Hi Elodie,<div><br></div><div>you seem to trust a lot the population labels for your samples. Which criteria do you use in plants to assign pops? I am really curious to know it (is that the sample location maybe?). I would say that the 11 SSR are telling another story about the structure of these plants and consequently about the ad-mixed individuals (if I didn't completely misunderstand what you mean). Although I have no idea about the factors influencing dispersal in this species and then even less about its dispersal behaviour, the picture of "an invasive plant along a corridor of dispersal" suggests a fairly messed up situation (more or less like a biologist seeking a postdoc, you never know in which part of this bloody world you will end up...). If you think previous labels are some way more reliable than the SSR, I wouldn't know how to interpret this data apart from saying that you don't have enough information in that (once excluding possible selective processes since they are microsats). Otherwise you can consider challenging the labels and trusting the data. I still think that a spatial principal component analysis might help you to obtain an overall idea of the situation according to the SSR. It's not a cluster analysis so it's not absolutely straightforward to interpret for groups, but, at least, whether somewhere in the data there are these 15 groups or an over-grouping of them, you might be able to see it in one or few of the components (hopefully). </div>
<div><br></div><div>This is my opinion, but I am sure you can get more competent help on this from someone else.</div><div><br></div><div>All the best</div><meta http-equiv="content-type" content="text/html; charset=utf-8"><div>
<br>Valeria</div><div><br><div class="gmail_quote">On 22 July 2011 14:39, Elodie Blanchet <span dir="ltr"><<a href="mailto:blanchet.elodie@gmail.com">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>
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Hi Valeria,<br>
thanks a lot for your help!<br>
<br>
1) Concerning the assignation of "actual" group to "assign" cluster,
i don't expect to found 15 clusters, but that the majority of each
"actual" population was assigned to "assign" cluster (i.e. 80% of
"actual" population 1 is assigned to cluster A).<br>
I did not expect 15 clusters because i worked on an invasive plant
along a corridor of dispersal. <br>
<br>
2)Then concerning the individuals with low probability, i agree that
it is normal to observe individual with low probability, but i
wondered if i compared this second observation with the first (see
above) what can i deduce about cluster revealed by the function? <br>
<br>
But, probably running "find.clusters" function with more iterations
will able to obtain more consistent results.<br>
<br>
Thanks a lot for your help.<br>
All the best.<br>
Elodie<br>
<br>
<br>
Le 22/07/2011 13:39, valeria montano a écrit :
<div><div></div><div class="h5"><blockquote type="cite">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>
<div><br>
</div>
<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>
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<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>
<br>
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