Hi,<div><br></div><div>Yes, I agree there are many limitations to viewing populations in a tree like perspective. Initially, I was interested in quantifying how far apart the groups are on a scatter plot because it was hard to tell. I think the code Vladimir sent me does just that, at least it tells me which ones are closer to each other.</div>
<div><br></div><div>It will be cool to have a more biologically significant (Fst based) way implemented. One thing that came to mind too was if I wanted to use something like IMa2, I would need to have an assumption in tree form of how the populations are related. </div>
<div><br></div><div>Mac</div><div><br><div class="gmail_quote">On Sat, Apr 30, 2011 at 8:56 AM, Jombart, Thibaut <span dir="ltr"><<a href="mailto:t.jombart@imperial.ac.uk">t.jombart@imperial.ac.uk</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div>
<div style="direction:ltr;font-family:Tahoma;color:#000000;font-size:10pt">Hello,
<br>
<br>
that's a good question. Actually I thought about implementing something along these lines for the dapc scatterplot. I agree with Russell's point that relationships between populations are not necessarily best presented by fully bifurcating trees. However, linking
the populations which are the closest according to a given distance measure (e.g. Fst ) does make sense. I would go for a minimum spanning tree, which is a nice way of showing which are the closest neighbours in terms of genetic distances. It won't be too
much of a pain to code either.<br>
<br>
I will be working on the next adegenet release over the weeks to come, so will probably give it a go soon.<br>
<br>
Cheers<br>
<br>
Thibaut<br>
<div style="font-family:Times New Roman;color:rgb(0, 0, 0);font-size:16px">
<hr>
<div style="direction:ltr"><font color="#000000" face="Tahoma" size="2"><b>From:</b> <a href="mailto:adegenet-forum-bounces@r-forge.wu-wien.ac.at" target="_blank">adegenet-forum-bounces@r-forge.wu-wien.ac.at</a> [<a href="mailto:adegenet-forum-bounces@r-forge.wu-wien.ac.at" target="_blank">adegenet-forum-bounces@r-forge.wu-wien.ac.at</a>] on behalf of Mac Campbell [<a href="mailto:macampbell2@alaska.edu" target="_blank">macampbell2@alaska.edu</a>]<br>
<b>Sent:</b> 29 April 2011 22:48<br>
<b>To:</b> Vladimir Mikryukov<br>
<b>Cc:</b> adegenet forum<br>
<b>Subject:</b> Re: [adegenet-forum] Population clustering idea<br>
</font><br>
</div><div><div></div><div class="h5">
<div></div>
<div>Vladimir,<br>
<br>
Hey, that worked great. <br>
<br>
Thanks,<br>
<br>
Mac<br>
<br>
<div class="gmail_quote">On Fri, Apr 29, 2011 at 9:17 AM, Vladimir Mikryukov <span dir="ltr">
<<a href="mailto:vmikryukov@gmail.com" target="_blank">vmikryukov@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0pt 0pt 0pt 0.8ex;border-left:1px solid rgb(204, 204, 204);padding-left:1ex">
Here is a draft solution with the built in example:<br>
<br>
library(adegenet)<br>
data(H3N2)<br>
pop(H3N2) <- factor(H3N2$other$epid)<br>
dapc1 <- dapc(H3N2, all.contrib=FALSE, scale=FALSE, n.pca=150, n.da=5)<br>
<br>
hc <- hclust(dist(dapc1$grp.coord))<br>
dend <- as.dendrogram(hc)<br>
plot(dend)<br>
<div><br>
</div>
<div><br>
</div>
<div>HTH,<br>
</div>
<div>Vladimir</div>
<div><br>
<span style="color:rgb(102, 102, 102)">--</span><br>
<font style="color:rgb(102, 102, 102)" color="#888888">Vladimir Mikryukov<br>
</font><span style="color:rgb(102, 102, 102)">PhD student</span><br style="color:rgb(102, 102, 102)">
<font color="#888888"><font color="#888888"><font color="#888888"><font color="#888888"><font color="#888888"><font color="#888888"><span style="color:rgb(102, 102, 102)">Institute of Plant & Animal Ecology UD RAS,</span><br style="color:rgb(102, 102, 102)">
<span style="color:rgb(102, 102, 102)">Lab. of Population and Community Ecotoxicology</span><br style="color:rgb(102, 102, 102)">
<span style="color:rgb(102, 102, 102)">[8 Marta 202, 620144, Ekaterinburg, Russia]</span></font></font></font></font></font><br style="color:rgb(102, 102, 102)">
<span style="color:rgb(102, 102, 102)">Tel. <a href="tel:%2B7%20343%20210%2038%2058" value="+73432103858" target="_blank">
+7 343 210 38 58</a> (ext.290), +7 922 61 36 8</span></font> <br>
</div>
<div><br>
</div>
<div class="gmail_quote">
<div>
<div></div>
<div>On Fri, Apr 29, 2011 at 10:10 PM, Mac Campbell <span dir="ltr"><<a href="mailto:macampbell2@alaska.edu" target="_blank">macampbell2@alaska.edu</a>></span> wrote:<br>
</div>
</div>
<blockquote class="gmail_quote" style="margin:0pt 0pt 0pt 0.8ex;border-left:1px solid rgb(204, 204, 204);padding-left:1ex">
<div>
<div></div>
<div>Hi ,
<div><br>
<div>I was looking at another grad students clustering analysis, and he displayed it as a tree. It was some sort of ecological data. Anyways, I thought it might be useful to compare groups identified through DAPC that way. Display them graphically using
adegenet, but also provide some sort of tree as a hypothesis of relationships. It would seem to me that this could already by done in R without the need to develop anything new. Does anybody have an idea how to take output from dapc and make a tree?<br>
<br>
Mac</div>
<div><br>
</div>
<div><br clear="all">
<br>
-- <br>
Matthew A Campbell<br>
Department of Biology and Wildlife<br>
University of Alaska, Fairbanks<br>
</div>
</div>
<br>
</div>
</div>
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</blockquote>
</div>
<br>
</blockquote>
</div>
<br>
<br clear="all">
<br>
-- <br>
Matthew A Campbell<br>
Department of Biology and Wildlife<br>
University of Alaska, Fairbanks<br>
</div>
</div></div></div>
</div>
</div>
</blockquote></div><br><br clear="all"><br>-- <br>Matthew A Campbell<br>Department of Biology and Wildlife<br>University of Alaska, Fairbanks<br>
</div>