[adegenet-forum] combining data

[Federico Calboli]

You might want to have a look at the information content of the 'ret' marker.  For instance, it used to be rule that 1 microsatellite could be considered as having the information equivalent of 3 SNPs (that was the idea about 6/7 years ago at least).  

If different kind of markers (note different KIND of markers, not just different markers of the same kind) have a different information content you might wish to weight them to make all the markers, irrespective of their type to have a similar effect on the analysis. YMMV though.

BW

F



On 13 Jun 2012, at 16:30, Jombart, Thibaut 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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--
Federico C. F. Calboli
Neuroepidemiology and Ageing Research
Imperial College, St. Mary's Campus
Norfolk Place, London W2 1PG

Tel +44 (0)20 75941602   Fax +44 (0)20 75943193

f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com

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