[adegenet-forum] combining data

[Jombart, Thibaut]

Hi there, 

it seems you have merged the data correctly together. So your results indicate that the marker 'ret' does contain some group structure as you mentioned. 

You could cross-validate these results using different approaches. One is using an AMOVA using 'ret' as a factor, I expect that the group differentiation will be significant. See function 'amova' in pegas (if I remember well).

Another option (not as satisfying, but quick) is to look for two clusters using find.clusters, and compare the obtained clusters to 'ret' (as a factor). This can be done using 'table( first_group, secondgroup).

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 Vinson Doyle [sonofvin at gmail.com]
Sent: 12 June 2012 19:28
To: adegenet-forum at lists.r-forge.r-project.org
Subject: [adegenet-forum] combining data

Dear Thibaut,

I have a situation with which I am bit confused.  I have 8 microsatellite loci
typed for a haploid organism.  I also have a retrotransposon-based marker
allowing me to recognize two different types of individuals in the populations.
If I code these two states with 3-digit codes "100" and "200" so as to combine them
with the microsatellite alleles, I use read.table to import the data:

>A<-read.table("populations.tab")
>A[1:5]
            Ret L10D10 L14F4 L2C1 LB5B4 LC192 LC2090 LC4168 LF9 Pop
103-M   200    278   325  217   366   305    349    220 284   M
105-M    100    276   321  217   366   286    349    220 284   M
108-M    200    278   321  215   366   286    349    220 284   M
1414-M   200    276   321  215   366   311    366    220 284   M
540-M    100    278   321  215   366   305    349    220 286   M


I convert this to a genind object and perform a PCA:

>NineLocusRegion<-df2genind(A[,-10], sep=NULL, ncode=3, pop=as.factor(A[,10]),ploidy=1, type="codom")

>obj <- na.replace(NineLocusRegion, method = "mean")
>pca1<-dudi.pca(obj$tab,cent=TRUE,scale=FALSE,scannf=FALSE, nf=3)
>barplot(pca1$eig[1:50],main="Eigenvalues")
>s.class(pca1$li,obj$pop,lab=obj$pop.names,sub="PCA1-2", csub=2)
>title("PCA of Regional Data\naxes 1-2")
>add.scatter.eig(pca1$eig[1:20],nf=3,xax=1,yax=2,posi="bottom")

> truenames(obj)$tab[1:5,1:10]
               Ret.100   Ret.200 L10D10.233 L10D10.235 L10D10.241 L10D10.243 L10D10.249 L10D10.251 L10D10.274 L10D10.276
103-M                 0         1          0          0          0          0          0          0          0          0
105-M                1         0          0          0          0          0          0          0          0          1
108-M                 0         1          0          0          0          0          0          0          0          0
1414-M                0         1          0          0          0          0          0          0          0          1
540-M                 1         0          0          0          0          0          0          0          0          0


The points on the plot do not cluster by population as expected.  However, they do seem to cluster on the plot by 1st column; the retrotransposon marker.  I figured this out using locator().  Confirmed by the fact that there is no clustering when this marker is removed.

Is there a problem with combining these data  in adegenet for PCA or any other analyses?

Thanks,
V