[adegenet-forum] Monmonier algorithm and individual scores

[Manuela]

Doctor Thibault and dear colleagues,

Deal:). I'll do my best.

About the PC's with biological meaning but not possessing
traits/phenotypic information. Later on, I'll explain to you why I think
this"crazy" idea might be feasible, in this case.

Thank you once more for the helpful suggestions.

Cheers,
Manuela


2014-06-03 12:47 GMT+01:00 Manuela <manuelacorreia2 at gmail.com>:

> Doctor Thibault and dear colleagues,
>
> Deal:). I'll do my best.
>
> About the PC's with biological meaning but not having traits/phenotipic
> information. Later I'll explain to you the reason why I insist on using the
> softwares you have developed for PCA and sPCA  to go on with this "crazy"
> idea.
>
> Thank you once more for the helpful suggestions.
>
> Cheers,
> Manuela
>
>
> 2014-06-03 10:26 GMT+01:00 Jombart, Thibaut <t.jombart at imperial.ac.uk>:
>
>
>> Hi there,
>>
>> I would not recommend using all three phylogenetic reconstruction
>> methods, even if with 19 SNPs there shouldn't be major differences. I
>> covered the maximum parsimony for historical reasons, but I can't see it
>> being useful here.
>>
>> Other clustering approaches sounds like a good idea. If you ever fancy
>> documenting how to use them on genetic data in a small tutorial, I think
>> that would be a very handy to others ;)
>>
>> As for your last question, it makes a lot of sense, but you will need
>> external information for this. Eigenvalue selection procedures based on
>> inertia will basically fail to detect the structures you talk about. So you
>> will need to be able to test e.g. the correlation of your PCs to a set of
>> traits, or their spatial distribution, etc.
>>
>> Cheers
>> Thibaut
>>
>>
>> ________________________________________
>> From: Manuela [manuelacorreia2 at gmail.com]
>> Sent: 03 June 2014 10:01
>> To: Jombart, Thibaut
>> Cc: adegenet-forum at lists.r-forge.r-project.org
>> Subject: Re: [adegenet-forum] Monmonier algorithm and individual scores
>>
>> 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
>> <mailto: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<mailto:
>> adegenet-forum-bounces at lists.r-forge.r-project.org> [
>> adegenet-forum-bounces at lists.r-forge.r-project.org<mailto:
>> adegenet-forum-bounces at lists.r-forge.r-project.org>] on behalf of
>> Manuela [manuelacorreia2 at gmail.com<mailto:manuelacorreia2 at gmail.com>]
>> Sent: 31 May 2014 21:46
>> To: adegenet-forum at lists.r-forge.r-project.org<mailto:
>> 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)
>>
>>
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.r-forge.r-project.org/pipermail/adegenet-forum/attachments/20140603/bd9f4286/attachment.html>