification of points is by moving the mouse over them. Referto the plotly package for further information.The interactive option is automatically enabled for 3D plotting.

Value

returns no value (i.e. NULL)

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.pcoa

Other Exploration/visualisation functions:gl.select.colors(),gl.select.shapes(),gl.smearplot()

Examples

# SET UP DATASETgl <- testset.gllevels(pop(gl))<-c(rep('Coast',5),rep('Cooper',3),rep('Coast',5),rep('MDB',8),rep('Coast',7),'Em.subglobosa','Em.victoriae')# RUN PCApca<-gl.pcoa(gl,nfactors=5)# VARIOUS EXAMPLESgl.pcoa.plot(pca, gl, ellipse=TRUE, plevel=0.95, pop.labels='pop', axis.label.size=1, hadjust=1.5,vadjust=1)gl.pcoa.plot(pca, gl, ellipse=TRUE, plevel=0.99, pop.labels='legend', axis.label.size=1)gl.pcoa.plot(pca, gl, ellipse=TRUE, plevel=0.99, pop.labels='legend', axis.label.size=1.5,scale=TRUE)gl.pcoa.plot(pca, gl, ellipse=TRUE, axis.label.size=1.2, xaxis=1, yaxis=3, scale=TRUE)gl.pcoa.plot(pca, gl, pop.labels='none',scale=TRUE)gl.pcoa.plot(pca, gl, axis.label.size=1.2, interactive=TRUE)gl.pcoa.plot(pca, gl, ellipse=TRUE, plevel=0.99, xaxis=1, yaxis=2, zaxis=3)# color AND SHAPE ADJUSTMENTSshp <- gl.select.shapes(select=c(16,17,17,0,2))col <- gl.select.colors(library='brewer',palette='Spectral',ncolors=11,select=c(1,9,3,11,11))gl.pcoa.plot(pca, gl, ellipse=TRUE, plevel=0.95, pop.labels='pop', pt.colors=col, pt.shapes=shp, axis.label.size=1, hadjust=1.5,vadjust=1)gl.pcoa.plot(pca, gl, ellipse=TRUE, plevel=0.99, pop.labels='legend', pt.colors=col, pt.shapes=shp, axis.label.size=1)  test <- gl.pcoa(platypus.gl) gl.pcoa.plot(glPca = test, x = platypus.gl)

Generates percentage allele frequencies by locus and population

Description

This is a support script, to take SNP data or SilicoDArT presence/absencedata grouped into populations in a genlight object {adegenet} and generatea table of allele frequencies for each population and locus

Usage

gl.percent.freq(x, verbose = NULL)

Arguments

Value

A matrix with allele (SNP data) or presence/absence frequencies(Tag P/A data) broken down by population and locus

Author(s)

Custodian: Arthur Georges (Post tohttps://groups.google.com/d/forum/dartr)

Examples

m <-  gl.percent.freq(testset.gl)

Replays the history and applies it to a genlight object

Description

Replays the history and applies it to a genlight object

Usage

gl.play.history(x, history = NULL, verbose = 0)

Arguments

Details

This function basically allows to create a 'template history'(=set of filters) and apply them to any other genlight object. Histories canalso be saved and loaded (see. gl.save.history and gl.load.history).

Value

Returns a genlight object that was created by replaying the providedapplied to the genlight object x. Please note you can 'mix' histories orpart of them and apply them to different genlight objects. If the historydoes not containgl.read.dart, histories of x and history areconcatenated.

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr).

Examples

## Not run: dartfile <- system.file('extdata','testset_SNPs_2Row.csv', package='dartR')metadata <- system.file('extdata','testset_metadata.csv', package='dartR')gl <- gl.read.dart(dartfile, ind.metafile = metadata, probar=FALSE)gl2 <- gl.filter.callrate(gl, method='loc', threshold=0.9)gl3 <- gl.filter.callrate(gl2, method='ind', threshold=0.95)#Now 'replay' part of the history 'onto' another genlight object#bc.fil <- gl.play.history(gl.compliance.check(bandicoot.gl),#history=gl3@other$history[c(2,3)], verbose=1)#gl.print.history(bc.fil)## End(Not run)

Plots fastStructure analysis results (Q-matrix)

Description

This function takes a fastStructure run object (output fromgl.run.faststructure) and plots the typical structure barplot that visualize the q matrix of a fastStructure run.

Usage

gl.plot.faststructure(  sr,  k.range,  met_clumpp = "greedyLargeK",  iter_clumpp = 100,  clumpak = TRUE,  plot_theme = NULL,  colors_clusters = NULL,  ind_name = TRUE,  border_ind = 0.15)

Arguments

Details

The function outputs a barplot which is the typical output offastStructure.

This function is based on the methods of CLUMPP and Clumpak as implemented in the R package starmie (https://github.com/sa-lee/starmie).

The Clumpak method identifies sets of highly similar runs among all the replicates of the same K. The method then separates the distinct groups of runs representing distinct modes in the space of possible solutions.

The CLUMPP method permutes the clusters output by independent runs of clustering programs such as structure, so that they match up as closely as possible.

This function averages the replicates within each mode identified by the Clumpak method.

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

List of Q-matrices

Author(s)

Bernd Gruber & Luis Mijangos (Post tohttps://groups.google.com/d/forum/dartr)

References

• Raj, A., Stephens, M., & Pritchard, J. K. (2014). fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics, 197(2), 573-589.

• Pritchard, J.K., Stephens, M., Donnelly, P. (2000) Inference ofpopulation structure using multilocus genotype data. Genetics 155, 945-959.

• Kopelman, Naama M., et al. "Clumpak: a program for identifying clustering modes and packaging population structure inferences across K." Molecular ecology resources 15.5 (2015): 1179-1191.

• Mattias Jakobsson and Noah A. Rosenberg. 2007. CLUMPP: a clustermatching and permutation program for dealing with label switching andmultimodality in analysis of population structure. Bioinformatics23(14):1801-1806. Available atclumpp

See Also

gl.run.faststructure

Examples

## Not run: t1 <- gl.filter.callrate(platypus.gl,threshold = 1)res <- gl.run.faststructure(t1, exec = "./fastStructure",k.range = 2:3,                           num.k.rep = 2,output = paste0(getwd(),"/res_str"))qmat <- gl.plot.faststructure(res,k.range=2:3)gl.map.structure(qmat, K=2, t1, scalex=1, scaley=0.5)## End(Not run)

Represents a distance matrix as a heatmap

Description

The script plots a heat map to represent the distances in the distance ordissimilarity matrix. This function is a wrapper forheatmap.2 (package gplots).

Usage

gl.plot.heatmap(D, palette.divergent = gl.colors("div"), verbose = NULL, ...)

Arguments

Value

returns no value (i.e. NULL)

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr)

Examples

## Not run:    gl <- testset.gl[1:10,]   D <- dist(as.matrix(gl),upper=TRUE,diag=TRUE)   gl.plot.heatmap(D)   D2 <- gl.dist.pop(possums.gl)   gl.plot.heatmap(D2)   D3 <- gl.fixed.diff(testset.gl)   gl.plot.heatmap(D3)   ## End(Not run)   if ((requireNamespace("gplots", quietly = TRUE))) {   D2 <- gl.dist.pop(possums.gl)   gl.plot.heatmap(D2)   }

Represents a distance or dissimilarity matrix as a network

Description

This script takes a distance matrix generated by dist() and represents therelationship among the specimens as a network diagram. In order to use thisscript, a decision is required on a threshold for relatedness to berepresented as link in the network, and on the layout used to create thediagram.

Usage

gl.plot.network(  D,  x = NULL,  method = "fr",  node.size = 3,  node.label = FALSE,  node.label.size = 0.7,  node.label.color = "black",  alpha = 0.005,  title = "Network based on genetic distance",  verbose = NULL)

Arguments

Details

The threshold for relatedness to be represented as a link in the network isspecified as a quantile. Those relatedness measures above the quantile areplotted as links, those below the quantile are not. Often you are looking forrelatedness outliers in comparison with the overall relatedness amongindividuals, so a very conservative quantile is used (e.g. 0.004), butultimately, this decision is made as a matter of trial and error. One way toapproach this trial and error is to try to achieve a sparse set of linksbetween unrelated 'background' individuals so that the stronger links arepreferentially shown.

There are several layouts from which to choose. The most popular are given asoptions in this script.

• fr – Fruchterman, T.M.J. and Reingold, E.M. (1991). Graph Drawing byForce-directed Placement. Software – Practice and Experience 21:1129-1164.

• kk – Kamada, T. and Kawai, S.: An Algorithm for Drawing GeneralUndirected Graphs. Information Processing Letters 31:7-15, 1989.

• drl – Martin, S., Brown, W.M., Klavans, R., Boyack, K.W., DrL:Distributed Recursive (Graph) Layout. SAND Reports 2936:1-10, 2008.

Colors of node symbols are those of the rainbow.

Value

returns no value (i.e. NULL)

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

if ((requireNamespace("rrBLUP", quietly = TRUE)) & (requireNamespace("gplots", quietly = TRUE))) {test <- gl.subsample.loci(platypus.gl, n = 100)test <- gl.keep.ind(test,ind.list = indNames(test)[1:10])D <- gl.grm(test, legendx=0.04)gl.plot.network(D,test)}

Plots STRUCTURE analysis results (Q-matrix)

Description

This function takes a structure run object (output fromgl.run.structure) and plots the typical structure barplot that visualize the q matrix of a structure run.

Usage

gl.plot.structure(  sr,  K = NULL,  met_clumpp = "greedyLargeK",  iter_clumpp = 100,  clumpak = TRUE,  plot_theme = NULL,  colors_clusters = NULL,  ind_name = TRUE,  border_ind = 0.15,  plot.out = TRUE,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The function outputs a barplot which is the typical output ofstructure. For a Evanno plot use gl.evanno.

This function is based on the methods of CLUMPP and Clumpak as implemented in the R package starmie (https://github.com/sa-lee/starmie).

The Clumpak method identifies sets of highly similar runs among all the replicates of the same K. The method then separates the distinct groups of runs representing distinct modes in the space of possible solutions.

The CLUMPP method permutes the clusters output by independent runs of clustering programs such as structure, so that they match up as closely as possible.

This function averages the replicates within each mode identified by the Clumpak method.

Plots and table are saved to the temporal directory (tempdir) and can beaccessed with the functiongl.print.reports and listed withthe functiongl.list.reports. Note that they can be accessedonly in the current R session because tempdir is cleared each time that theR session is closed.

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

List of Q-matrices

Author(s)

Bernd Gruber & Luis Mijangos (Post tohttps://groups.google.com/d/forum/dartr)

References

• Pritchard, J.K., Stephens, M., Donnelly, P. (2000) Inference ofpopulation structure using multilocus genotype data. Genetics 155, 945-959.

• Kopelman, Naama M., et al. "Clumpak: a program for identifying clustering modes and packaging population structure inferences across K." Molecular ecology resources 15.5 (2015): 1179-1191.

• Mattias Jakobsson and Noah A. Rosenberg. 2007. CLUMPP: a clustermatching and permutation program for dealing with label switching andmultimodality in analysis of population structure. Bioinformatics23(14):1801-1806. Available atclumpp

See Also

gl.run.structure,gl.plot.structure

Examples

## Not run: #bc <- bandicoot.gl[,1:100]#sr <- gl.run.structure(bc, k.range = 2:5, num.k.rep = 3, exec = './structure')#ev <- gl.evanno(sr)#ev#qmat <- gl.plot.structure(sr, K=3)#head(qmat)#gl.map.structure(qmat, K=3, bc, scalex=1, scaley=0.5)## End(Not run)

Prints history of a genlight object

Description

Prints history of a genlight object

Usage

gl.print.history(x = NULL, history = NULL)

Arguments

Value

Prints a table with all history records. Currently the style cannotbe changed.

Author(s)

Bernd Gruber (bugs? Post tohttps://groups.google.com/d/forum/dartr)

Examples

dartfile <- system.file('extdata','testset_SNPs_2Row.csv', package='dartR')metadata <- system.file('extdata','testset_metadata.csv', package='dartR')gl <- gl.read.dart(dartfile, ind.metafile = metadata, probar=FALSE)gl2 <- gl.filter.callrate(gl, method='loc', threshold=0.9)gl3 <- gl.filter.callrate(gl2, method='ind', threshold=0.95)#Now 'replay' part of the history 'onto' another genlight object#bc.fil <- gl.play.history(gl.compliance.check(bandicoot.gl),#history=gl3@other$history[c(2,3)], verbose=1)#gl.print.history(bc.fil)

Prints dartR reports saved in tempdir

Description

Prints dartR reports saved in tempdir

Usage

gl.print.reports(print_report)

Arguments

Value

Prints reports that were saved in tempdir.

Author(s)

Bernd Gruber & Luis Mijangos (bugs? Post tohttps://groups.google.com/d/forum/dartr)

See Also

gl.list.reports

Examples

## Not run: reports <- gl.print.reports(1)## End(Not run)

Calculates a similarity (distance) matrix for individuals on the proportion ofshared alleles

Description

This script calculates an individual based distance matrix. It uses an C++implementation, so package Rcpp needs to be installed and it is thereforereally fast (once it has compiled the function after the first run).

Usage

gl.propShared(x)

Arguments

Value

A similarity matrix

Author(s)

Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

Examples

#takes some time at the first run of the function...## Not run: res <- gl.propShared(bandicoot.gl)res[1:5,1:7] #show only a small part of the matrix## End(Not run)

Randomly changes the allocation of 0's and 2's in a genlight object

Description

This function samples randomly half of the SNPs and re-codes, in the sampledSNP's, 0's by 2's.

Usage

gl.random.snp(x, plot.out = TRUE, save2tmp = FALSE, verbose = NULL)

Arguments

Details

DArT calls the most common allele as the reference allele. In a genlightobject, homozygous for the reference allele are coded with a '0' andhomozygous for the alternative allele are coded with a '2'. This causes somedistortions in visuals from time to time.

If plot.out = TRUE, two smear plots (pre-randomisation andpost-randomisation) are presented using a random subset of individuals (10)and loci (100) to provide an overview of the changes.

Resultant ggplots are saved to the session's temporary directory.

Value

Returns a genlight object with half of the loci re-coded.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

Examples

require("dartR.data")res <- gl.random.snp(platypus.gl[1:5,1:5],verbose = 5)

Reads SNP data from a csv file into a genlight object

Description

This script takes SNP genotypes from a csv file, combines them withindividual and locus metrics and creates a genlight object.

Usage

gl.read.csv(  filename,  transpose = FALSE,  ind.metafile = NULL,  loc.metafile = NULL,  verbose = NULL)

Arguments

Details

The SNP data need to be in one of two forms. SNPs can be coded 0 forhomozygous reference, 2 for homozygous alternate, 1 for heterozygous, and NA for missing values; or the SNP data can be coded A/A, A/C, C/T, G/A etc,and -/- for missing data. In this format, the reference allele is the most frequent allele, as used by DArT. Other formats will throw an error.

The SNP data need to be individuals as rows, labeled, and loci as columns,also labeled. If the orientation is individuals as columns and loci by rows,then set transpose=TRUE.

The individual metrics need to be in a csv file, with headings, with amandatory id column corresponding exactly to the individual identity labelsprovided with the SNP data and in the same order.

The locus metadata needs to be in a csv file with headings, with a mandatorycolumn headed AlleleID corresponding exactly to the locus identity labelsprovided with the SNP data and in the same order.

Note that the locus metadata will be complemented by calculable statisticscorresponding to those that would be provided by Diversity Arrays Technology(e.g. CallRate).

Value

A genlight object with the SNP data and associated metadata included.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

Examples

csv_file <- system.file('extdata','platy_test.csv', package='dartR')ind_metadata <- system.file('extdata','platy_ind.csv', package='dartR')gl  <- gl.read.csv(filename = csv_file, ind.metafile = ind_metadata)

Imports DArT data into dartR and converts it into a dartR genlight object

Description

This function is a wrapper function that allows you to convert your DArT fileinto a genlight object of class dartR.

Usage

gl.read.dart(  filename,  ind.metafile = NULL,  recalc = TRUE,  mono.rm = FALSE,  nas = "-",  topskip = NULL,  lastmetric = NULL,  covfilename = NULL,  service.row = 1,  plate.row = 3,  probar = FALSE,  verbose = NULL)

Arguments

Details

The function will determine automatically if the data are in Diversity Arraysone-row csv format or two-row csv format.

The first row of data is determined from the number of rows with an * in the first column. This can be alternatively specified with the topskip parameter.

The DArT service code is added to the ind.metrics of the genlight object. The row containing the service code for each individual can be specified with the service.row parameter.

#'The DArT plate well is added to the ind.metrics of the genlight object. The row containing the plate well for each individual can be specified with the plate.row parameter.

If individuals have been deleted from the input file manually, then the locusmetrics supplied by DArT will no longer be correct and some loci may bemonomorphic. To accommodate this, set mono.rm and recalc to TRUE.

Value

A dartR genlight object that contains individual and locus metrics[if data were provided] and locus metrics [from a DArT report].

Author(s)

Custodian: Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

See Also

Other dartR-base:gl.drop.ind(),gl.drop.loc(),gl.drop.pop(),gl.edit.recode.ind(),gl.edit.recode.pop(),gl.keep.loc(),gl.make.recode.ind(),gl.recode.ind(),gl.recode.pop(),gl.set.verbosity()

Examples

dartfile <- system.file('extdata','testset_SNPs_2Row.csv', package='dartR')metadata <- system.file('extdata','testset_metadata.csv', package='dartR')gl <- gl.read.dart(dartfile, ind.metafile = metadata, probar=TRUE)

Reads FASTA files and converts them to genlight object

Description

The following IUPAC Ambiguity Codes are taken as heterozygotes:

• M is heterozygote forAC and CA

• R is heterozygotefor AG and GA

• W is heterozygotefor AT and TA

• S is heterozygotefor CG and GC

• Y is heterozygotefor CT and TC

• K is heterozygotefor GT and TG

The following IUPAC Ambiguity Codes are taken as missing data:

• V

• H

• D

• B

• N

The function can deal with missing data in individuals, e.g. when FASTA files have different number of individuals due to missing data.

The allele with the highest frequency is taken as the reference allele.

SNPs with more than two alleles are skipped.

Usage

gl.read.fasta(fasta_files, parallel = FALSE, n_cores = NULL, verbose = NULL)

Arguments

Details

Ambiguity characters are often used to code heterozygotes. However, usingheterozygotes as ambiguity characters may bias many estimates. See moreinformation in the link below:https://evodify.com/heterozygotes-ambiguity-characters/

Value

A genlight object.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

Examples

 # Folder where the fasta files are located.  folder_samples <- system.file('extdata', package ='dartR') # listing the FASTA files, including their path. Files have an extension # that contains "fas". file_names <- list.files(path = folder_samples, pattern = "*.fas",                           full.names = TRUE) # reading fasta files  obj <- gl.read.fasta(file_names)

Imports presence/absence data from SilicoDArT to genlight {agegenet}format (ploidy=1)

Description

DaRT provide the data as a matrix of entities (individual animals) across thetop and attributes (P/A of sequenced fragment) down the side in a formatthat is unique to DArT. This program reads the data in to adegenet formatfor consistency with other programming activity. The script may requiremodification as DArT modify their data formats from time to time.

Usage

gl.read.silicodart(  filename,  ind.metafile = NULL,  nas = "-",  topskip = NULL,  lastmetric = "Reproducibility",  probar = TRUE,  verbose = NULL)

Arguments

Details

gl.read.silicodart() opens the data file (csv comma delimited) and skips thefirst n=topskip lines. The script assumes that the next line contains theentity labels (specimen ids) followed immediately by the SNP data for thefirst locus.

It reads the presence/absence data into a matrix of 1s and 0s, and inputs thelocus metadata and specimen metadata. The locus metadata comprises a seriesof columns of values for each locus including the essential columns ofCloneID and the desirable variables Reproducibility and PIC. Refer todocumentation provide by DArT for an explanation of these columns.

The specimen metadata provides the opportunity to reassign specimens topopulations, and to add other data relevant to the specimen. The keyvariables are id (specimen identity which must be the same and in the sameorder as the SilicoDArT file, each unique), pop (population assignment), lat(latitude, optional) and lon (longitude, optional). id, pop, lat, lon arethe column headers in the csv file. Other optional columns can be added.

The data matrix, locus names (forced to be unique), locus metadata, specimennames, specimen metadata are combined into a genind object. Refer to thedocumentation for {adegenet} for further details.

Value

An object of classgenlight with ploidy set to 1, containingthe presence/absence data, and locus and individual metadata.

Author(s)

Custodian: Bernd Gruber – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.read.dart

Examples

silicodartfile <- system.file('extdata','testset_SilicoDArT.csv', package='dartR')metadata <- system.file('extdata',ind.metafile ='testset_metadata_silicodart.csv', package='dartR')testset.gs <- gl.read.silicodart(filename = silicodartfile, ind.metafile = metadata)

Converts a vcf file into a genlight object

Description

This function needs package vcfR, please install it.

Usage

gl.read.vcf(vcffile, ind.metafile = NULL, verbose = NULL)

Arguments

Details

The ind.metadata file needs to have very specific headings. First a headingcalled id. Here the ids have to match the ids in the dartR object. The following column headings are optional.pop: specifies the population membership of each individual. lat and lonspecify spatial coordinates (in decimal degrees WGS1984 format). Additionalcolumns with individual metadata can be imported (e.g. age, gender).

Value

A genlight object.

Author(s)

Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr)

Examples

## Not run: obj <- gl.read.vcf(system.file('extdata/test.vcf', package='dartR'))## End(Not run)

Assigns an individual metric as pop in a genlight {adegenet} object

Description

Individuals are assigned to populations based on theindividual/sample/specimen metrics file (csv) used with gl.read.dart().

One might want to define the population structure in accordance with anotherclassification, such as using an individual metric (e.g. sex, male orfemale). This script discards the current population assignments and replacesthem with new population assignments defined by a specified individualmetric.

The script returns a genlight object with the new population assignments.Note that the original population assignments are lost.

Usage

gl.reassign.pop(x, as.pop, verbose = NULL)

Arguments

Value

A genlight object with the reassigned populations.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

# SNP data   popNames(testset.gl)   gl <- gl.reassign.pop(testset.gl, as.pop='sex',verbose=3)   popNames(gl)# Tag P/A data   popNames(testset.gs)   gs <- gl.reassign.pop(testset.gs, as.pop='sex',verbose=3)   popNames(gs)

Recalculates locus metrics when individuals or populations are deleted from agenlight {adegenet} object

Description

When individuals,or populations, are deleted from a genlight object, thelocus metrics no longer apply. For example, the Call Rate may be differentconsidering the subset of individuals, compared with the full set. Thisscript recalculates those affected locus metrics, namely, avgPIC, CallRate,freqHets, freqHomRef, freqHomSnp, OneRatioRef, OneRatioSnp, PICRef andPICSnp.Metrics that remain unaltered are RepAvg and TrimmedSeq as they areunaffected by the removal of individuals.

Usage

gl.recalc.metrics(x, mono.rm = FALSE, verbose = NULL)

Arguments

Details

The script optionally removes resultant monomorphic loci or lociwith all values missing and deletes them (using gl.filter.monomorphs.r).

The script returns a genlight object with the recalculated locus metadata.

Value

A genlight object with the recalculated locus metadata.

Author(s)

Custodian: Luis Mijangos (Post tohttps://groups.google.com/d/forum/dartr)

See Also

gl.filter.monomorphs

Examples

  gl <- gl.recalc.metrics(testset.gl, verbose=2)

Recodes individual (=specimen = sample) labels in a genlight object

Description

This function recodes individual labels and/or deletes individuals from a DaRTgenlight SNP file based on a lookup table provided as a csv file.

Usage

gl.recode.ind(x, ind.recode, recalc = FALSE, mono.rm = FALSE, verbose = NULL)

Arguments

Details

Renaming individuals may be required when there have been errors in labelingarising in the process from sample to sequence files. There may be occasionswhere renaming individuals is required for preparation of figures. Whencaution needs to be exercised because of the potential for breaking the'chain of evidence' associated with the samples, recoding individuals usinga recode table (csv) can provide a durable record of the changes.

The function works with genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

For SNP genotype data, the function, having deleted individuals, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them. The script also optionally recalculates thelocus metadata as appropriate. The optional deletion of monomorphic lociand the optional recalculation of locus statistics is not available forTag P/A data (SilicoDArT).

The script returns a dartR genlight object with the new individual names and the recalculated locus metadata.

Value

A genlight or genind object with the recoded and reduced data.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.filter.monomorphs for filtering monomorphs,gl.recalc.metrics for recalculating locus metrics,gl.recode.pop for recoding populations

Other dartR-base:gl.drop.ind(),gl.drop.loc(),gl.drop.pop(),gl.edit.recode.ind(),gl.edit.recode.pop(),gl.keep.loc(),gl.make.recode.ind(),gl.read.dart(),gl.recode.pop(),gl.set.verbosity()

Examples

  file <- system.file('extdata','testset_ind_recode.csv', package='dartR')  gl <- gl.recode.ind(testset.gl, ind.recode=file, verbose=3)

Recodes population assignments in a genlight object

Description

This function recodes population assignments and/or deletes populations from aDaRT genlight object based on information provided in a csv populationrecode file.

Usage

gl.recode.pop(x, pop.recode, recalc = FALSE, mono.rm = FALSE, verbose = NULL)

Arguments

Details

Individuals are assigned to populations based on the specimen metadata datafile (csv) used with gl.read.dart(). Recoding can be used to amalgamatepopulations or to selectively delete or retain populations.

When caution needs to be exercised because of the potential for breaking the'chain of evidence' associated with the samples, recoding individuals usinga recode table (csv) can provide a durable record of the changes.

The population recode file contains a list of populations taken from the genlightobject as the first column of the csv file, and the new populationassignments are located in the second column of the csv file. The keyword 'Delete' used as a new population assignment will result in the associated specimen being dropped from the dataset.

The function works with genlight objectscontaining SNP genotypes and Tag P/A data (SilicoDArT).

For SNP genotype data, the function, having deleted populations, optionally identifies resultant monomorphic loci or loci with all values missing and deletes them. The script also optionally recalculates thelocus metadata as appropriate. The optional deletion of monomorphic lociand the optional recalculation of locus statistics is not available forTag P/A data (SilicoDArT).

Value

A genlight object with the recoded and reduced data.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.filter.monomorphs

gl.recode.pop

Other dartR-base:gl.drop.ind(),gl.drop.loc(),gl.drop.pop(),gl.edit.recode.ind(),gl.edit.recode.pop(),gl.keep.loc(),gl.make.recode.ind(),gl.read.dart(),gl.recode.ind(),gl.set.verbosity()

Examples

  mfile <- system.file('extdata', 'testset_pop_recode.csv', package='dartR')  nPop(testset.gl)  gl <- gl.recode.pop(testset.gl, pop.recode=mfile, verbose=3)

Renames a population in a genlight object

Description

Individuals are assigned to populations based on the specimen metadata datafile (csv) used with gl.read.dart().

This script renames a nominated population.

The script returns a genlight object with the new population name.

Usage

gl.rename.pop(x, old = NULL, new = NULL, verbose = NULL)

Arguments

Value

A genlight object with the new population name.

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

Examples

   gl <- gl.rename.pop(testset.gl, old='EmsubRopeMata', new='Outgroup')

Reports summary of base pair frequencies

Description

This script calculates the frequencies of the four DNA nucleotide bases:adenine (A), cytosine (C), 'guanine (G) and thymine (T), and the frequency oftransitions (Ts) and transversions (Tv) in a DArT genlight object.

Usage

gl.report.bases(  x,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The script checks first if trimmed sequences are included in thelocus metadata (@other$loc.metrics$TrimmedSequence), and if so, tallies upthe numbers of A, T, G and C bases. Only the reference state at the SNP locusis counted. Counts of transitions (Ts) and transversions (Tv) assume thatthere is no directionality, that is C->T is the same as T->C, because thereference state is arbitrary.

For presence/absence data (SilicoDArT), it is not possible to counttransversions or transitions or transversions/transitions ratio because theSNP data is not available, only a single sequence tag.

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

The unchanged genlight object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

Other report functions:gl.report.callrate(),gl.report.diversity(),gl.report.hamming(),gl.report.hwe(),gl.report.ld.map(),gl.report.locmetric(),gl.report.maf(),gl.report.monomorphs(),gl.report.overshoot(),gl.report.pa(),gl.report.parent.offspring(),gl.report.rdepth(),gl.report.replicates(),gl.report.reproducibility(),gl.report.secondaries(),gl.report.sexlinked(),gl.report.taglength()

Examples

# SNP data  out <- gl.report.bases(testset.gl)  #' # Tag P/A data  out <- gl.report.bases(testset.gs)

Reports summary of Call Rate for loci or individuals

Description

SNP datasets generated by DArT have missing values primarily arising fromfailure to call a SNP because of a mutation at one or both of the restrictionenzyme recognition sites. P/A datasets (SilicoDArT) have missing valuesbecause it was not possible to call whether a sequence tag was amplified ornot. This function tabulates the number of missing values as quantiles.

Usage

gl.report.callrate(  x,  method = "loc",  by_pop = FALSE,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  bins = 50,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

This function expects a genlight object, containing either SNP data orSilicoDArT (=presence/absence data).

Callrate is summarized by locus or by individual to allow sensible decisionson thresholds for filtering taking into consideration consequential loss ofdata. The summary is in the form of a tabulation and plots.

Plot themes can be obtained from:

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Resultant ggplots and the tabulation are saved to the session's temporarydirectory.

Value

Returns unaltered genlight object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.filter.callrate

Other report functions:gl.report.bases(),gl.report.diversity(),gl.report.hamming(),gl.report.hwe(),gl.report.ld.map(),gl.report.locmetric(),gl.report.maf(),gl.report.monomorphs(),gl.report.overshoot(),gl.report.pa(),gl.report.parent.offspring(),gl.report.rdepth(),gl.report.replicates(),gl.report.reproducibility(),gl.report.secondaries(),gl.report.sexlinked(),gl.report.taglength()

Examples

 # SNP data  test.gl <- testset.gl[1:20,]  gl.report.callrate(test.gl)  gl.report.callrate(test.gl,method='ind')# Tag P/A data  test.gs <- testset.gs[1:20,]  gl.report.callrate(test.gs)  gl.report.callrate(test.gs,method='ind')    test.gl <- testset.gl[1:20,]  gl.report.callrate(test.gl)

Calculates diversity indexes for SNPs

Description

This script takes a genlight object and calculates alpha and beta diversityfor q = 0:2. Formulas are taken from Sherwin et al. 2017. The paper describesnicely the relationship between the different q levels and how they relate topopulation genetic processes such as dispersal and selection.

Usage

gl.report.diversity(  x,  plot.out = TRUE,  pbar = TRUE,  table = "DH",  plot_theme = theme_dartR(),  plot_colors = discrete_palette,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

For all indexes, the entropies (H) and corresponding effectivenumbers, i.e. Hill numbers (D), which reflect the number of needed entitiesto get the observed values, are calculated. In a nutshell, the alpha indexesbetween the different q-values should be similar if there is no deviationfrom expected allele frequencies and occurrences (e.g. all loci in HWE &equilibrium). If there is a deviation of an index, this links to a processcausing it, such as dispersal, selection or strong drift. For a detailedexplanation of all the indexes, we recommend resorting to the literatureprovided below. Confidence intervals are +/- 1 standard deviation.

Function's output

If the function's parameter "table" = "DH" (the default value) is used, the output of the function is 20 tables.

The first two show the number of loci used. The name of each of the rest of the tables starts with three terms separated by underscores.

The first term refers to the q value (0 to 2).

The second term refers to whether it is the diversity measure (H) or its transformation to Hill numbers (D).

The third term refers to whether the diversity is calculated within populations (alpha) or between populations (beta).

In the case of alpha diversity tables, standard deviations have their own table, which finishes with a fourth term: "sd".

In the case of beta diversity tables, standard deviations are in the upper triangle of the matrix and diversity values are in the lower triangle of the matrix.

Plots are saved to the temporal directory (tempdir) and can be accessed withthe functiongl.print.reports and listed with the functiongl.list.reports. Note that they can be accessed only in thecurrent R session because tempdir is cleared each time that the R sessionis closed.

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

A list of entropy indexes for each level of q and equivalent numbersfor alpha and beta diversity.

Author(s)

Bernd Gruber (Post tohttps://groups.google.com/d/forum/dartr),Contributors: William B. Sherwin, Alexander Sentinella

References

Sherwin, W.B., Chao, A., Johst, L., Smouse, P.E. (2017). Information TheoryBroadens the Spectrum of Molecular Ecology and Evolution. TREE 32(12)948-963. doi:10.1016/j.tree.2017.09.12

See Also

Other report functions:gl.report.bases(),gl.report.callrate(),gl.report.hamming(),gl.report.hwe(),gl.report.ld.map(),gl.report.locmetric(),gl.report.maf(),gl.report.monomorphs(),gl.report.overshoot(),gl.report.pa(),gl.report.parent.offspring(),gl.report.rdepth(),gl.report.replicates(),gl.report.reproducibility(),gl.report.secondaries(),gl.report.sexlinked(),gl.report.taglength()

Examples

div <- gl.report.diversity(bandicoot.gl[1:10,1:100], table = FALSE, pbar=FALSE)div$zero_H_alphadiv$two_H_betanames(div)

Reports various statistics of genetic differentiation betweenpopulations with confident intervals

Description

This function calculates four genetic differentiation between populationsstatistics (see the "Details" section for further information).

• Fst - Measure of the degree of genetic differentiation of subpopulations (Nei, 1987).

• Fstp - Unbiased (i.e. corrected for sampling error, see explanation below) Fst (Nei, 1987).

• Dest - Jost’s D (Jost, 2008).

• Gst_H - Gst standardized by the maximum level that it can obtain forthe observed amount of genetic variation (Hedrick 2005).

Sampling errors arise because allele frequencies in our samples differ from those in the subpopulations from which they were taken (Holsinger, 2012).

Confident Intervals are obtained using bootstrapping.

Usage

gl.report.fstat(  x,  nboots = 0,  conf = 0.95,  CI.type = "bca",  ncpus = 1,  plot.stat = "Fstp",  plot.display = TRUE,  palette.divergent = gl.colors("div"),  font.size = 0.5,  plot.dir = NULL,  plot.file = NULL,  verbose = NULL,  ...)

Arguments

Details

Even though Fst and its relatives can predict evolutionary processes(Holsinger & Weir, 2009), they are not true measures of geneticdifferentiation in the sense that they are dependent on the diversitywithin populations (Meirmans & Hedrick, 2011), the number of populationsanalysed (Alcala & Rosenberg, 2017) and are not monotonic(Sherwin et al., 2017). Recent approaches have been developed toaccommodate these mathematical restrictions (G'ST; "Gst_H"; Hedrick, 2005,and Jost's D; "Dest"; Jost, 2008). More recently, novel approaches based oninformation theory (Mutual Information; Sherwin et al., 2017) and allelefrequencies (Allele Frequency Difference; Berner, 2019) have distinctproperties that make them valuable resources to interpret geneticdifferentiation between populations.

Note that each measure of genetic differentiation has advantages anddrawbacks, and the decision of using a particular measure is usuallybased on the research question.

Statistics calculated

The equations used to calculate the statistics are shown below.

• Ho - Unbiased estimate of observed heterozygosity across subpopulations (Nei, 1987, pp. 164, eq. 7.38) is calculated as:

  

  wherePkii represents the proportion of homozygoteii for allelei in individualk ands represents the numberof subpopulations.

• Hs - Unbiased estimate of the expected heterozygosity under Hardy-Weinberg equilibrium across subpopulations (Nei, 1987, pp. 164,eq. 7.39) is calculated as:

  

  whereñ is the harmonic mean ofnk (the number of individuals in each subpopulation),pki is the proportion (sometimes misleadingly called frequency) of allelei in subpopulationk.

• Ht - Heterozygosity for the total population (Nei, 1987, pp. 164,eq. 7.40) is calculated as:

  

• Dst - The average allele frequency differentiation between populations (Nei, 1987, pp. 163) is calculated as:

  

• Htp - Unbiased estimate of Heterozygosity for the total population(Nei, 1987, pp. 165) is calculated as:

  

• Dstp - Unbiased estimate of the average allele frequency differentiation between populations (Nei, 1987, pp. 165)

• Fst - Measure of the extent of genetic differentiation of subpopulations (Nei, 1987, pp. 162, eq. 7.34) is calculated as:

  

• Fstp - Unbiased measure of the extent of genetic differentiation of subpopulations (Nei, 1987, pp. 163, eq. 7.36) is calculated as:

  

• Dest - Jost’s D (Jost, 2008, eq. 12)

• Gst-max - The maximum level that Gst can obtain for the observed amount of genetic variation (Hedrick 2005, eq. 4a) is calculated as:

  

• Gst-H - Gst standardized by the maximum level that it can obtain for the observed amount of genetic variation (Hedrick 2005, eq. 4b) is calculated as:

  

Confident Intervals

The uncertainty of a parameter, in this case the mean of the statistic, canbe summarised by a confidence interval (CI) which includes the true parametervalue with a specified probability (i.e. confidence level; the parameter"conf" in this function).

In this function, CI are obtained using Bootstrap which is an inferencemethod that samples with replacement the data (i.e. loci) and calculates thestatistics every time.

This function uses the functionboot (package boot) to performthe bootstrap replicates and the functionboot.ci(package boot) to perform the calculations for the CI.

Four different types of nonparametric CI can be calculated(parameter "CI.type" in this function):

• First order normal approximation interval ("norm").

• Basic bootstrap interval ("basic").

• Bootstrap percentile interval ("perc").

• Adjusted bootstrap percentile interval ("bca").

The studentized bootstrap interval ("stud") was not included in the CI typesbecause it is computationally intensive, it may produce estimates outsidethe range of plausible values and it has been found to be erratic inpractice, see for example the "Studentized (t) Intervals" section in:

www.r-bloggers.com/2019/09/understanding-bootstrap-confidence-interval-output-from-the-r-boot-package

Nice tutorials about the different types of CI can be found at:

https://www.datacamp.com/tutorial/bootstrap-r

Efron and Tibshirani (1993, p. 162) and Davison and Hinkley(1997, p. 194) suggest that the number of bootstrap replicates shouldbe between 1000 and 2000.

It is important to note that unreliable confident intervals will beobtained if too few number of bootstrap replicates are used.Therefore, the functionboot.ci will throw warnings and errorsif bootstrap replicates are too few. Consider increasing then number ofbootstrap replicates to at least 200.

The "bca" interval is often cited as the best for theoretical reasons,however it may produce unstable results if the bootstrap distributionis skewed or has extreme values. For example, you might get the warning"extreme order statistics used as endpoints" or the error "estimatedadjustment 'a' is NA". In this case, you may want to use more bootstrapreplicates or a different method or check your data for outliers.

The error "estimated adjustment 'w' is infinite" means that the estimatedadjustment ‘w’ for the "bca" interval is infinite, which can happen whenthe empirical influence values are zero or very close to zero. This canbe caused by various reasons, such as:

The number of bootstrap replicates is too small, the statistic of interestis constant or nearly constant across the bootstrap samples, the datacontains outliers or extreme values.

You can try some possible solutions, such as:

Increasing the number of bootstrap replicates, using a different type ofbootstrap confidence interval or removing or transforming the outliers orextreme values.

Plotting

The plot can be customised by including any parameter(s) from the functionheatmap.2 (package gplots).

For the color palette you could try for example:

>library(viridis)

>res <- gl.report.fstat(platypus.gl, palette.divergent = viridis)

If a plot.file is given, the plot arising from this function is saved as an"RDS" binary file using the functionsaveRDS (package base);can be reloaded with functionreadRDS (package base). A filename must be specified for the plot to be saved.

If a plot directory (plot.dir) is specified, the gplot binary is saved tothat directory; otherwise to the tempdir().

Your plot might not shown in full because your 'Plots' pane is too small(in RStudio).Increase the size of the 'Plots' pane before running the function.Alternatively, use the parameter 'plot.file' to save the plot to a file.

Parallelisation

If the parameter ncpus > 1, parallelisation is enabled. In Windows, parallelcomputing employs a "socket" approach that starts new copies of R on eachcore. POSIX systems, on the other hand (Mac, Linux, Unix, and BSD),utilise a "forking" approach that replicates the whole current version ofR and transfers it to a new core.

Opening and terminating R sessions in each core involves a significantamount of processing time, therefore parallelisation in Windows machinesis only quicker than not usung parallelisation when nboots > 1000-2000.

Value

Two lists, the first list contains matrices with genetic statisticstaken pairwise by population, the second list contains tables with thegenetic statistics for each pair of populations. If nboots > 0, tables withthe four statistics calculated with Low Confidence Intervals (LCI) and HighConfidence Intervals (HCI).

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

References

• Alcala, N., & Rosenberg, N. A. (2017). Mathematical constraints on FST:Biallelic markers in arbitrarily many populations. Genetics (206), 1581-1600.

• Berner, D. (2019). Allele frequency difference AFD–an intuitive alternativeto FST for quantifying genetic population differentiation. Genes, 10(4), 308.

• Davison AC, Hinkley DV (1997). Bootstrap Methods and their Application.Cambridge University Press: Cambridge.

• Efron, B. (1979). Bootstrap methods: Another look at the jackknife. Annals ofStatistics 7, 1–26.

• Efron B, Tibshirani RJ (1993). An Introduction to the Bootstrap. Chapman andHall: London.

• Hedrick, P. W. (2005). A standardized genetic differentiation measure.Evolution, 59(8), 1633-1638.

• Holsinger, K. E. (2012). Lecture notes in population genetics.

• Holsinger, K. E., & Weir, B. S. (2009). Genetics in geographically structuredpopulations: defining, estimating and interpreting FST. Nature ReviewsGenetics, 10(9), 639- 650.

• Jost, L. (2008). GST and its relatives do not measure differentiation.Molecular Ecology, 17(18), 4015-4026.

• Meirmans, P. G., & Hedrick, P. W. (2011). Assessing population structure:FST and related measures. Molecular Ecology Resources, 11(1), 5-18.

• Nei, M. (1987). Molecular evolutionary genetics: Columbia University Press.

• Sherwin, W. B., Chao, A., Jost, L., & Smouse, P. E. (2017). Informationtheory broadens the spectrum of molecular ecology and evolution. Trends inEcology & Evolution, 32(12), 948-963.

Examples

res <- gl.report.fstat(platypus.gl)

Calculates the pairwise Hamming distance between DArT trimmed DNAsequences

Description

Hamming distance is calculated as the number of base differencesbetween two sequences which can be expressed as a count or a proportion.Typically, it is calculated between two sequences of equal length. In thecontext of DArT trimmed sequences, which differ in length but which areanchored to the left by the restriction enzyme recognition sequence, it issensible to compare the two trimmed sequences starting from immediately afterthe common recognition sequence and terminating at the last base of theshorter sequence.

Usage

gl.report.hamming(  x,  rs = 5,  threshold = 3,  taglength = 69,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  probar = FALSE,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The functiongl.filter.hamming will filter out one oftwo loci if their Hamming distance is less than a specified percentage

Hamming distance can be computed by exploiting the fact that the dot productof two binary vectors x and (1-y) counts the corresponding elements that aredifferent between x and y. This approach can also be used for vectors thatcontain more than two possible values at each position (e.g. A, C, T or G).

If a pair of DNA sequences are of differing length, the longer is truncated.

The algorithm is that of Johann de Jonghttps://johanndejong.wordpress.com/2015/10/02/faster-hamming-distance-in-r-2/as implemented inutils.hamming

Plots and table are saved to the session's temporary directory (tempdir)

Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

Returns unaltered genlight object

Author(s)

Custodian: Arthur Georges – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.filter.hamming

Other report functions:gl.report.bases(),gl.report.callrate(),gl.report.diversity(),gl.report.hwe(),gl.report.ld.map(),gl.report.locmetric(),gl.report.maf(),gl.report.monomorphs(),gl.report.overshoot(),gl.report.pa(),gl.report.parent.offspring(),gl.report.rdepth(),gl.report.replicates(),gl.report.reproducibility(),gl.report.secondaries(),gl.report.sexlinked(),gl.report.taglength()

Examples

 gl.report.hamming(testset.gl[,1:100])gl.report.hamming(testset.gs[,1:100])#' # SNP datatest <- platypus.gltest <- gl.subsample.loci(platypus.gl,n=50)result <- gl.filter.hamming(test, threshold=0.25, verbose=3)

Reports observed, expected and unbiased heterozygosities and FIS(inbreeding coefficient) by population or by individual from SNP data

Description

Calculates the observed, expected and unbiased expected (i.e.corrected for sample size) heterozygosities and FIS (inbreeding coefficient)for each population or the observed heterozygosity for each individual in agenlight object.

Usage

gl.report.heterozygosity(  x,  method = "pop",  n.invariant = 0,  nboots = 0,  conf = 0.95,  CI.type = "bca",  ncpus = 1,  plot.display = TRUE,  plot.theme = theme_dartR(),  plot.colors.pop = gl.colors("dis"),  plot.colors.ind = gl.colors(2),  error.bar = "SD",  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

Observed heterozygosity for a population takes the proportion ofheterozygous loci for each individual then averages over the individuals inthat population. The calculations take into account missing values.Expected heterozygosity for a population takes the expected proportion ofheterozygotes, that is, expected under Hardy-Weinberg equilibrium, for eachlocus, then averages this across the loci for an average estimate for thepopulation.

Expected heterozygosity is calculated using the correction for sample sizefollowing equation 2 from Nei 1978.

Observed heterozygosity for individuals is calculated as the proportion ofloci that are heterozygous for that individual.

Finally, the loci that are invariant across all individuals in the dataset(that is, across populations), is typically unknown. This can renderestimates of heterozygosity analysis specific, and so it is not valid tocompare such estimates across species or even across different analyses. Thisis a similar problem faced by microsatellites. If you have an estimate of thenumber of invariant sequence tags (loci) in your data, such as provided bygl.report.secondaries, you can specify it with the n.invariantparameter to standardize your estimates of heterozygosity.

NOTE: It is important to realise that estimation of adjustedheterozygosity requires that secondaries not to be removed.

Heterozygosities and FIS (inbreeding coefficient) are calculated by locuswithin each population using the following equations:

• Observed heterozygosity (Ho) = number of homozygotes / n_Ind,where n_Ind is the number of individuals without missing data.

• Observed heterozygosity adjusted (Ho.adj) <- Ho * n_Loc /(n_Loc + n.invariant),where n_Loc is the number of loci that do not have all missing data andn.invariant is an estimate of the number of invariant loci to adjustheterozygosity.

• Expected heterozygosity (He) = 1 - (p^2 + q^2),where p is the frequency of the reference allele and q is the frequency ofthe alternative allele.

• Expected heterozygosity adjusted (He.adj) = He * n_Loc /(n_Loc + n.invariant)

• Unbiased expected heterozygosity (uHe) = He * (2 * n_Ind /(2 * n_Ind - 1))

• Inbreeding coefficient (FIS) = 1 - (mean(Ho) / mean(uHe))

Function's outputOutput for method='pop' is an ordered barchart of observed heterozygosity,unbiased expected heterozygosity and FIS (Inbreeding coefficient) across populationstogether with a table of mean observed and expected heterozygosities and FISby population and their respective standard deviations (SD).In the output, it is also reported by population: the number of loci used toestimate heterozygosity(n.Loc), the number of polymorphic loci (polyLoc),the number of monomorphic loci (monoLoc) and loci with all missing data(all_NALoc).Output for method='ind' is a histogram and a boxplot of heterozygosity acrossindividuals.Plots and table are saved to the session temporary directory (tempdir)Examples of other themes that can be used can be consulted in

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Error bars

The best method for presenting or assessing genetic statistics depends on the type of data you have and the specific questions you're trying to answer. Here's a brief overview of when you might use each method:

1. Confidence Intervals ("CI"):

- Usage: Often used to convey the precision of an estimate.

- Advantage: Confidence intervals give a range in which the true parameter (like a population mean) is likely to fall, given the data and a specified probability (like 95

- In Context: For genetic statistics, if you're estimating a parameter,a 95lies.

2. Standard Deviation ("SD"):

- Usage: Describes the amount of variation from the average in a set of data.

- Advantage: Allows for an understanding of the spread of individual datapoints around the mean.

- In Context: If you're looking at the distribution of a quantitative trait (like height) in a population with a particular genotype, the SD can describe how much individual heights vary around the average height.

3. Standard Error ("SE"):

- Usage: Describes the precision of the sample mean as an estimate of the population mean.

- Advantage: Smaller than the SD in large samples; it takes into account both the SD and the sample size.

- In Context: If you want to know how accurately your sample mean representsthe population mean, you'd look at the SE.

Recommendation:

- If you're trying to convey the precision of an estimate, confidence intervals are very useful.

- For understanding variability within a sample, standard deviation is key.

- To see how well a sample mean might estimate a population mean, consider the standard error.

In practice, geneticists often use a combination of these methods to analyze and present their data, depending on their research questions and the nature of the data.

Confident Intervals

The uncertainty of a parameter, in this case the mean of the statistic, canbe summarised by a confidence interval (CI) which includes the true parametervalue with a specified probability (i.e. confidence level; the parameter"conf" in this function).

In this function, CI are obtained using Bootstrap which is an inferencemethod that samples with replacement the data (i.e. loci) and calculates thestatistics every time.

This function uses the functionboot (package boot) to performthe bootstrap replicates and the functionboot.ci(package boot) to perform the calculations for the CI.

Four different types of nonparametric CI can be calculated(parameter "CI.type" in this function):

• First order normal approximation interval ("norm").

• Basic bootstrap interval ("basic").

• Bootstrap percentile interval ("perc").

• Adjusted bootstrap percentile interval ("bca").

The studentized bootstrap interval ("stud") was not included in the CI typesbecause it is computationally intensive, it may produce estimates outsidethe range of plausible values and it has been found to be erratic inpractice, see for example the "Studentized (t) Intervals" section in:

www.r-bloggers.com/2019/09/understanding-bootstrap-confidence-interval-output-from-the-r-boot-packageEfron and Tibshirani (1993, p. 162) and Davison and Hinkley(1997, p. 194) suggest that the number of bootstrap replicates shouldbe between 1000 and 2000.

It is important to note that unreliable confident intervals will beobtained if too few number of bootstrap replicates are used.Therefore, the functionboot.ci will throw warnings and errorsif bootstrap replicates are too few. Consider increasing then number ofbootstrap replicates to at least 200.

The "bca" interval is often cited as the best for theoretical reasons,however it may produce unstable results if the bootstrap distributionis skewed or has extreme values. For example, you might get the warning"extreme order statistics used as endpoints" or the error "estimatedadjustment 'a' is NA". In this case, you may want to use more bootstrapreplicates or a different method or check your data for outliers.

The error "estimated adjustment 'w' is infinite" means that the estimatedadjustment ‘w’ for the "bca" interval is infinite, which can happen whenthe empirical influence values are zero or very close to zero. This canbe caused by various reasons, such as:

The number of bootstrap replicates is too small, the statistic of interestis constant or nearly constant across the bootstrap samples, the datacontains outliers or extreme values.

You can try some possible solutions, such as:

Increasing the number of bootstrap replicates, using a different type ofbootstrap confidence interval or removing or transforming the outliers orextreme values.

Parallelisation

If the parameter ncpus > 1, parallelisation is enabled. In Windows, parallelcomputing employs a "socket" approach that starts new copies of R on eachcore. POSIX systems, on the other hand (Mac, Linux, Unix, and BSD),utilise a "forking" approach that replicates the whole current version ofR and transfers it to a new core.

Opening and terminating R sessions in each core involves a significantamount of processing time, therefore parallelisation in Windows machinesis only quicker than not usung parallelisation when nboots > 1000-2000.

Value

A dataframe containing population labels, heterozygosities, FIS,their standard deviations and sample sizes

Author(s)

Custodian: Luis Mijangos (Post tohttps://groups.google.com/d/forum/dartr)

References

Nei, M. (1978). Estimation of average heterozygosity and genetic distancefrom a small number of individuals. Genetics, 89(3), 583-590.

See Also

gl.filter.heterozygosity

Other unmatched report:gl.allele.freq()

Examples

 require("dartR.data")df <- gl.report.heterozygosity(platypus.gl)df <- gl.report.heterozygosity(platypus.gl,method='ind')n.inv <- gl.report.secondaries(platypus.gl)gl.report.heterozygosity(platypus.gl, n.invariant = n.inv[7, 2])df <- gl.report.heterozygosity(platypus.gl)

Reports departure from Hardy-Weinberg proportions

Description

Calculates the probabilities of agreement with H-W proportions based on observedfrequencies of reference homozygotes, heterozygotes and alternate homozygotes.

Usage

gl.report.hwe(  x,  subset = "each",  method_sig = "Exact",  multi_comp = FALSE,  multi_comp_method = "BY",  alpha_val = 0.05,  pvalue_type = "midp",  cc_val = 0.5,  sig_only = TRUE,  min_sample_size = 5,  plot.out = TRUE,  plot_colors = two_colors_contrast,  max_plots = 4,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

There are several factors that can cause deviations from Hardy-Weinbergproportions including: mutation, finite population size, selection,population structure, age structure, assortative mating, sex linkage,nonrandom sampling and genotyping errors. Therefore, testing forHardy-Weinberg proportions should be a process that involves a carefulevaluation of the results, a good place to start is Waples (2015).

Note that tests for H-W proportions are only valid if there is no populationsubstructure (assuming random mating) and have sufficient power only whenthere is sufficient sample size (n individuals > 15).

Populations can be defined in three ways:

• Merging all populations in the dataset using subset = 'all'.

• Within each population separately using: subset = 'each'.

• Within selected populations using for example: subset =c('pop1','pop2').

Two different statistical methods to test for deviations from Hardy Weinbergproportions:

• The classical chi-square test (method_sig='ChiSquare') based on thefunctionHWChisq of the R package HardyWeinberg.By default a continuity correction is applied (cc_val=0.5). Thecontinuity correction can be turned off (by specifying cc_val=0), for examplein cases of extreme allele frequencies in which the continuity correction canlead to excessive type 1 error rates.

• The exact test (method_sig='Exact') based on the exact calculationscontained in the functionHWExactStats of the Rpackage HardyWeinberg, and described in Wigginton et al. (2005). The exacttest is recommended in most cases (Wigginton et al., 2005).Three different methods to estimate p-values (pvalue_type) in the Exact testcan be used:

  • 'dost' p-value is computed as twice the tail area of a one-sided test.

  • 'selome' p-value is computed as the sum of the probabilities of allsamples less or equally likely as the current sample.

  • 'midp', p-value is computed as half the probability of the currentsample + the probabilities of all samples that are more extreme.

  The standard exact p-value is overly conservative, in particularfor small minor allele frequencies. The mid p-value ameliorates this problemby bringing the rejection rate closer to the nominal level, at the price ofoccasionally exceeding the nominal level (Graffelman & Moreno, 2013).

Correction for multiple tests can be applied using the following methodsbased on the functionp.adjust:

• 'holm' is also known as the sequential Bonferroni technique (Rice,1989). This method has a greater statistical power than the standardBonferroni test, however this method becomes very stringent when many testsare performed and many real deviations from the null hypothesis can goundetected (Waples, 2015).

• 'hochberg' based on Hochberg, 1988.

• 'hommel' based on Hommel, 1988. This method is more powerful thanHochberg's, but the difference is usually small.

• 'bonferroni' in which p-values are multiplied by the number of tests.This method is very stringent and therefore has reduced power to detectmultiple departures from the null hypothesis.

• 'BH' based on Benjamini & Hochberg, 1995.

• 'BY' based on Benjamini & Yekutieli, 2001.

The first four methods are designed to give strong control of the family-wiseerror rate. The last two methods control the false discovery rate (FDR),the expected proportion of false discoveries among the rejected hypotheses.The false discovery rate is a less stringent condition than the family-wiseerror rate, so these methods are more powerful than the others, especiallywhen number of tests is large.The number of tests on which the adjustment for multiple comparisons isthe number of populations times the number of loci.

Ternary plots

Ternary plots can be used to visualise patterns of H-W proportions (plot.out= TRUE). P-values and the statistical (non)significance of a large number ofbi-allelic markers can be inferred from their position in a ternary plot.See Graffelman & Morales-Camarena (2008) for further details. Ternary plotsare based on the functionHWTernaryPlot fromthe package HardyWeinberg. Each vertex of the Ternary plot represents one of the three possible genotypes for SNP data: homozygous for the reference allele (AA), heterozygous (AB) and homozygous for the alternative allele(BB). Loci deviating significantly from Hardy-Weinberg proportions after correction for multiple tests are shown in pink. The blue parabola represents Hardy-Weinberg equilibrium, and the area between green lines represents the acceptance region.

For these plots to work it is necessary to install the package ggtern.

Value

A dataframe containing loci, counts of reference SNP homozygotes,heterozygotes and alternate SNP homozygotes; probability of departure fromH-W proportions, per locus significance with and without correction formultiple comparisons and the number of population where the same locus is significantly out of HWE.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

References

• Benjamini, Y., and Yekutieli, D. (2001). The control of the falsediscovery rate in multiple testing under dependency. Annals of Statistics,29, 1165–1188.

• Graffelman, J. (2015). Exploring Diallelic Genetic Markers: The HardyWeinberg Package. Journal of Statistical Software 64:1-23.

• Graffelman, J. & Morales-Camarena, J. (2008). Graphical tests forHardy-Weinberg equilibrium based on the ternary plot. Human Heredity65:77-84.

• Graffelman, J., & Moreno, V. (2013). The mid p-value in exact tests forHardy-Weinberg equilibrium. Statistical applications in genetics andmolecular biology, 12(4), 433-448.

• Hochberg, Y. (1988). A sharper Bonferroni procedure for multiple testsof significance. Biometrika, 75, 800–803.

• Hommel, G. (1988). A stagewise rejective multiple test procedure basedon a modified Bonferroni test. Biometrika, 75, 383–386.

• Rice, W. R. (1989). Analyzing tables of statistical tests. Evolution,43(1), 223-225.

• Waples, R. S. (2015). Testing for Hardy–Weinberg proportions: have welost the plot?. Journal of heredity, 106(1), 1-19.

• Wigginton, J.E., Cutler, D.J., & Abecasis, G.R. (2005). A Note on ExactTests of Hardy-Weinberg Equilibrium. American Journal of Human Genetics76:887-893.

See Also

gl.filter.hwe

Other report functions:gl.report.bases(),gl.report.callrate(),gl.report.diversity(),gl.report.hamming(),gl.report.ld.map(),gl.report.locmetric(),gl.report.maf(),gl.report.monomorphs(),gl.report.overshoot(),gl.report.pa(),gl.report.parent.offspring(),gl.report.rdepth(),gl.report.replicates(),gl.report.reproducibility(),gl.report.secondaries(),gl.report.sexlinked(),gl.report.taglength()

Calculates pairwise linkage disequilibrium by population

Description

This function calculates pairwise linkage disequilibrium (LD) by population using the functionld (package snpStats).

If SNPs are not mapped to a reference genome, the parameterld_max_pairwiseshould be set as NULL (the default). In this case, the function will assign the same chromosome ("1") to all the SNPs in the datasetand assign a sequence from 1 to n loci as the position of each SNP. The function will then calculate LD for all possible SNP pair combinations.

If SNPs are mapped to a reference genome, the parameterld_max_pairwiseshould be filled out (i.e. not NULL). In this case, theinformation for SNP's position should be stored in the genlight accessor"@position" and the SNP's chromosome name in the accessor "@chromosome"(see examples). The function will then calculate LD within each chromosomeand for all possible SNP pair combinations within a distance ofld_max_pairwise.

Usage

gl.report.ld.map(  x,  ld_max_pairwise = NULL,  maf = 0.05,  ld_stat = "R.squared",  ind.limit = 10,  stat_keep = "AvgPIC",  ld_threshold_pops = 0.2,  plot.out = TRUE,  plot_theme = NULL,  histogram_colors = NULL,  boxplot_colors = NULL,  bins = 50,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

This function reports LD between SNP pairs by population. The functiongl.filter.ld filters out the SNPs in LD using asinput the results ofgl.report.ld.map. The actual number of SNPs to be filtered out depends on the parameters set in the functiongl.filter.ld.

Boxplots of LD by population anda histogram showing LD frequency are presented.

Value

A dataframe with information for each SNP pair in LD.

Author(s)

Custodian: Luis Mijangos – Post tohttps://groups.google.com/d/forum/dartr

See Also

gl.filter.ld

Other report functions:gl.report.bases(),gl.report.callrate(),gl.report.diversity(),gl.report.hamming(),gl.report.hwe(),gl.report.locmetric(),gl.report.maf(),gl.report.monomorphs(),gl.report.overshoot(),gl.report.pa(),gl.report.parent.offspring(),gl.report.rdepth(),gl.report.replicates(),gl.report.reproducibility(),gl.report.secondaries(),gl.report.sexlinked(),gl.report.taglength()

Examples

require("dartR.data")x <- platypus.glx <- gl.filter.callrate(x,threshold = 1)x <- gl.filter.monomorphs(x)x$position <- x$other$loc.metrics$ChromPos_Platypus_Chrom_NCBIv1x$chromosome <- as.factor(x$other$loc.metrics$Chrom_Platypus_Chrom_NCBIv1)ld_res <- gl.report.ld.map(x,ld_max_pairwise = 10000000)

Reports summary of the slot $other$loc.metrics

Description

This script uses any field with numeric values stored in $other$loc.metricsto produce summary statistics (mean, minimum, average, quantiles), histogramsand boxplots to assist the decision of choosing thresholds for the filterfunctiongl.filter.locmetric.

Usage

gl.report.locmetric(  x,  metric,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors = two_colors,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The functiongl.filter.locmetric will filter out theloci with a locmetric value below a specified threshold.

The fields that are included in dartR, and a short description, are foundbelow. Optionally, the user can also set his/her own field by adding a vectorinto $other$loc.metrics as shown in the example. You can check the names ofall available loc.metrics via: names(gl$other$loc.metrics).

• SnpPosition - position (zero is position 1) in the sequence tag of thedefined SNP variant base.

• CallRate - proportion of samples for which the genotype call isnon-missing (that is, not '-' ).

• OneRatioRef - proportion of samples for which the genotype score is 0.

• OneRatioSnp - proportion of samples for which the genotype score is 2.

• FreqHomRef - proportion of samples homozygous for the Reference allele.

• FreqHomSnp - proportion of samples homozygous for the Alternate (SNP)allele.

• FreqHets - proportion of samples which score as heterozygous, that is,scored as 1.

• PICRef - polymorphism information content (PIC) for the Reference allele.

• PICSnp - polymorphism information content (PIC) for the SNP.

• AvgPIC - average of the polymorphism information content (PIC) of thereference and SNP alleles.

• AvgCountRef - sum of the tag read counts for all samples, divided by thenumber of samples with non-zero tag read counts, for the Reference allele row.

• AvgCountSnp - sum of the tag read counts for all samples, divided by thenumber of samples with non-zero tag read counts, for the Alternate (SNP) allelerow.

• RepAvg - proportion of technical replicate assay pairs for which themarker score is consistent.

• rdepth - read depth.

Function's output

The minimum, maximum, mean and a tabulation of quantiles of the locmetricvalues against thresholds rate are provided. Output also includes a boxplotand a histogram.

Quantiles are partitions of a finite set of values into q subsets of (nearly)equal sizes. In this function q = 20. Quantiles are useful measures becausethey are less susceptible to long-tailed distributions and outliers.

Plots and table were saved to the temporal directory (tempdir) and can beaccessed with the functiongl.print.reports and listed withthe functiongl.list.reports. Note that they can be accessedonly in the current R session because tempdir is cleared each time that theR session is closed.

Examples of other themes that can be used can be consulted in:

• https://ggplot2.tidyverse.org/reference/ggtheme.html and

• https://yutannihilation.github.io/allYourFigureAreBelongToUs/ggthemes/

Value

An unaltered genlight object.

Author(s)

Luis Mijangos (Post tohttps://groups.google.com/d/forum/dartr)

See Also

gl.filter.locmetric,gl.list.reports,gl.print.reports

Other report functions:gl.report.bases(),gl.report.callrate(),gl.report.diversity(),gl.report.hamming(),gl.report.hwe(),gl.report.ld.map(),gl.report.maf(),gl.report.monomorphs(),gl.report.overshoot(),gl.report.pa(),gl.report.parent.offspring(),gl.report.rdepth(),gl.report.replicates(),gl.report.reproducibility(),gl.report.secondaries(),gl.report.sexlinked(),gl.report.taglength()

Examples

# adding dummy datatest <- testset.gltest$other$loc.metrics$test <- 1:nLoc(test)# SNP dataout <- gl.report.locmetric(test,metric='test')# adding dummy datatest.gs <- testset.gstest.gs$other$loc.metrics$test <- 1:nLoc(test.gs)# Tag P/A dataout <- gl.report.locmetric(test.gs,metric='test')

Reports minor allele frequency (MAF) for each locus in a SNP dataset

Description

This script provides summary histograms of MAF for eachpopulation in the dataset and an overall histogram to assist the decision ofchoosing thresholds for the filter functiongl.filter.maf

Usage

gl.report.maf(  x,  maf.limit = 0.5,  ind.limit = 5,  plot.out = TRUE,  plot_theme = theme_dartR(),  plot_colors_pop = discrete_palette,  plot_colors_all = two_colors,  bins = 25,  save2tmp = FALSE,  verbose = NULL)

Arguments

Details

The functiongl.filter.maf will filter out theloci with MAF below a specified threshold.

Function's output