[adegenet-forum] interpretation sPCA

[valeria montano]

Hi again,

I see what you mean, I was superficially considering the fact that a clear
spatial structure result with the spca could actually lead to spatially
defined clusters in the dapc, but of course there is no warranty at all for
that. I know your point about the (somehow) biological meaningless of the
clusters, but still they are the genetic optimization of a specific dataset
and I think this is, let's say, the "best achievable structure" in the
contingency of someone's dataset. In very truth, in my view population
structure is almost a philosophic concept (as much as fitness) and it is
also true that any result in population genetic is usually the "dataset"'s
point of view (with the genuine intuition of the one interpreting, of
course). As for the summary statistics, a part from Fst, there are other
useful ones, at least to get to know your data and also to support main
findings. Btw the geoGraph package is really interesting...

Cheers

Valeria

On 5 October 2011 12:37, Jombart, Thibaut <t.jombart at imperial.ac.uk> wrote:

> Hello,
>
> thanks for jumping into the discussion.
>
> One has to be careful when playing with summary statistics derived from
> k-means. k-means finds groups which, by definition, maximise the Fst. So it
> is not clear how Fst values should be interpreted: real strong structuring,
> or indication or a good optimization procedure? In any case they cannot be
> tested, but that's not what's at stake here.
>
> The problem using non-geographically constrained groups while looking for
> an origin is: how do you define the location of say, the associated Hs?
> Barycentre may do, but these groups may well not be geographically organised
> at all. One alternative though, following this idea, would be using
> spatially-constrained clustering.
>
> True, moving windows are a bit arbitrary in that how the windows are
> defined is one's choice, but windows of say 3 different sizes could be
> defined and the results compared. Anyway I know of little spatial analyses
> which are not arbitrary (e.g. Neighbouring graphs in autocorrelation
> methods, data transformation in IBD/Mantel tests, etc.).
>
> Cheers
>
> Thibaut
>
>
>
> ________________________________________
> From: valeria montano [mirainoshojo at gmail.com]
> Sent: 05 October 2011 01:25
> To: Jombart, Thibaut
> Cc: Thomas, Evert (Bioversity-Colombia);
> adegenet-forum at r-forge.wu-wien.ac.at
> Subject: Re: [adegenet-forum] interpretation sPCA
>
> Hi there,
>
> sorry for my usual gratuitous intervention. I just wanted to suggest the
> eventuality to use the dapc groups as an alternative to population labels
> (summary statistics of internal diversity on them would already help getting
> an idea of the overall situation). I actually think that at the
> intraspecific level previous grouping of individuals based on sample
> location criteria or whatever are usually quite biased. Genetic structure
> grouping might sound as a circular reasoning, but I have the feeling it is
> less arbitrary than any other approach. The "sliding windows" is definitely
> an alternative but still a bit arbitrary and maybe scheming, although it may
> be worth comparing the results of the both. Moreover, to individuate the
> most probable point of origin of the species, it may be useful to also
> explore the phylogeography as a support to the results obtained with the
> population approach.
>
> Best
>
> Valeria
>
> On 4 October 2011 14:37, Jombart, Thibaut <t.jombart at imperial.ac.uk
> <mailto:t.jombart at imperial.ac.uk>> wrote:
> Dear Evert,
>
> I don't think the existence of a cline can be used to infer the origin of
> an organism. Surely in this case the cline you obtain is compatible with a
> 'central' origin, but the origin could as well be at either extremities of
> the cline, or anywhere in between. All the pattern says is that gene flow is
> somehow negatively related to geographic distance. More generally, no
> multivariate analysis result is directional. It would be reassuring if the
> outcome of sPCA roughly match that of DAPC, although both methods are
> different. This can be easily checked by DAPC scores on the map.
> Discrepancies can be due to, for instance, the fact that non-spatial genetic
> structures are the strongest (then DAPC will pick that up first). Another
> one would be the absence of spatial structure. It is safer to perform a
> global.rtest (although it lacks power) and to check the screeplot of sPCA
> before interpreting structures.
>
> Test the origin of your populations would need population-level data. The
> idea is that within-population diversity decreases when we get away from the
> origin due to repeated bottlenecks. If you don't have population data, one
> workaround would be using moving windows to map diversity geographically,
> and then use a simple optimisation procedure to find the 'optimal' origin. I
> don't know if this has been done before, so it might be newish. I have
> developed a package "geoGraph" (on Rforge, not on CRAN:
> https://r-forge.r-project.org/R/?group_id=348) which does this (apart from
> the moving windows) and has a vignette illustrating the whole process.
>
> Cheers
>
> Thibaut.
>
>
>
> ________________________________________
> From: Thomas, Evert (Bioversity-Colombia) [E.Thomas at CGIAR.ORG<mailto:
> E.Thomas at CGIAR.ORG>]
> Sent: 03 October 2011 21:48
> To: Jombart, Thibaut; Linda Rutledge; adegenet-forum at r-forge.wu-wien.ac.at
> <mailto:adegenet-forum at r-forge.wu-wien.ac.at>
> Subject: interpretation sPCA
>
> Dear Thibaut,
>
> I have a question regarding the interpretation of the sPCA scores as
> visualized in a color plot or interpolated lagged scores. I am working with
> intraspecific species data at continental level and found a strong gradient
> in my data  with a clear separation of a northern and southern group. Based
> on a number of grounds I believe that the center of origin of the species I
> am working with is located  at the "genotone" (or what to call this, I mean
> the grey area between both groups where the genetic differentiation is the
> steepest) . Does this make sense with the theory behind sPCA? I think the
> species moved north and south from the putative center of origin and
> developed into different genotypes which becomes apparent in the
> visualization of the sPCA...
>
> And should the outcome of an sPCA be somewhat reflected in the outcomes of
> discriminant analysis of principal components or are these really two
> different methods? (I apologize for my ignorance)
>
> Many thanks in advance
>
> Evert
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