adegenetTutorial(which="name-below"):
- basics: introduction to the package.
- spca: multivariate analysis of spatial genetic patterns.
- dapc: population structure and group assignment using DAPC.
- genomics: introduction to the class vignette("name-below", package="adegenet"):
- adegenet-basics.
- adegenet-spca.
- adegenet-dapc.
- adegenet-genomics.
Important functions are also summarized below.
=== IMPORTING DATA ===
= TO GENIND OBJECTS =
adegenet imports data to 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.
In addition, it can extract polymorphic sites from nucleotide and
amino-acid alignments:
- DNA files: use read.dna from the ape package,
and then extract SNPs from DNA alignments using
DNAbin2genind.
- protein sequences alignments: polymorphic sites can be extracted from
protein sequences alignments in alignment format (package
seqinr, see as.alignment) using the
function alignment2genind.
The function fasta2DNAbin allows for reading fasta
files into DNAbin object with minimum RAM requirements.
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.
= TO GENLIGHT OBJECTS =
SNP data can be read from the following formats:
- PLINK: see function read.PLINK
- .snp (adegenet's own format): see function read.snp
SNP can also be extracted from aligned DNA sequences with the fasta
format, using fasta2genlight
=== EXPORTING DATA ===
adegenet exports data from genind2genotype
- the hierfstat package: see genind2hierfstat
Genotypes can also be recoded from a genind2df.
Also note that the pegas package imports as.loci.
=== MANIPULATING DATA ===
Several functions allow one to manipulate genind2genpop: convert a seploc: creates one object per marker; for
seppop: creates one object per population
- na.replace: replaces missing data (NA) in an
approriate way
- truenames: restores true names of an object
(makefreq: returns a table of allelic frequencies from
a repool merges genoptypes from different
gene pools into one single 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
- pairwise.fst: computes simple pairwise Fst between populations
- 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.
- spca: implements Jombart et al. (2008) 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
Hs: computes the average expected heterozygosity by
population in a find.clusters and dapc: implement the
Discriminant Analysis of Principal Component (DAPC, Jombart et al.,
2010).
- seqTrack: implements the SeqTrack algorithm for
recontructing transmission trees of pathogens (Jombart et al.,
2010) .
glPca: implements PCA for gengraph: implements some simple graph-based
clustering using genetic data.
- snpposi.plot and snpposi.test:
visualize the distribution of SNPs on a genetic sequence and test
their randomness.
- adegenetServer: opens up a web interface for some
functionalities of the package (DAPC with cross validation and
feature selection).
=== GRAPHICS ===
- colorplot: plots points with associated values for up
to three variables represented by colors using the RGB system;
useful for spatial mapping of principal components.
- loadingplot: plots loadings of variables. Useful for
representing the contribution of alleles to a given principal
component in a multivariate method.
- scatter.dapc: scatterplots for DAPC results.
- compoplot: plots membership probabilities from a DAPC
object.
=== SIMULATING DATA ===
- hybridize: implements hybridization between two populations.
- haploGen: simulates genealogies of haplotypes,
storing full genomes.
- glSim: simulates simple H3N2: Seasonal influenza (H3N2) HA segment data.
- dapcIllus: Simulated data illustrating the DAPC.
- eHGDP: Extended HGDP-CEPH dataset.
- microbov: Microsatellites genotypes of 15 cattle breeds.
- nancycats: Microsatellites genotypes of 237 cats from 17 colonies of Nancy (France).
- rupica: Microsatellites genotypes of 335 chamois
(Rupicapra rupicapra) from the Bauges mountains (France).
- sim2pop: Simulated genotypes of two georeferenced populations.
- spcaIllus: Simulated data illustrating the sPCA.
For more information, visit the adegenet website using the function
adegenetWeb.
Tutorials are available via the command adegenetTutorials.
To cite adegenet, please use the reference given by
citation("adegenet") (or see reference below).
Jombart T, Devillard S and Balloux F (2010) Discriminant analysis of
principal components: a new method for the analysis of genetically
structured populations. BMC Genetics 11:94.
doi:10.1186/1471-2156-11-94
Jombart T, Eggo R, Dodd P, Balloux F (2010) Reconstructing disease
outbreaks from genetic data: a graph approach. Heredity. doi:
10.1038/hdy.2010.78.
Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. (2008) Revealing
cryptic spatial patterns in genetic variability by a new multivariate
method. Heredity, 101, 92--103.
See adegenet website:
ade4 for multivariate analysis
- pegas for population genetics tools
- ape for phylogenetics and DNA data handling
- seqinr for handling nucleic and proteic sequences
- shiny for R-based web interfaces