ipPCA

Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and structure. Conclusions: The EigenDev heuristic is robust to sampling and is thus superior for detecting structure in large datasets. The application of EigenDev to the ipPCA algorithm improves the estimation of the number of subpopulations and the individual assignment accuracy, especially for very large and complex datasets. Furthermore, we have demonstrated that the structure resolved by this approach complements parametric analysis, allowing a much more comprehensive account of population structure. The new version of the ipPCA software with EigenDev incorporated can be downloaded from http://www4a.biotec.or.th/GI/tools/ippca.

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References in zbMATH (referenced in 2 articles )

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  1. Yushi Liu, Toru Nyunoya, Shuguang Leng, Steven A Belinsky, Yohannes Tesfaigzi, Shannon Bruse: Softwares and methods for estimating genetic ancestry in human populations (2013) not zbMATH
  2. Limpiti, Tulaya; Intarapanich, Apichart; Assawamakin, Anunchai; Shaw, Philip J.; Wangkumhang, Pongsakorn; Piriyapongsa, Jittima; Ngamphiw, Chumpol; Tongsima, Sissades: Study of large and highly stratified population datasets by combining iterative pruning principal component analysis and STRUCTURE (2011) ioport