Hi Elodie, <div><br></div><div>I can try to give you a superficial opinion which I hope to be of some interest for you. </div><div><br></div><div>To obtain a consistent estimate of number of cluster you can try to increase the number of iteration (n.iter) and that should work out. I experimented the problem of a non consistent number of cluster when using a few components retained, but I assume you're retaining all the components. </div>
<div><br></div><div>When you say "actual groups" I guess that you expected to see your 15 pops divided in 15 clusters. </div><div><br></div><div>If your pops are "actually" 15 pops, maybe your loci are not powerful enough to detect them. In any case, I wouldn't say that they are lying to you, it's merely the point of view of your 11 SSR.</div>
<div>I would say that in general, population structure is a question of tones between complete isolation and panmixia. If analysing different sets of molecular data for the same sample, there is a concordant indication of structure, one can probably assume that is the best way to cluster the individuals and that probably mirror reality quite well. </div>
<div>If you have other information that makes you be almost sure that your pops are 15 (I don't know, maybe something like: my pops are physically divided in 15 valleys, or other spatial information), you could try to run a sPCA. If you get a significant global structure (and there is the chance since you're working with nice plants and not stupid humans), you can see if one of the components gives you the expected 15 pops. Considering the result obtained with the DAPC, it won't probably be the first component, but maybe the second or the third...who knows...this could be a test to see if there is a global structure above the 15 pops and maybe your 15 is a kind of secondary structure (sorry, I am not explaining myself really well). In that case, you might be quite sure that your 15 SSR are giving you a good genetic point of view. Otherwise, if nothing that I've said will happen, you can only trust your 11 SSR and their clustering and try to find a good biological explanation to convince yourself and the rest of world that your number of clusters is the best for you individuals, or type more markers...</div>
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<div>Concerning the individuals with low probability, I have to confess that I've never worked at the individual level, but I imagine that it's perfectly normal to have those individuals in any cluster analysis. They might be hybrids, expression of the genetic/spatial continuity existing among natural pops. </div>
<div><br></div><div>I don't know what else to add...</div><div><br></div><div>good luck</div><div><br></div><div>Valeria</div><div><br><div class="gmail_quote">On 21 July 2011 10:12, Elodie Blanchet <span dir="ltr"><<a href="mailto:blanchet.elodie@gmail.com" target="_blank">blanchet.elodie@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><u></u>
<div bgcolor="#ffffff" text="#000000">
<p class="MsoNormal"><span lang="EN-GB">Dear Dr.
Jombart and Adegenet users,</span></p>
<p class="MsoNormal"><span lang="EN-GB"> </span></p>
<p class="MsoNormal"><span lang="EN-GB">I have some
questions about DAPC analysis. </span></p>
<p class="MsoNormal"><span lang="EN-GB">I worked on
tetraploid plant, with 11 SSR markers, 15 populations sampled
with 30
individuals each. </span></p>
<p class="MsoNormal"><span lang="EN-GB"> </span></p>
<p class="MsoNormal"><span lang="EN-GB">1) When I ran
‘find.clusters’ function, elbow in the curve of BIC values was
not very clear
so I ran it many time. But I obtained different optimal number
of cluster even
if I increase “max.n.cluster” option.</span></p>
<p class="MsoNormal"><span lang="EN-GB"><span> </span>I
agree that it is made with Bayesian
computation, but in this case how can I choose the “best”
optimal number of
cluster?</span></p>
<p class="MsoNormal"><span lang="EN-GB">Maybe,
these non-homogenous results between different runs are due to
the sampling
pattern of my populations which were along a corridor (thus
suggesting a
stepping-stone model of dispersal?)</span></p>
<p class="MsoNormal"><span lang="EN-GB"> </span></p>
<p class="MsoNormal"><span lang="EN-GB"><span> </span>2)
Besides, if I took into account the most
frequent “k” after ten runs of “find.clusters” function (k=8), I
observed that actual
groups did not correspond to inferred group. I mean that in the
best case, only
17,5 % of my actual group are inferred to clusters revealed by
the analysis. Even
if individual posterior membership was upper than 75% in most of
case, I did
not know if the genetic structure revealed by the analysis is
supported or not?
</span></p>
<p class="MsoNormal"><span lang="EN-GB"> </span></p>
<p class="MsoNormal"><span lang="EN-GB">3)
Moreover, some of the clusters revealed by the analysis, are
made with
individuals having posterior membership probability <60%, how
interpreting
these clusters? I would tend to run again the analysis and
reduce “k”…?</span></p>
<p class="MsoNormal"><span lang="EN-GB"> </span></p>
<p class="MsoNormal"><span lang="EN-GB"> </span></p>
<p class="MsoNormal"><span lang="EN-GB"> </span></p>
<p class="MsoNormal"><span lang="EN-GB">Sorry for this
long mail, I hope it is sufficiently clear.</span></p>
<p class="MsoNormal"><span lang="EN-GB">Thanks in
advance for your help.</span></p>
<p class="MsoNormal"><span lang="EN-GB">Elodie </span></p>
</div>
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<br></blockquote></div><br></div>