<div dir="ltr"><div><div><p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)">1. xvalDAPC() require one of the input as group membership (grp) of individuals and then it gives number of optimum PC as one of the
output. However the grp what we used is from find.clusters(). Do I need to use optimum PC from xvalDAPC and re-run find.clusters()?</span></p><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"></span><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"></span><p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"><br></span></p></div><div>No. With find.clusters(), you want to use as many principal components as you can for the k-means clustering. The purpose of xvalDAPC() is to determine the optimum number of PC to perform the DAPC, not any other analysis. This is to prevent over-fitting the data. <br></div><div><br></div><div>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)">2. How can I use posterior probabilities from DAPC analysis? The prior and posterior group membership changing for few individuals</span></p></div><br></div><div>There's a lot you can do with the results from the DAPC. You can use summary() on your result to give you an overall or by cluster reassignment rate. You can also use the loadings from the discriminant axes to investigate what alleles are influencing the separation. I would recommend going through the tutorial: <a href="https://github.com/thibautjombart/adegenet/blob/master/tutorials/tutorial-dapc.pdf">https://github.com/thibautjombart/adegenet/blob/master/tutorials/tutorial-dapc.pdf</a><br><br></div><div>If you want an indicator of how well the clustering separated the sample by genetic distance, you can use AMOVA and report the phi statistic (but not the p-value, see: <a href="https://doi.org/10.1111/mec.13243">https://doi.org/10.1111/mec.13243</a>)</div><div><div><br></div><div><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)">3. How can I select optimum number of DA functions? I used default (n.da=grp-1) in DAPC analysis. How to decide how many DA functions
is required? Is there any statistic to take account of<br></span></div><div><br></div><div>I believe this is covered in the DAPC tutorial, but in short, the barplot represents the amount of separation between groups along the axes of the DAPC; choose the largest ones. The first one always represents the most separation, the second one, the second most, and so on. For example, If you see three large bars and several smaller trailing bars, then that indicates that most of the variation is described by the first three axes and you only need those three in the model (the rest will only provide noise). When you run a scatterplot from DAPC, it will always show you the first two discriminant axes.</div><div><br></div><div>Hope that helps.</div><div><br></div><div>Best,</div><div>Zhian<br></div><div><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"></span></div><div><br></div><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Thu, Oct 24, 2019 at 6:26 PM Das, Roma (ICRISAT-IN) <<a href="mailto:r.das@cgiar.org">r.das@cgiar.org</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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<p class="MsoNormal"><a name="m_-5932225516471178104__MailEndCompose"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)">Thanks Zhian for your reply. Really helpful. Please help me to further understand this<u></u><u></u></span></a></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)">1. xvalDAPC() require one of the input as group membership (grp) of individuals and then it gives number of optimum PC as one of the
output. However the grp what we used is from find.clusters(). Do I need to use optimum PC from xvalDAPC and re-run find.clusters()?<u></u><u></u></span></p><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"></span><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"></span><p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"><br><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)">2. How can I use posterior probabilities from DAPC analysis? The prior and posterior group membership changing for few individuals<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)">3. How can I select optimum number of DA functions? I used default (n.da=grp-1) in DAPC analysis. How to decide how many DA functions
is required? Is there any statistic to take account of<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)">Thanks and regards,<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)">Roma</span><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"><u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11pt;font-family:"Calibri",sans-serif;color:rgb(31,73,125)"><u></u> <u></u></span></p>
<p class="MsoNormal"><b><span style="font-size:11pt;font-family:"Calibri",sans-serif" lang="EN-US">From:</span></b><span style="font-size:11pt;font-family:"Calibri",sans-serif" lang="EN-US"> adegenet-forum [mailto:<a href="mailto:adegenet-forum-bounces@lists.r-forge.r-project.org" target="_blank">adegenet-forum-bounces@lists.r-forge.r-project.org</a>]
<b>On Behalf Of </b>Zhian Kamvar<br>
<b>Sent:</b> 24 October 2019 10:15<br>
<b>To:</b> <a href="mailto:adegenet-forum@lists.r-forge.r-project.org" target="_blank">adegenet-forum@lists.r-forge.r-project.org</a><br>
<b>Subject:</b> Re: [adegenet-forum] adegenet-forum Digest, Vol 131, Issue 2<u></u><u></u></span></p>
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<p class="MsoNormal">Hello Roma,<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<p class="MsoNormal">Use the groups from find.clusters.<u></u><u></u></p>
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<p class="MsoNormal" style="margin-bottom:12pt"><br>
It's a common misconception, but DAPC is not a method to define groups. It is a tool that allows you to create a model of your data based on your groups so that you can assess how well you can differentiate samples into individual groups (similar to AMOVA)
and give you a method to predict what groups your samples belong in based on that model.<u></u><u></u></p>
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<p class="MsoNormal" style="margin-bottom:12pt">find.clusters() and snapclust() are the only functions in adegenet that can determine groups de novo from your data.<u></u><u></u></p>
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<p class="MsoNormal">Hope that helps,<u></u><u></u></p>
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<p class="MsoNormal">Zhian<u></u><u></u></p>
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<p class="MsoNormal" style="margin-bottom:12pt"><br>
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<u></u><u></u></p>
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<p class="MsoNormal"><u></u> <u></u></p>
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<div>
<p class="MsoNormal">On Thu, Oct 24, 2019 at 11:00 AM <<a href="mailto:adegenet-forum-request@lists.r-forge.r-project.org" target="_blank">adegenet-forum-request@lists.r-forge.r-project.org</a>> wrote:<u></u><u></u></p>
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1. DAPC-Find optimum number of groups (Das, Roma (ICRISAT-IN))<br>
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Date: Thu, 24 Oct 2019 07:59:10 +0000<br>
From: "Das, Roma (ICRISAT-IN)" <<a href="mailto:r.das@cgiar.org" target="_blank">r.das@cgiar.org</a>><br>
To: "<a href="mailto:adegenet-forum@lists.r-forge.r-project.org" target="_blank">adegenet-forum@lists.r-forge.r-project.org</a>"<br>
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Subject: [adegenet-forum] DAPC-Find optimum number of groups<br>
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Hello everyone,<br>
<br>
* Based on DAPC analysis, I am not sure whether I should treat the final group for individuals line as 1) prior group from find.clusters() or<br>
<br>
2) group with maximum posterior probability after xval.DAPC()<br>
<br>
<br>
As in scatterplot from DAPC analysis individuals are plotted based on prior group. Please advise if there a way to choose optimum number of discriminating functions to be used.<br>
<br>
<br>
<br>
<br>
Regards,<br>
<br>
Roma<br>
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