[adegenet-forum] more clusters with less PCs retained in find.clusters

Lu Maffey lucia.maffey at gmail.com
Mon May 13 15:38:40 CEST 2019

That makes total sense. Thank you for your answer! (And for taking the time
to address all the questions and doubts in this forum. It's highly
apprecciated! =)

El lun., 13 de may. de 2019 a la(s) 08:36, Thibaut Jombart (
thibautjombart at gmail.com) escribió:

> Hi Lu,
> you have a lot of variables for few individuals. Chances are when you
> retain too many PCs, you end up keeping a lot of random noise and Kmeans
> struggles to find meaningful centroids for your clusters. When keeping less
> PCs, you likely get rid of a lot of the noise, but the signal (group
> discrimination) stays.
> Best
> Thibaut
> --
> Dr Thibaut Jombart
> Associate Professor in Outbreak Analytics, London School of Hygiene and
> Tropical Medicine
> Senior Lecturer in Genetic Analysis, Imperial College London
> President of RECON: repidemicsconsortium.org
> https://thibautjombart.netlify.com
> Twitter: @TeebzR
> On Sun, 12 May 2019 at 18:00, Lu Maffey <lucia.maffey at gmail.com> wrote:
>> Hi everyone!
>> I'm performing DAPC analyses on radseq data from mosquitoes. When I run
>> the previous find.clusters function, I get larger K values (that is more
>> clusters) when I retain fewer PCs. For example, I have a data set of 40
>> individuals (with over 80.000 SNPS). When I retain 30-38 PCs, I get K=1 and
>> when I choose to retain 15 PCs, I get K=3. Shouldn't it be the other way
>> around? More PCs retained smaller K and vicecersa? When I run the same data
>> in Structure I get  K=3 so I tend to think that I should retain fewer PCs
>> in find.clusters but as the vignette explains that you should retain all of
>> them, I'm worried I'm missing something here. I've been searching the
>> archive but only found the same question unanswered.
>> Thanks in advance!!!
>> Lu
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