[adegenet-forum] Monmonier algorithm and individual scores

[Jombart, Thibaut]

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)