<div dir="ltr"><span style="font-family:arial,sans-serif;font-size:13px">Hi Jutta! </span><br style="font-family:arial,sans-serif;font-size:13px"><br style="font-family:arial,sans-serif;font-size:13px"><span style="font-family:arial,sans-serif;font-size:13px">well, ehm...sooo, </span><div style="font-family:arial,sans-serif;font-size:13px">
<br></div><div style="font-family:arial,sans-serif;font-size:13px">you already know about the limitations of Structure and, in general, bayesian approaches to cluster analysis. <div><br></div><div>For what matters, I can give you my opinion/suggestion in brief:<br>
<br>1) Structure and DAPC can give different results in several cases, depending on the evolutionary processes ongoing among specific inds/pops. I wish they always agreed - that would make our lives happier. In general, relying on a method rather than another is a decision that can be made based on the knowledge of the models assumed in different approaches and their limitations, and certainly the feeling you have about your case study given all the results you already got. Personally, I never take a best k out of the find.clusters unless the BIC shows a very clear cut-off (i.e. the curve nicely rising up after a certain K), but this is really a personal standard.<br>
<br></div><div>2) My understanding of the distribution of continuous populations (as this is seems to be the case of your data) is that there is actually no best clustering one can do. When the spatial distribution of the allele frequencies is organized in gradients or clines, the clusters are not the best tool to use to describe the data. That is why a method such as BAPS is useful. GENELAND is cool too, but there is no explicit modelling of gradients, plus the integration of the spatial info has never been totally clear to me. I find BAPS and TESS more straightforward. In this sense, they are good approaches to optimize a number of "clusters" although what you find out cannot be really called clusters (in the structure or dapc meaning). <br>
<br></div><div>It took me a while to learn how to manage the sense of panic/disorientation provoked by the absence of best clustering in some genetic datasets, but afterwards I even developed a preference for gradients, although I admit clusters are very useful.<br>
<br>Hope this is somehow useful<br>Best wishes </div><div><br></div><div>Valeria</div></div></div><div class="gmail_extra"><br><br><div class="gmail_quote">On 8 September 2013 15:35, Jutta Geismar <span dir="ltr"><<a href="mailto:Jutta.Geismar@senckenberg.de" target="_blank">Jutta.Geismar@senckenberg.de</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div style="MARGIN:4px 4px 1px;FONT:10pt Tahoma">
<div>
<p style="LINE-HEIGHT:normal;MARGIN:0cm 0cm 0pt" class="MsoNormal"><span style="FONT-FAMILY:'Tahoma','sans-serif';FONT-SIZE:10pt" lang="EN-US">Dear Valeria,<u></u><u></u><u></u></span></p>
<p style="LINE-HEIGHT:normal;MARGIN:0cm 0cm 0pt" class="MsoNormal"><span style="FONT-FAMILY:'Tahoma','sans-serif';FONT-SIZE:10pt" lang="EN-US"> <u></u><u></u></span></p>
<p style="LINE-HEIGHT:normal;MARGIN:0cm 0cm 0.75pt" class="MsoNormal"><span style="FONT-FAMILY:'Tahoma','sans-serif';FONT-SIZE:10pt" lang="EN-US">thank you very much for your quick answer. I’m aware of the problems STUCTURE has to analyze genetic data of continuous populations (see also <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2664.2008.01606.x/pdf" target="_blank">http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2664.2008.01606.x/pdf</a>). That is one reason I don’t want to use STUCTURE as the only cluster analysis. <span> </span>I haven’t attempted to use BAPS yet, but I gave GENELAND a trial to include spatial information. Besides testing for IBD with a Mantel test, I also modified the geographic distances by resistance values etc. I inferred from a SDM. A spatial autocorrelations didn’t show a clear pattern of spatial relation (also in different distance classes). <span> </span>A PCA indicates a big cloud around the center point. Each of the first two axes explained about 19 % of the variance.<u></u><u></u></span></p>
<p style="LINE-HEIGHT:normal;MARGIN:0cm 0cm 0.75pt" class="MsoNormal"><span style="FONT-FAMILY:'Tahoma','sans-serif';FONT-SIZE:10pt" lang="EN-US">Thanks to assure the correctness of my DAPC script. I set the maximum number of clusters to 50 to exclude a missing of structural shifts.<u></u><u></u></span></p>
<p style="LINE-HEIGHT:normal;MARGIN:0cm 0cm 0.75pt" class="MsoNormal"><span style="FONT-FAMILY:'Tahoma','sans-serif';FONT-SIZE:10pt" lang="EN-US">Nonetheless, I cannot explain the contrary results of structure indicating a panmictic population (4 parallel stripes) and DAPC assigning most individuals to one specific cluster. <u></u><u></u></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US">Thanks again for your comments. I will have a look at BAPS.<u></u><u></u></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US">Best wishes, <u></u><u></u></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US">Jutta<u></u><u></u></span></p></div>
<div>>>> Valeria Montano <<a href="mailto:mirainoshojo@gmail.com" target="_blank">mirainoshojo@gmail.com</a>> 9/5/2013 10:59 >>><br></div><div><div class="h5">
<div dir="ltr">
<div>
<div>Dear Jutta,<br><br></div>cluster analysis can be tricky when the samples analysed are distributed along a gradient and if there is no clear-cut subdivision, this can lead to contradictory results (have a look at this paper <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.192.3029&rep=rep1&type=pdf" target="_blank">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.192.3029&rep=rep1&type=pdf</a>). You may want to consider using TESS or BAPS with the admixture model option. These two software allow including the geographic coordinates as a prior information and the admixture model is a way to model spatial gradients. If you tested the IBD with a Mantel test, just be careful that a significant mantel test is not directly due to IBD, geo to gen correlation can be significant for different spatial/migratory schemes. I think your DAPC is ok, a part from the fact that there is no need to use the find.clusters with the number of PCs indicated by the optim.a.score. This procedure is used to optimize the discriminant space among clusters in the DAPC. To assign individuals to clusters you can simply retrieve all the variance (even though in your case is almost the same given that you have 98%). Only thing, I would try with max number of clusters around 20, more than your sampling locations. You can also give sPCA a try.<br>
<br></div>
<div>Hope this helps<br><br></div>
<div>Ciao<br><br></div>
<div>Valeria<br></div></div>
<div class="gmail_extra"><br><br>
<div class="gmail_quote">On 4 September 2013 15:03, Jutta Geismar <span dir="ltr"><<a href="mailto:Jutta.Geismar@senckenberg.de" target="_blank">Jutta.Geismar@senckenberg.de</a>></span> wrote:<br>
<blockquote style="BORDER-LEFT:#ccc 1px solid;MARGIN:0px 0px 0px 0.8ex;PADDING-LEFT:1ex" class="gmail_quote">
<div style="MARGIN:4px 4px 1px;FONT:10pt Tahoma">
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US"><font size="3"><font face="Calibri">Dear Mr Jombart and DAPC users,<u></u><u></u><u></u></font></font></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US"><u></u><font size="3" face="Calibri"></font><u></u></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US"><font size="3"><font face="Calibri">I used DAPC to analyze genetic structure in a small region with 20 microsatellite markers. I analyzed 330 individuals (14 sampling sites) and found little genetic differences (FST, D Jost), but a significant isolation by distance pattern. A cluster analysis in STRUCTURE resulted in four clusters (STRUCTURE Harvester) but all individuals had more or less equal posterior probability in all of the four inferred clusters. Therefore I assume a panmictic population structure. Since STRUCTURE is known for some problems analyzing datasets under IBD I analyzed the data with DAPC. DAPC resulted in 3 or 4 clusters (and tested up until K=7 to be sure), but in both cases these were randomly distributed among all individuals without a geographic context. Only 94 individuals were not assigned to one cluster with more than 90% and therefore would be counted as “admixed” (example in DAPC tutorial). For me the results of STRUCTURE and DAPC are in conflict to each other, but I don’t know how a panmictic population would look like in DAPC. Distances between sites are small and it is very likely that gene flow occurs among my sampling points, which might cause problems in genetic cluster analyses. I don’t know if I made any mistake in my thinking, that’s why I want to explain my procedure briefly:<u></u><u></u></font></font></span></p>
<p style="MARGIN:0cm 0cm 0pt 36pt"><span lang="EN-US"><span><font size="3" face="Calibri">1.</font><span style="FONT:7pt 'Times New Roman'"> </span></span></span><span lang="EN-US"><font size="3"><font face="Calibri">I used dapc and chose 1/3 of the sample size as PC (as suggested) and counted DAs in the plot (100% of the variability was included, 110 PC, 13 DA)<u></u><u></u></font></font></span></p>
<p style="MARGIN:0cm 0cm 0pt 36pt"><span lang="EN-US"><span><font size="3" face="Calibri">2.</font><span style="FONT:7pt 'Times New Roman'"> </span></span></span><span lang="EN-US"><font size="3"><font face="Calibri">To reduce variability I used optim.a.score (smart FALSE). The best a-score was around 0.2 (PC 61)<u></u><u></u></font></font></span></p>
<p style="MARGIN:0cm 0cm 0pt 36pt"><span lang="EN-US"><span><font size="3" face="Calibri">3.</font><span style="FONT:7pt 'Times New Roman'"> </span></span></span><span lang="EN-US"><font size="3"><font face="Calibri">After that I wanted to estimate the number of clusters by find.clusters and used the a-score as number of PCs and repeated the dapc (conserved variance was still 98%, 61 PCs, 2 DA) <u></u><u></u></font></font></span></p>
<p style="MARGIN:0cm 0cm 10pt 36pt"><span lang="EN-US"><font size="3"><font face="Calibri">I chose k in the BIC values after which the decrease was less compared to the previous, but not the lowest k.<u></u><u></u></font></font></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US"><font size="3"><font face="Calibri">If I have some mistakes in my procedure I would appreciate some advice. But also if the procedure is okay I cannot explain the contrariness of these two analyses. <u></u><u></u></font></font></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US"><font size="3"><font face="Calibri">Thanks a lot in advance for some help.<u></u><u></u></font></font></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US"><font size="3"><font face="Calibri">Jutta Geismar <u></u><u></u></font></font></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US"><font size="3"><font face="Calibri">PhD student</font></font></span></p>
<p style="MARGIN:0cm 0cm 10pt" class="MsoNormal"><span lang="EN-US"><font size="3"><font face="Calibri">Germany<u></u><u></u></font></font></span></p></div><br>_______________________________________________<br>adegenet-forum mailing list<br>
<a href="mailto:adegenet-forum@lists.r-forge.r-project.org" target="_blank">adegenet-forum@lists.r-forge.r-project.org</a><br><a href="https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/adegenet-forum" target="_blank">https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/adegenet-forum</a><br>
</blockquote></div><br></div></div></div></div>
</blockquote></div><br></div>