<div dir="ltr">Hi,<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="gmail_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$grp)</div></div><div><br></div><div><div>dapc1</div><div><span style="white-space:pre"> </span>#################################################</div><div><span style="white-space:pre"> </span># Discriminant Analysis of Principal Components #</div><div><span style="white-space:pre"> </span>#################################################</div><div>class: dapc</div><div>$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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