[adegenet-forum] combining data

[Vinson Doyle]

Thanks for your replies.

I tried removing 'ret' and using it as a population factor in AMOVA and the
group differentiation is not significant.  Similarly, removing it and using
it as a population identifier in a PCA produces a plot absent of structure.
 Using scaleGen on the genind object prior to the pca provides a different
picture.  There is not much structure there.  There is a small cluster that
all share the same 'ret' type.

Thanks for your time.

Vinson

On Wed, Jun 13, 2012 at 11:30 AM, Jombart, Thibaut <t.jombart at imperial.ac.uk
> wrote:

> 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
>
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