[adegenet-forum] Combining genetic and phenotypic data?

[Jombart, Thibaut]

Hello,

the quick and dirty way to do this would be taking the transformed data, normalize them to the same inertia (sum of squared values of the entries in each table), bind them into a single table, and run DAPC on this. It is not very elegant, but may do the trick if you want a quick and sample answer.

There is quite a bit of literature on coupling data. I think I mention a few in a very quick overview in my review paper (http://www.ncbi.nlm.nih.gov/pubmed/19156164). Coinertia analysis would be an option (function "coinertia" in the package "ade4"), but it won't allow you to couple two DAPC (only say, two PCA). Such implementation would be possible, but would probably demand quite a bit of work (essentially, a new paper, and a slightly boring one to write too!).

One option in between a clean, elegant solution and something manageable without too much pain is: look for combinations of the Discriminant Factors which are most alike between the two datasets. The procedure would be:

1) Make a DAPC for each table; keep all axes
2) Get the DAPC coordinates of the two tables, and standardize them to the same (say, 1) inertia. That is, divide the table by the sum of all squared entries.
3) Use these new matrices as inputs of coinertia

Does this make sense?

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 Niklas Tysklind [bssc08 at bangor.ac.uk]
Sent: 20 February 2013 19:40
To: adegenet-forum at lists.r-forge.r-project.org
Subject: [adegenet-forum] Combining genetic and phenotypic data?

Dear Thibaut and the rest of the Adegenet users,

Well done on all those DAPC assignment and structure functions. Me and my data are loving them!
I am currently using the assignment functions based on a set of microsatellite data AND (following your suggestion at the top of the vignette) on a set of environment driven quantitative phenotype data. It’s working quite well with both datasets, albeit showing rather different structures at different spatial scales. And here’s the thing, would it be possible to analyse both datasets merged together? I believe this would allow maximum assignment power as the structures complement each other (rather than mimic each other).

Many thanks in advance for any help and congratulations on an amazing package!

Regards,

Niklas

Dr Niklas Tysklind
Postdoctoral Research Officer
Celtic Sea Trout Project
Environment Centre for Wales
School of Biological Sciences
College of Natural Sciences
Bangor University,
Bangor, LL57 2UW
UK
Phone: +44 1248 382139
Email: ntysklind at bangor.ac.uk<mailto:ntysklind at bangor.ac.uk>

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