Exploratory Analysis of Genetic and Genomic Data
Accessors for adegenet objects
The adegenet package
Compute Akaike Information Criterion (AIC) for snapclust
Compute Akaike Information Criterion for small samples (AICc) for snap...
Converting genind/genpop objects to other classes
Conversion to class "genlight"
Conversion to class "SNPbin"
Compute and optimize a-score for Discriminant Analysis of Principal Co...
Auxiliary functions for adegenet
Compute Bayesian Information Criterion (BIC) for snapclust
Function to choose a connection network
Represents a cloud of points with colors
Genotype composition plot
Returns original points in results paths of an object of class 'monmon...
Discriminant Analysis of Principal Components (DAPC)
Graphics for Discriminant Analysis of Principal Components (DAPC)
Convert a data.frame of allele data to a genind object.
Genetic distances between populations
Internal C routines
Export analysis for mvmapper visualisation
Read large DNA alignments into R
Extract Single Nucleotide Polymorphism (SNPs) from alignments
find.cluster: cluster identification using successive K-means
Genetic transitive graphs
adegenet formal class (S4) for individual genotypes
Convert a genind object to a data.frame.
Conversion from a genind to a genpop object
Formal class "genlight"
adegenet formal class (S4) for allele counts in populations
Auxiliary functions for genlight objects
Principal Component Analysis for genlight objects
Plotting genlight objects
Simulation of simple genlight objects
Simulation of genealogies of haplotypes
Access and manipulate the population hierarchy for genind or genlight ...
Expected heterozygosity (Hs)
Test differences in expected heterozygosity (Hs)
Hardy-Weinberg Equilibrium test for multilocus data
Function hybridize takes two genind in inputs and generates hybrids in...
Importing data from several softwares to a genind object
Likelihood-based estimation of inbreeding
Assess polymorphism in genind/genpop objects
Compute Akaike Information Criterion for small samples (AICc) for snap...
Represents a cloud of points with colors
Compute allelic frequencies
Compute minor allele frequency
Boundary detection using Monmonier algorithm
Identify mutations between DNA sequences
genind constructor
genpop constructor
Convert objects with obsolete classes into new objects
Pairwise distance plots
Manipulate the population factor of genind objects.
Compute proportion of shared alleles
Compute the proportion of typed elements
Reading data from Fstat
Reading data from Genepop
Reading data from GENETIX
Reading PLINK Single Nucleotide Polymorphism data
Reading Single Nucleotide Polymorphism data
Reading data from STRUCTURE
Pool several genotypes into a single dataset
Compute scaled allele frequencies
Select genotypes of well-represented populations
Separate data per locus
Separate genotypes per population
SeqTrack algorithm for reconstructing genealogies
Importing data from an alignement of sequences to a genind object
Web servers for adegenet
When you need a break...
Choose the number of clusters for snapclust using AIC, BIC or AICc
Maximum-likelihood genetic clustering using EM algorithm
Formal class "SNPbin"
Analyse the position of polymorphic sites
Identification of structural SNPs
Spatial principal component analysis
Global and local tests
Monte Carlo test for sPCA
Access and manipulate the population strata for genind or genlight obj...
Access allele counts or frequencies
Restore true labels of an object
Virtual classes for adegenet
Functions to access online resources for adegenet
Cross-validation for Discriminant Analysis of Principal Components (DA...
Toolset for the exploration of genetic and genomic data. Adegenet provides formal (S4) classes for storing and handling various genetic data, including genetic markers with varying ploidy and hierarchical population structure ('genind' class), alleles counts by populations ('genpop'), and genome-wide SNP data ('genlight'). It also implements original multivariate methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance and similarity measures, and several spatial methods. A range of both empirical and simulated datasets is also provided to illustrate various methods.