Overview

Welcome to the Shiny Genotyping Server

The DAPC Server is designed for the analysis of multilocus genotyping data, specifically micro/minisatellite data and Multilocus VNTR analysis (MLVA), on haploid microorganisms (bacteria and fungi)


Github

I recommand to download the code and use the application from github to avoid several server problems and for more feedback

https://github.com/Aucomte/ShinyGenotyping


Citations


the DAPC tab was adapted from the code written by the adegenet team. They have their own DAPC shiny application : https://github.com/thibautjombart/adegenet

Citation for adegenet:

Jombart T.(2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24: 1403-1405. doi:10.1093/bioinformatics/btn129 [link to paper]

Jombart T. and Ahmed I. (2011) adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics. doi: 10.1093/bioinformatics/btr521

Citation for the DAPC:

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 [link to paper]

http://adegenet.r-forge.r-project.org/

Citation for poppr:

Kamvar ZN, Tabima JF, Grünwald NJ (2014). “ extit{Poppr}: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction.” PeerJ, 2, e281. ISSN 2167-8359, doi: 10.7717/peerj.281, https://doi.org/10.7717/peerj.281.

Kamvar ZN, Brooks JC, Grünwald NJ (2015). “Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality.” Front. Genet., 6, 208. doi: 10.3389/fgene.2015.00208, https://doi.org/10.3389/fgene.2015.00208.

Input data

First of all, please submit your input data (in csv or txt tabulated format), either repetition file or genemapper output (see file example). Do not forget to hit submit files! After submitting, fill in the genotype object tab to indicate your loci, the variable you choose as population and then create the genind object that will be used for the rest of the analyse.

Genemapper table :

Metadata table :

repetition table :

Final Table :

genind = adegenet class for individual genotypes
(rdocumentation)
Before everithing select the loci and the population (ex: Country for the datatest) you want to work with, the his the button Submit. If you change the parameters, do not forget to hit sumbit again.
Each haplotype have been numeroted and have a specific allelic profile. Each strain have a corresponding haplotype.

Table 1: Haplotypes and Strains.

Table 2: Allelic profiles of the haplotypes.

Statistics

Diversity by locus, estimated by PopGeneReport :


Download Output archive

Pairwise FST :

One of several genetic distances among all pairs of populations. Calculated with genet.dist from the package Hierfstat.

Basic statistics per locus (hierfstat) :

The number of alleles used for rarefaction :


                                    
Rarefied allele counts :

                                    

                                      

                                        
Estimates allelic richness, the rarefied allelic counts, per locus and population

                                      

                                    

                                    

                                      































                                      

                                      

                                    

                                  

                                

                              

                              

                                

                                  

                                    
missing data by locus and by population:

                                    

                                    

                                      































                                      

                                    

                                  

                                

                              

                            

                          

                        

                      

                      

                        

                          

                            
The genotype accumulation curve is generated by the Poppr package (R::poppr:: genotype_curve, https://doi.org/10.7717/peerj.281). 
t describes the genotypic diversity in relation to different combinations of TR loci, and is used to estimate the genotypic resolution of the MLVA scheme 
(further details: https://doi.org/10.1111/j.1471-8286.2006.01522.x). The curve is generated by sampling x loci randomly and counting the number of multilocus genotypes (MLG) observed. 
This sampling is repeated r times from 1 to n-1 loci, creating n-1 distributions of observed MLGs. The genotypic resolution is considered good when the curve reaches a plateau. 

                          

                        

                        

                          

                            































                            

                          

                        

                      

                      

                        

                          

                            
N=number of individuals in the specified population; MLG=number of multilocus genotypes found in the population;
                           eMLG=the expected MLG at the lowest common sample size;
                           SE=standard error for the rarefaction analysis; H=Shannon-Weiner diversity index;
                           G=Stoddard and Taylor’s index;
                           lambda=Simpson’s index;
                           E.5=Evenness estimate;
                           Hexp=Nei’s gene diversity (expected heterozygosity);
                           la= index of association for each population factor (a measure of multilocus linage disequilibrium);
                           p.la=p-value for la;
                           rbarD=Standardised index of association;
                           p.rD=p-value for rbarD.
                           NA: not available.
                           For details, please read https://doi.org/10.7717/peerj.281.

                          

                        

                        

                          

                            

                              

                                

                                  































                                  

                                  

                                

                              

                            

                          

                        

                      

                    

                  

                

              

            

          

          

            

              

                

                  
Find K : the number of clusters

                

              

              

                

                  
                

                

                  

                    
                    

                      

                        

                          

                            

                              Two methods to select the right number of clusters for DAPC clusterisation:
                              

                              - Snapclust is a fast maximum-likelihood method, combining the advantages of both model-based and geometric approaches (Beugin et al., 2018). The optimal number of clusters (k) are estimated using both the Akaike, Kullback and Bayesian Information Criterion (AIC, KIC, BIC, respectively). Ten runs of the Expectation-Maximisation (EM) algorithm are advised to estimate an accurate K and the probability of assignment (Q) of each individual into each of the k inferred.
                              

                              - The function find.clusters runs successive k-means clustering with increasing number of clusters (k) and the optimal number of clusters is selected based on lowest Bayesian information criterion (BIC) (Jombart et al., 2010). 10 to 20 runs are advised to estimate an accurate K.
                              

                              References:
                              

                              Beugin, M.-P., Gayet, T., Pontier, D., Devillard, S. and Jombart, T. (2018) A fast likelihood solution to the genetic clustering problem. Methods in Ecology and Evolution, 9, 4. doi: 10.1111/2041-210X.12968.
                              

                              Jombart, T., Devillard, S. and Balloux, F. (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet., 11, 94. doi: 10.1186/1471-2156-11-94.
                            

                          

                        

                        

                          

                            
Ideally, the lowest AIC/BIC corresponds to the best model.

                          

                        

                        

                          

                            

                              

                                































                                

                              

                            

                          

                        

                        

                          

                            

                              

                                































                                

                              

                            

                          

                        

                        

                          

                            

                              

                                































                                

                              

                            

                          

                        

                      

                      

                        

                          































                          

                        

                      

                    

                  

                

              

            

          

          

            

              

                

                  
DAPC Analysis

                

              

              

                

                  
                

                

                  

                    
                    

                      

                        

                        

                          

                          Scatter Plot
                        

                        
The Scatter Plot page provides a visual assessment of between-population differentiation.
                             Generated by applying the R function scatterplot to a dapc object, the output generated will appear in one of two forms.
                             If only one DA is retained (always the case if there are only 2 groups),
                             or both the x-axis and y-axis of the scatterplot are set to the same value, the output will display the
                             densities of individuals on the given discriminant function.
                             If more than one DA is retained and selected, the output will display individuals as dots and
                             groups as inertia ellipses, and will represent the relative position of each along the two
                             selected axes.

                        
The number of axes retained in both the PCA and DA steps of DAPC will have an impact on the
                             analysis and affect the scatter plot. By default, the number of DA axes retained is set at
                             the maximum of (K - 1) axes, where K is the number of groups. The default value of the number of
                             PCA axes is more arbitrarily defined, however, the 'Use suggested number of PCA components?'
                             tickbox provides the user with the option to use cross-validation to identify and select an optimal number
                             of PCs, where one exists. For more on this, see the section on cross-validation.

                        
There are a wide variety of graphical parameters for the DAPC scatterplot that can be customised
                             by the user. Those parameters that lack intuitive definition are described further in the Glossary.

                      

                      

                        

                        

                          

                          Summary
                        

                        
This page provides a summary of the dapc object.

                        
$n.dim' indicates the number of retained DAPC axes, which is affected by both the number of PCA
                             axes and DA axes retained.

                        
'$n.pop' indicates the number of groups or populations, which is defined by the dataset.

                        
'$assign.prop' indicates the proportion of overall correct assignment

                        
'$assign.per.pop' indicates the proportions of successful reassignment (based on the discriminant
                             functions) of individuals to their original clusters. Large values indicate clear-cut clusters, while low
                             values suggest admixed groups.

                        
'$prior.grp.size' indicates prior group sizes.

                        
'$post.grp.size' indicates posterior group sizes.

                      

                      

                        

                        

                          

                          Compoplot
                        

                        
This page displays a compoplot, which is a bar plot showing the probabilities of assignment of
                             individuals to the different clusters. Individuals are plotted along the x-axis and membership probabilities are
                             plotted along the y-axis.From the compoplot, one can draw inferences about potential
                             admixture, and about the way in which the selection of PCA axes affects the stability of membership probabilities.

                      

                      

                        

                          

                            
Assignment of each individuals to one or several groups. If all the individuals have only one assignment, the DAPC is robust. 
However, an individuals with two or more assignments can be an hybrid (with alleles coming from different groups). In this kind of case, the blue cross is the main assignment.
                               

                          

                        

                        

                      

                      

                        

                        

                          
Number of selected vs. unselected alleles

                          

                          
List of selected alleles

                          

                          
Names of selected alleles

                          

                          
Contributions of selected alleles to discriminant axis

                          

                        

                        

                          

                          Loading Plot
                        

                        
The Loading Plot page allows the user to examine how the original variables contribute to the
                             discriminant functions created by DAPC. Variables are plotted along the x-axis, and the contribution
                             of those variables to the DAPC is plotted in the y-axis.

                        
The side panel on the Loading Plot page provides the option of selecting a threshold above which variables are identified.
                             This can be useful simply for clarifying the image; hence, by default, only variables above the third quartile threshold are labelled.
                             A drop-down menu contains a variety of clustering methods that can also be used to set this threshold.
                             If desired, the user can choose to 'Select and describe features above the threshold'

                      

                      

                        

                          

                            
Comparison of the repartition of the individuals between the DAPC clusters and a chosen factor.

                          

                        

                        

                        

                      

                      

                        

                          

                            
Assignation of the individuals to the different DAPC clusters

                          

                        

                        

                      

                    

                  

                

              

            

          

          

            

              

                

                  
SnapClust Analysis