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<div style="direction: ltr;font-family: Tahoma;color: #000000;font-size: 10pt;">Hi David,
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yes, the conversion from genlight to genind is still on the TODO list. The reason for this not existing so far is that I designed genlights to be used when genind objects cannot - ie. when the RAM would explode to store data as genind.<br>
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Cheers<br>
Thibaut <br>
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<div style="direction: ltr;" id="divRpF931354"><font face="Tahoma" size="2" color="#000000"><b>From:</b> adegenet-forum-bounces@lists.r-forge.r-project.org [adegenet-forum-bounces@lists.r-forge.r-project.org] on behalf of David Dayan [snook91901@gmail.com]<br>
<b>Sent:</b> 22 October 2015 17:34<br>
<b>To:</b> adegenet-forum@lists.r-forge.r-project.org<br>
<b>Subject:</b> [adegenet-forum] Resampling loci from a large genlight<br>
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<div dir="ltr">Hi all,
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<div>I'm looking to sample loci from a single population in a large genlight object (13850 loci, 872 diploid individuals), and convert this genlight information to a genind object (to use across other packages/adegenet functions). </div>
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<div>Simulated example (calculating He using the Hs() function from 100 sampled loci):</div>
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<div>> simdata<-glSim(10,500, ploidy=2)</div>
<div>> pop(simdata)<-rep(c("a","b"),5)</div>
<div>> Hs(df2genind(as.data.frame(seppop(simdata)$a[,sample(1:500, 100)]),sep="/t",ploidy=2))</div>
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<div>This is problematic because the df2genind(as.data.frame(genlight)) method doesn't seem to work for diploid data (see below).<br>
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<div>> sim2sampled_genind<-df2genind(as.data.frame(seppop(simdata)$a[,sample(1:500, 100)]),sep="/t",ploidy=2)</div>
<div>> head(sim2sampled_genind@tab)</div>
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<div>This produces a genind that stores data as binary and therefore cannot represent my actual data (diploid + missing data).</div>
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<div>I realize that starting from a genind object and using the @tab slot will solve this, but resampling from a genind this size is exceptionally slow and my ultimate goal is to work this script into a bootstrapping procedure:<br>
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<div>So, (a) any advice on converting genlight to genind for diploid data? or (b) is there a better way to sample loci for bootstrapping that is computationally efficient/fast?</div>
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<div>Thanks,</div>
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<div>David Dayan</div>
<div>Rosenstiel School of Marine and Atmospheric Science</div>
<div>University of Miami</div>
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