[adegenet-forum] PCA query?

Jombart, Thibaut t.jombart at imperial.ac.uk
Mon Jun 20 15:28:11 CEST 2011


Hello, 

in none, as far as PCoA / MDS are concerned, they do the same as PCA, but just allow for using fancier Euclidean distances. Loosing information in terms of total variance does not necessarily imply loosing information in terms of group discrimination. But if you're looking for clusters, you don't necessarily need to reduce the dimensionality of the data - most clustering algorithm don't.

Please have a look at the DAPC paper which is really on these topics. You may also be interested in the DAPC vignette for the next release of adegenet.
DAPC paper is here:
http://www.biomedcentral.com/1471-2156/11/94

DAPC vignette is there:
http://adegenet.r-forge.r-project.org/files/adegenet-dapc.pdf

Cheers

Thibaut

________________________________________
From: adegenet-forum-bounces at r-forge.wu-wien.ac.at [adegenet-forum-bounces at r-forge.wu-wien.ac.at] on behalf of AVIK RAY [avik.ray.kol at gmail.com]
Sent: 20 June 2011 13:12
To: adegenet-forum at r-forge.wu-wien.ac.at
Subject: [adegenet-forum] PCA query?

Hi all
bit of confusion with PCA in general, I did PCA in adegenet and it has
shown some plot with multiple clusters. My data is tetraploid
microsatellite data and I need to find out potential clusters i.e. some
individuals are more similar than others with allele data. But If not
mistaken PCA converts allele information into some synthetic variable
and does clustering where we tend to loose out lot of information since
it will select most but not all alleles; so in that sense does PCoA/
Multidimentional scaling or simply clustering analysis (e.g. K means or
hierarchical clustering) make more sense?
Thanks in advance for reply

AVIK

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