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 fromGenotypes 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
-
- tab: access the allele data (counts or frequencies) 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
- 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 references 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