[adegenet-forum] Inconsistent DAPC

Jombart, Thibaut t.jombart at imperial.ac.uk
Wed Jul 9 12:49:04 CEST 2014

Hello there, 

this is because you are using a fixed criteria to decide the group partitioning. This was only designed for simulated data analysis, or cases where one needs to analyse hundreds of datasets.

Best for your to look at the BIC curves (over a couple of iterations) and assess which clustering solution you want to retain.


From: adegenet-forum-bounces at lists.r-forge.r-project.org [adegenet-forum-bounces at lists.r-forge.r-project.org] on behalf of Vojtěch Zeisek [vojta at trapa.cz]
Sent: 09 July 2014 11:34
To: Adegenet R-Forum
Subject: [adegenet-forum] Inconsistent DAPC

I was running DAPC on almost 500 individuals (over 60 populations) genotyped
by 10 microsatellite primers as follows:
kfind <- find.clusters(genind, n.pca=100, stat="BIC", choose.n.clust=FALSE,
criterion="diffNgroup", max.n.clust=75, n.iter=100000000, n.start=10000,
scale=FALSE, pca.select="nbEig", truenames=TRUE)
table.value(table(pop(genind), kfind$grp), col.lab = paste("kfind",
dapc <- dapc(genind, kfind$grp, n.pca=35, n.da=20, center=TRUE, scale=FALSE,
var.contrib=TRUE, pca.info=TRUE, pca.select="nbEig", truenames=TRUE)
scatter(dapc, main="DAPC", posi.da=“bottomleft“)
When I did it for first time, I've got nice partitioning to 3 clusters, quiet
similar to output of Structure and PCoA. When I tried it second time with same
parameters, I've got division to 12 groups. So I wonder how is it possible and
how to trace to make it reproducible and find out why it shifted so much.

Vojtěch Zeisek

Department of Botany, Faculty of Science
Charles University in Prague
Benátská 2, Prague, 12801, CZ

Institute of Botany, Academy of Science
Zámek 1, Průhonice, 25243, CZ

Czech Republic

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