<html><head><meta http-equiv="Content-Type" content="text/html; charset=us-ascii"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">Hello Matthew,<div class=""><br class=""></div><div class="">I'm glad you found the solution and thank you for posting the answer to this forum! You are correct that it's not necessary to retain all 900 PCs for downstream analysis since there generally will be a long tail of <1% variance explained. I'm also forwarding it to the adegenet forum as this also concerns non-poppr users. </div><div class=""><div><br class=""></div><div>Best,</div><div>Zhian</div><div><br class=""></div><div><br class=""><blockquote type="cite" class=""><div class="">On Feb 5, 2020, at 08:57 , Matthew Haas <<a href="mailto:haasx092@umn.edu" class="">haasx092@umn.edu</a>> wrote:</div><br class="Apple-interchange-newline"><div class=""><div dir="ltr" class="">Hi Zhian,<div class=""><br class=""></div><div class="">Thank you for such a quick response. I believe I found the root cause last night. I have ~900 individuals, so there are 900 eigenvalues. PLiNK calculates the % variance based on the top 20 values only, while poppr uses all of them. When I altered my code to only use the top 20 eigenvalues generated by poppr, I get the same result. This is somewhat unfamiliar territory for me, but I think it is not necessary to use all of the eigenvalues when many of them are as small as they are.</div><div class=""><br class=""></div><div class="">Thanks again.</div><div class=""><br class=""></div><div class="">Kind regards,</div><div class="">Matthew</div></div><br class=""><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Feb 5, 2020 at 10:49 AM Zhian Kamvar <<a href="mailto:zkamvar@gmail.com" class="">zkamvar@gmail.com</a>> wrote:<br class=""></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div style="overflow-wrap: break-word;" class="">Hello Matthew,<div class=""><br class=""></div><div class="">I'm afraid that I don't have a good answer for you at the moment. I'm not familiar with how PLiNK processes the GBS data for PCA. Without seeing your code, it's also difficult to figure out what may be going on, so I can only make guesses. One common mistake is centering and scaling the data beforehand (which is default in ade4). It's a common procedure for PCA to account for different variable types, but is not necessary where all the data are allele counts; it can dampen the signal. If that's not the issue, then try making sure you have the most recent version of adegenet/ade4 (poppr doesn't perform PCA, ade4 does).</div><div class=""><br class=""></div><div class="">Hope that helps.</div><div class=""><br class=""></div><div class="">Best,</div><div class="">Zhian</div><div class=""><div class=""><br class=""><blockquote type="cite" class=""><div class="">On Feb 1, 2020, at 16:16 , Matthew Haas <<a href="mailto:haasx092@umn.edu" target="_blank" class="">haasx092@umn.edu</a>> wrote:</div><br class=""><div class=""><div dir="ltr" class="">Hello fellow poppr users,<div class=""><br class=""></div><div class="">I am confused by some results that I am getting from poppr. I have attached two PCA plots: one was created with PLINK and explains a greater proportion of phenotypic variation (PC1=23%). According to poppr, the first PC explains only about 6% of the variation.. The PLINK results make more biological sense, but I am troubled that poppr isn't in better agreement. The samples are clustering as expected, but I would think the % variation should be in better agreement. Initially, I attributed different methods of handling missing data as the underlying cause, but after imputing missing SNPs, I am seeing the same plots. I am not 100% sure I have imputation figured out, but I now think there must be some other cause that I'm not thinking of.. I also filter to remove indels and retain only balletic SNPs. I set the minor allele frequency to 0.05 and have tried a few different thresholds for Hardy-Weinberg inclusion.</div><div class=""><br class=""></div><div class="">Is there some majorly obvious parameter I'm not considering? I am new to population genetics--having previously worked as a plan geneticist/bioinformatician. One last point that might be important: I am working with an outcrossed, so I expect a fair amount of heterozygosity in my population. How much does mating system matter to poppr?</div><div class=""><br class=""></div><div class="">Thank you all in advance for your help.</div><div class=""><br class=""></div><div class="">Kind regards,</div><div class="">Matthew</div></div><div class=""><br class=""></div>
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<span id="gmail-m_5230981773510099278cid:ed256649-efef-4962-b25f-0d3dc99b29f7" class=""><200130_main_gbs_imputed.pdf></span><span id="gmail-m_5230981773510099278cid:a94106e2-6a65-4df5-bfb3-e35aa7bb32a9" class=""><200130_main_GBS_imputed_PCA_poppr.pdf></span></div></blockquote></div><br class=""></div></div></blockquote></div><br clear="all" class=""><div class=""><br class=""></div>-- <br class=""><div dir="ltr" class="gmail_signature"><div dir="ltr" class=""><div class=""><div dir="ltr" class=""><div class=""><div dir="ltr" class="">Matthew Haas, PhD</div><div dir="ltr" class="">Post-doctoral research associate<br class=""><div class="">Department of Agronomy and Plant Genetics</div><div class="">University of Minnesota</div><div class="">1991 Upper Buford Circle</div><div class="">411 Borlaug Hall</div><div class="">Saint Paul, MN 55108</div><div class=""><br class=""></div><div class="">Mobile: (651) 356-9305</div></div></div></div></div></div></div>
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