adegenet (version 1.2-3)

adegenet-package: The adegenet package

Description

This package is devoted to the multivariate analysis of genetic markers data. These data can be codominant markers (e.g. microsatellites) or presence/absence data (e.g. AFLP), and have any level of ploidy. 'adegenet' defines two formal (S4) classes: - genind: a class for data of individuals ("genind" stands for genotypes-individuals). - genpop: a class for data of groups of individuals ("genpop" stands for genotypes-populations) For more information about these classes, type "class ? genind" or "class ? genpop". Both types of objects store information from molecular markers in a matrix ($tab slot), that can be directly analyzed using multivariate methods such as Principal Component Analysis, Correspondance Analysis, etc. See the "dudi.[...]" methods in the ade4 package. Moreover, this package offers methods for manipulating and analyzing information coming from genetic markers (see below). === IMPORTING DATA === adegenet imports data to genind object from the following softwares: - STRUCTURE: see read.structure - GENETIX: see read.genetix - FSTAT: see read.fstat - Genepop: see read.genepop To import data from any of these formats, you can also use the general function import2genind. It is also possible to read genotypes coded by character strings from a data.frame in which genotypes are in rows, markers in columns. For this, use df2genind. Note that df2genind can be used for any level of ploidy. === EXPORTING DATA === adegenet exports data from genind object to formats recognized by other R packages: - the genetics package: see genind2genotype - the hierfstat package: see genind2hierfstat Genotypes can also be recoded from a genind object into a data.frame of character strings, using any separator between alleles. This covers formats from many softwares like GENETIX or STRUCTURE. For this, see genind2df. === MANIPULATING DATA === Several functions allow one to manipulate genind or genpop objects - genind2genpop: convert a genind object to a genpop - seploc: creates one object per marker - seppop: creates one object per population - na.replace: replaces missing data (NA) in an approriate way - truenames: restores true names of an object (genind and genpop use generic labels) - x[i,j]: create a new object keeping only genotypes (or populations) indexed by 'i' and the alleles indexed by 'j'. - makefreq: returns a table of allelic frequencies from a genpop object. - repool merges genoptypes from different genetic pools into one single genind object. - propTyped returns the proportion of available (typed) data, by individual, population, and/or locus. - selPopSize subsets data, retaining only genotypes from a population whose sample size is above a given level. - pop sets the population of a set of genotypes. === ANALYZING DATA === Several functions allow to use usual, and less usual analyses: - HWE.test.genind: performs HWE test for all populations and loci combinations - gstat.randtest: performs a Monte Carlo test of Goudet's G statistic, measuring population structure (based on g.stat.glob package hierfstat). - dist.genpop: computes 5 genetic distances among populations. - monmonier: implementation of the Monmonier algorithm, used to seek genetic boundaries among individuals or populations. Optimized boundaries can be obtained using optimize.monmonier. Object of the class monmonier can be plotted and printed using the corresponding methods. - hybridize: implements hybridization between two populations. - spca: implements Jombart et al. (in revision) spatial Principal Component Analysis - global.rtest: implements Jombart et al. (2008) test for global spatial structures - local.rtest: implements Jombart et al. (2008) test for local spatial structures - propShared: computes the proportion of shared alleles in a set of genotypes (i.e. from a genind object) - propTyped: function to investigate missing data in several ways - scaleGen: generic method to scale genind or genpop before a principal component analysis Adegenet also proposes several real or simulated datasets. For more information, visit the adegenet website by typing adegenetWeb(). To cite adegenet, please use the reference given by citation("adegenet") (or see reference below).

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UTF-8

Details

ll{ Package: adegenet Type: Package Version: 1.2-3 Date: 2009-04-01 License: GPL (>=2) }

References

Jombart T. (2008) adegenet: a R package for the multivariate analysis of genetic markers Bioinformatics 24: 1403-1405. doi: 10.1093/bioinformatics/btn129 See adegenet website: http://adegenet.r-forge.r-project.org/ Please post your questions on 'the adegenet forum': [e-mail]

See Also

The ade4 package for multivariate analysis