<div dir="ltr">Yes I overlooked that, but +1 to Zhian's suggestions. <div><br></div><div>Also a sign I need to get more familiar with poppr ;) </div><div><br></div><div>Best</div><div>Thibaut</div></div><div class="gmail_extra"><br clear="all"><div><div class="gmail_signature" data-smartmail="gmail_signature"><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</div></div>
<br><div class="gmail_quote">On 4 August 2017 at 17:22, Zhian Kamvar <span dir="ltr"><<a href="mailto:zkamvar@gmail.com" target="_blank">zkamvar@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="auto"><div>Hi Phillip,</div><div><br></div><div>I would agree with Thibaut to first check to make sure you don't have duplicated genotypes for each cluster in your data. If you use the poppr package, you can use nmll() to check the number of unique genotypes and mll() to view their assignment. </div><div><br></div><div>One thing I notice in your DAPC output is the fact that you are retaining ~95% of the variance for these artificial groups, which may suggest that you are over-fitting the model (although, I would defer to Thibaut for confirmation on this). This could result in minute differences within the groups as compared to between groups, giving the appearance of all within group points stacked on top of one another if the differences between groups is sufficiently large. </div><div><br></div><div>My suggestion is to try reducing the number of retained PCs for the DAPC. </div><div><br><div>Sent from my iPhone</div></div><div><div class="h5"><div><br>On Aug 4, 2017, at 08:10, Thibaut Jombart <<a href="mailto:thibautjombart@gmail.com" target="_blank">thibautjombart@gmail.com</a>> wrote:<br><br></div><blockquote type="cite"><div><div dir="ltr">Dear Philip, <div><br></div><div>it looks like all individuals from a given cluster are exactly at the same location, which would be the case if they are identical genotypes. </div><div><br></div><div>Assuming your genind object is 'x', can you check what this returns:</div><div><br></div><div><div> table(table(apply(genind2df(<wbr>x, sep="", usepop = FALSE), 1, paste, collapse = "")))</div></div><div><br></div><div>This will derive the frequencies of haplotypes in the data. I think there is something to do this more elegantly in poppr but I will let Zhian comment if this is the case.</div><div><br></div><div>Best</div><div>Thibaut</div><div><br></div><div><br></div></div><div class="gmail_extra"><br clear="all"><div><div class="m_-4176915412785446491gmail_signature" data-smartmail="gmail_signature"><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/<wbr>thibautjombart/</a><br>Twitter: @TeebzR<br><a href="tel:+44%2020%207594%203658" value="+442075943658" target="_blank">+44(0)20 7594 3658</a></div></div>
<br><div class="gmail_quote">On 22 July 2017 at 05:19, Phillip Skipwith <span dir="ltr"><<a href="mailto:pskipwith@gmail.com" target="_blank">pskipwith@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div class="m_-4176915412785446491HOEnZb"><div class="m_-4176915412785446491h5"><div dir="ltr"><div><div class="m_-4176915412785446491m_-1522149890038328153gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr">Hi,</div></div></div></div></div><div class="gmail_quote"><div dir="ltr"><div><br></div><div>I'm pretty new to Adegenet, but I have been through the tutorials and have been more or less successful getting it to work on my empirical data. This is a phylogenomic dataset of 83 individuals from eight clades and 4,268 loci (I'm using 4,035 SNPs for ordination, etc.). I realize the sample size is small, but this is hard-earned field data. The problem arises when I'm trying to use dapc after find.clusters on the below genind object.</div><div><br></div><div><div>gen.struct</div><div>/// GENIND OBJECT /////////</div><div><br></div><div> // 83 individuals; 4,035 loci; 8,341 alleles; size: 4.5 Mb</div><div><br></div><div> // Basic content</div><div> @tab: 83 x 8341 matrix of allele counts</div><div> @loc.n.all: number of alleles per locus (range: 2-4)</div><div> @loc.fac: locus factor for the 8341 columns of @tab</div><div> @all.names: list of allele names for each locus</div><div> @ploidy: ploidy of each individual (range: 2-2)</div><div> @type: codom</div><div> @call: read.structure(file = "final_Struct_good_maybe.str", n.ind = 83, </div><div> n.loc = 4035, onerowperind = F, col.lab = 1, col.pop = 2, </div><div> row.marknames = 0, ask = F)</div><div><br></div><div> // Optional content</div><div> @pop: population of each individual (group size range: 2-27)</div><div><div class="m_-4176915412785446491m_-1522149890038328153m_6034531476113309615gmail_signature"><div dir="ltr"><div dir="ltr"><div><br></div><div><div>grp <- find.clusters(gen.struct, max.n.clust=35)</div><div><br></div><div>Choose the number PCs to retain (>=1): </div><div>80</div><div>Choose the number of clusters (>=2: </div><div>9</div><div><br></div><div><div>dapc1 <- dapc(gen.struct, grp$grp)</div></div><div><br></div><div><div>dapc1</div><div><span style="white-space:pre-wrap"> </span>##############################<wbr>###################</div><div><span style="white-space:pre-wrap"> </span># Discriminant Analysis of Principal Components #</div><div><span style="white-space:pre-wrap"> </span>##############################<wbr>###################</div><div>class: dapc</div><div>$call: dapc.genind(x = gen.struct, pop = grp$grp)</div><div><br></div><div>$n.pca: 60 first PCs of PCA used</div><div>$n.da: 4 discriminant functions saved</div><div>$var (proportion of conserved variance): 0.946</div><div><br></div><div>$eig (eigenvalues): 182000 71010 34130 20710 16790 ...</div><div><br></div><div> vector length content </div><div>1 $eig 8 eigenvalues </div><div>2 $grp 83 prior group assignment </div><div>3 $prior 9 prior group probabilities </div><div>4 $assign 83 posterior group assignment</div><div>5 $pca.cent 8341 centring vector of PCA </div><div>6 $pca.norm 8341 scaling vector of PCA </div><div>7 $pca.eig 82 eigenvalues of PCA </div><div><br></div><div> data.frame nrow ncol content </div><div>1 $tab 83 60 retained PCs of PCA </div><div>2 $means 9 60 group means </div><div>3 $loadings 60 4 loadings of variables </div><div>4 $ind.coord 83 4 coordinates of individuals (principal components)</div><div>5 $grp.coord 9 4 coordinates of groups </div><div>6 $posterior 83 9 posterior membership probabilities </div><div>7 $pca.loadings 8341 60 PCA loadings of original variables </div><div>8 $var.contr 8341 4 contribution of original variables </div></div><div><br></div><div><div>Choose the number PCs to retain (>=1): </div><div>60</div><div>Choose the number discriminant functions to retain (>=1): </div><div>4</div></div><div><br></div><div><div>scatter(dapc1, scree.da = T)</div></div></div><div><br></div><div>The end result is a plot with the centroid points for each of the clusters but not the individuals. I know there is probably something simple that I'm missing or there's something intrinsically wrong with my code and or data. I've perused the forum for similar issues and nothing is quite spot on to what I'm asking here.</div><div><br></div><div>Any help would be greatly appreciated.</div><div><br></div><div>Best,</div><div><br></div><div>Phillip</div><div><br></div></div></div></div></div>
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