Département de Biologie, Université Laval, Québec, QC, Canada, G1V 0A6.
Mol Ecol Resour. 2016 Jan;16(1):13-6. doi: 10.1111/1755-0998.12480.
Anderson & Barry (Molecular Ecology Resources, 2015, 10, 1020-1030) compared a reprogrammed version of flock (Duchesne & Turgeon , Molecular Ecology Resources, 2009, 9, 1333-1344), flockture, to a particular model of structure (Pritchard , Genetics, 2000, 155, 945-959) that they propose is equivalent to flock, a non-MCMC, non-Bayesian algorithm. They conclude that structure performs better than flockture at clustering individuals from simulated populations with very low level of differentiation (FST c. 0.008) based on 15 microsatellites or 96 SNPs. We rather consider that both algorithms failed, with proportions of correct allocations lower than 50%. The authors also noted the slightly better performance of flockture with SNPs at intermediate FST values (c. 0.02-0.04) but did not comment. Finally, we disagree with the way the processing time of each program was compared. When compared on the basis of a run leading to a clustering solution, the main output of any clustering algorithm, flock, is, as users can readily experience, much faster. In all, we feel that flock performs at least as well as structure as a clustering algorithm. Moreover, flock has two major assets: high speed and clear, well validated, rules to estimate K, the number of populations. It thus provides a valuable addition to the set of tools at the disposal of the many researchers dealing with real empirical data sets.
安德森和巴里（《分子生态学资源》，2015 年，10，1020-1030）比较了 flock 的一个重新编程版本 flockture（杜切恩和特伦，《分子生态学资源》，2009 年，9，1333-1344），与他们提出的一种特定的结构模型（普里查德，《遗传学》，2000 年，155，945-959），该模型他们认为是 flock 的等价物，是一种非 MCMC、非贝叶斯算法。他们的结论是，基于 15 个微卫星或 96 个 SNP，结构在聚类具有非常低分化水平（FST c. 0.008）的模拟种群个体方面表现优于 flockture。我们认为这两个算法都失败了，正确分配的比例低于 50%。作者还注意到 flockture 在中间 FST 值（c. 0.02-0.04）的 SNP 上的性能略有提高，但未做评论。最后，我们不同意比较每个程序处理时间的方式。当根据导致聚类解决方案的运行进行比较时，任何聚类算法 flock 的主要输出，如用户可以轻松体验到的那样，速度要快得多。总之，我们认为 flock 作为一种聚类算法的性能至少与 structure 一样好。此外，flock 有两个主要优势：高速和清晰、经过良好验证的规则，用于估计 K，即种群数量。因此，它为处理大量真实经验数据集的许多研究人员提供了一种有价值的工具。
