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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US">Hi Thibaut,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US">I have been using in just as a normal matrix (i.e. not a genlight object) and it is pretty fast on a decent cpu. However, I still have
an outstanding question on the DAPC itself. My earlier post was as follows:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black">I'm using DAPC to try to discriminate between two groups. However, the data are not individual genotypes, but rather the result of genotyping pools of samples. There are
20 individual pools in each of the two groups. So basically I am providing the analysis with a frequency of the A allele (all dimorphic SNPs) for each pool. There are ~600,000 SNPs in the dataset. I ran the xvalDapc function and it identified 20 PC as the
optimum. However when I run the DAPC on the 20, I get the following warning:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black">Warning message:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:red">In dapc.data.frame(as.data.frame(x), ...) :<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:red"> number of retained PCs of PCA may be too large (> N /3)<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:red">results may be unstable<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black">What does this mean in terms of my discrimination, which is pretty good among the two groups? In other analyses such as ranking SNPs according to FST, outlier analyses,
etc. the separation is pretty good but not as clear as with DAPC overall.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black">Therefore I am not sure if 1) DAPC is genuinely doing a better job at separating the groups or (2) there is still over-fitting of the data with DAPC given the large number
of variables and am I simply finding a solution (which may not be real?)<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black"><o:p> </o:p></span></p>
<pre><span style="color:black">Also, I have a question on the xvalDapc function. <o:p></o:p></span></pre>
<pre><span style="color:black"><o:p> </o:p></span></pre>
<pre><span style="color:black">When I run the following<o:p></o:p></span></pre>
<pre><span style="color:black">xval1 <- xvalDapc(FD_t, group, n.pca.max=40, result="groupMean", center=TRUE, scale=FALSE, xval.plot=TRUE)<o:p></o:p></span></pre>
<pre><span style="color:black"><o:p> </o:p></span></pre>
<pre><span style="color:black">I get results back at 5, 10, 15, 20, 25, 30, 35<o:p></o:p></span></pre>
<pre><span style="color:black"><o:p> </o:p></span></pre>
<pre><span style="color:black">However, when I run (on the same dataset)<o:p></o:p></span></pre>
<pre><span style="color:black"><o:p> </o:p></span></pre>
<pre><span style="color:black">xval1a <- xvalDapc(FD_t, group, n.pca.max=40, result="groupMean", training.set=0.7, center=TRUE, scale=FALSE, xval.plot=TRUE)<o:p></o:p></span></pre>
<pre><span style="color:black"><o:p> </o:p></span></pre>
<pre><span style="color:black">I get results back at 13 different PCA axes levels, roughly by increments of 2<o:p></o:p></span></pre>
<pre><span style="color:black"><o:p> </o:p></span></pre>
<pre><span style="color:black">Also, I am looking to specify the increments so tried something like the following:<o:p></o:p></span></pre>
<pre><span style="color:black">xval2 <- xvalDapc(FD_t, group, n.pca.max=40, result="groupMean", training.set=0.7, center=TRUE, scale=FALSE, n.pca=seq(5, by=5,to=40),xval.plot=TRUE)<o:p></o:p></span></pre>
<pre><span style="color:black"><o:p> </o:p></span></pre>
<pre><span style="color:black">but I don't get these exact increments.<o:p></o:p></span></pre>
<pre><span style="color:black"><o:p> </o:p></span></pre>
<pre><span style="color:black">So what determines the scale of the x-axis?<o:p></o:p></span></pre>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black">Any thoughts would be helpful<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:#1F497D">Dr Mark Coulson</span></b><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:#1F497D"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Arial",sans-serif;color:#1F497D">Researcher – Rivers and Lochs Institute<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-left:18.0pt"><span style="font-size:8.0pt;font-family:"Arial",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Arial",sans-serif;color:#1F497D">T: 01463 273576 / 279477<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-left:18.0pt"><span style="font-size:9.0pt;font-family:"Arial",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Arial",sans-serif;color:#1F497D">Normal working days: Tues-Friday<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-left:18.0pt"><span style="font-family:"Calibri",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><a href="http://www.inverness.uhi.ac.uk/"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;text-decoration:none"><img border="0" width="141" height="40" id="Picture_x0020_10" src="cid:image006.jpg@01D2FB30.0CE9DA20"></span></a><span style="font-size:11.0pt;color:#1F497D"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Arial",sans-serif;color:#1F497D">1 Inverness Campus<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:9.0pt;font-family:"Arial",sans-serif;color:#1F497D">IV2 5NA<o:p></o:p></span></p>
<p class="MsoNormal" style="margin-left:18.0pt"><span style="font-size:9.0pt;font-family:"Arial",sans-serif;color:#17375E"><o:p> </o:p></span></p>
<p class="MsoNormal"><a href="http://www.facebook.com/invernesscollegeuhi"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;text-decoration:none"><img border="0" width="31" height="28" id="Picture_x0020_6" src="cid:image002.png@01D2FB30.0CE79030" alt="cid:image005.png@01D05FDC.CF5914F0"></span></a><a href="https://twitter.com/ic_uhi"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;text-decoration:none"><img border="0" width="27" height="28" id="Picture_x0020_1" src="cid:image003.png@01D2FB30.0CE79030" alt="cid:image006.png@01D05FDC.CF5914F0"></span></a><span style="color:#1F497D"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><a href="http://www.inverness.uhi.ac.uk/"><span style="font-size:8.0pt;font-family:"Arial",sans-serif;color:black">www.inverness.uhi.ac.uk</span></a></span><u><span style="font-size:8.0pt;font-family:"Arial",sans-serif;color:black"><o:p></o:p></span></u></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:8.0pt;font-family:"Arial",sans-serif;color:black"><img border="0" width="164" height="55" id="_x0000_i1026" src="cid:image007.jpg@01D2FB30.0CE9DA20" alt="IIP_GOLD_19"></span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D">
<img border="0" width="75" height="90" id="Picture_x0020_4" src="cid:image008.jpg@01D2FB30.0CE9DA20" alt="CSEUK Primary (r) RGB"></span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1F497D;mso-fareast-language:EN-US"><o:p> </o:p></span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif">From:</span></b><span lang="EN-US" style="font-size:11.0pt;font-family:"Calibri",sans-serif"> adegenet-forum-bounces@lists.r-forge.r-project.org [mailto:adegenet-forum-bounces@lists.r-forge.r-project.org]
<b>On Behalf Of </b>Thibaut Jombart<br>
<b>Sent:</b> 12 July 2017 15:22<br>
<b>To:</b> Mark Coulson <coulsonmw@gmail.com><br>
<b>Cc:</b> adegenet-forum@lists.r-forge.r-project.org<br>
<b>Subject:</b> Re: [adegenet-forum] dapc on allele frequencies<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Hi Mark, <o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">in principle you could use genlight, setting the ploidy for each pool to (the number of individuals) * ploidy. It should still be quite efficient in terms of memory savings, and run decently fast for a small number of pools (<100).<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">Best<o:p></o:p></p>
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<p class="MsoNormal">Thibaut<o:p></o:p></p>
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<p class="MsoNormal"><br clear="all">
<o:p></o:p></p>
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<p class="MsoNormal"><br>
--<br>
Dr Thibaut Jombart<br>
Lecturer, Department of Infectious Disease Epidemiology, Imperial College London<br>
Head of RECON: <a href="http://repidemicsconsortium.org" target="_blank">repidemicsconsortium.org</a><br>
WHO Consultant - outbreak analysis<br>
<a href="http://sites.google.com/site/thibautjombart/" target="_blank">sites.google.com/site/thibautjombart/</a><br>
Twitter: @TeebzR<br>
+44(0)20 7594 3658<o:p></o:p></p>
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<p class="MsoNormal"><o:p> </o:p></p>
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<p class="MsoNormal">On 17 May 2017 at 16:48, Mark Coulson <<a href="mailto:coulsonmw@gmail.com" target="_blank">coulsonmw@gmail.com</a>> wrote:<o:p></o:p></p>
<blockquote style="border:none;border-left:solid #CCCCCC 1.0pt;padding:0cm 0cm 0cm 6.0pt;margin-left:4.8pt;margin-right:0cm">
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<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Hi<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">I have allele frequency data for pools of individuals (no individual genotype data) for >500,000 SNPs. I know I can do a dapc on allele frequencies directly but given this many
SNPs should I be using a ‘genlight’ object or is this only for individual genotypes?<o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto"> <o:p></o:p></p>
<p class="MsoNormal" style="mso-margin-top-alt:auto;mso-margin-bottom-alt:auto">Thanks,<o:p></o:p></p>
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<p class="MsoNormal"><br>
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<p class="MsoNormal"><o:p> </o:p></p>
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