[adegenet-forum] Monmonier algorithm and individual scores

[Manuela]

Doctor Thibault and dear colleagues,


I would like to thank you for the valuable criticism you made in this
output. The idea behind the IS was, solely, to have a first draft of the
georeferenced clusters because in spatial clusters  I'm well-aware that
several different genoypes at the same coordinates in species with a very
low mobility or with no mobility could be a strong indication that the
genetic variability is only due to environment while a great genetic
diversity nearby may result from a short dispersal highly spatial
correlated. To need of further confirmation by sPCA and/or clustering
techniques.


The identification of spatial clusters in PCA, particularly by sPCA is no
doubt more realiable than with Monmonier algoritm in this case. But I'd
rather try to study more deeply each one of the 3 different methods
(distance based-methods, Parsymony and maximum Likelihood) proposed in your
tutorial "Trees" just to check it in first place if they might be
appropriate to this dataset, Secondly, if they would gave different
information perhaps with higher resolution when compared to classic NJ
Tree, after validation by bootstrap.  Eventually, if none is appropriate I
always be able to rely on several clustering techniques more adequate for
qualitative data, available at the "Cluster" package and to perform the
validation by "cl Valid" following several criteria.



>From a very simplistic point of view, PCA analysis (not scaled) might
provides us with information of the genetic variability whereas sPCA about
the significance of local and global structures. But, on the whole, the
information provided by these two analysis: Moran's Index , variance and
allele loadings, enable us to discriminate the loci more informative on
genetic variability but not spatially structured from those whose
variability its spatial structured. To be further confirmed through
biplots.


Another challenge ahead. To figure out the way to select the PC's having
biological meaning and most probably not associated to the highest
eigenvalues. Particularly, in the absence of traits or phenotype
information.


Please, feel free to make more comments or to give another suggestion(s).



Cheers,

Manuela


2014-06-02 17:20 GMT+01:00 Jombart, Thibaut <t.jombart at imperial.ac.uk>:

> Hi Manuela,
>
> thanks for re-posting on the forum. In this case, it seems that locations
> are very aggregated - a lot of genotypes were sampled roughly at the same
> place. Monmonier is unlikely to do well under such circumstances. The
> algorithm is very sensitive to local differences, and these are unstable
> for this kind of spatial distribution. I would recommend other approaches.
> For instance, if you want to define spatial clusters, you could use a basic
> clustering algorithm based on the principal components of a PCA (if spatial
> structure is obvious) or sPCA (if not, but there is still a spatial
> structure). Assuming 'foo' is your analysis (PCA or sPCA), one example
> would be using something along the lines of:
>
> h1 <- hclust(dist(foo$li)^2)
> plot(h1)
> cutree(h1)
>
> Etc.
> Check ?hclust for different clustering methods.
>
> Cheers
> Thibaut
>
>
> ________________________________________
> From: adegenet-forum-bounces at lists.r-forge.r-project.org [
> adegenet-forum-bounces at lists.r-forge.r-project.org] on behalf of Manuela [
> manuelacorreia2 at gmail.com]
> Sent: 31 May 2014 21:46
> To: adegenet-forum at lists.r-forge.r-project.org
> Subject: [adegenet-forum] Monmonier algorithm and individual scores
>
> Dear colleagues of Adegenet forum,
>
> First of all I must congratulate Doctor Thimbault for the wonderful work
> he has been so far developed. And following his own suggestion I'm sharing
> with you a specific issue raised by the output generated by Monmonier
> algorithm used for boundary detection.
> I have a sample made of 170 individuals, collected on 9 different places
> and genotyped for 19 SNPs by Realtime PCR.
> Before I run this line on the R script I had to explain to you about each
> one of them:
> mon1<- monmonier(xy ,D, gab)
>
> xy – spatial coordinates UTM/Km) ;
> D – pairwise allele sharing distance (“Prabclus” package);
> gab <-chooseCN(xy,ask=FALSE,type=1)  (Delaunay Triangulation)
>
> plot(mon1,1:170,method=”greylevel”,add.arr=FALSE,bwd=6,col=”red”)
> From the output produced, it can be clearly seen that there are 4 clusters
> of individuals having four scores (50,100,150,200). But, I can't find a way
> to have access to individual scores. As matter in fact, I consulted in
> detail all the arguments provided on Plot function but none of them seemed
> to me to be on the way I could extract the individuals scores (IS).
> I’m wondering if you could give me a hint about it. Any help will be
> appreciated.
> Kind regards,
> Manuela (Biochemist)
>
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