Extrapolation of urn models via poissonization: accurate measurements of the microbial unknown.
Extrapolation of urn models via poissonization: accurate measurements of the microbial unknown.
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DOI：
10.1371/journal.pone.0021105
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Reeder J
中科院分区：
文献类型：
--
作者：
Lladser ME;Gouet R;Reeder J
高通量并行方法测序微生物群落的可用性正在以前所未有的速度增加我们对微生物世界的了解。虽然大多数注意力集中在确定下限的多样性，即在环境中存在的不同物种的总数，这个数量的严格界限可能是高度不确定的，因为环境的一小部分可能是由大量的不同物种。为了更好地评估仍然未知的内容，我们建议预测属于未采样类的环境部分。建模样本绘制与替换的彩球从一个未知的组合物的骨灰盒，并在唯一的假设下，仍然有未发现的物种，我们表明，有条件的无偏预测和准确的预测区间（对数尺度的恒定长度）是可能的环境中属于未采样类的部分。我们的预测是基于泊松化的论点，我们已经实现了我们所谓的嵌入算法。在固定的即非随机化的样本大小中，该算法导致对原始样本的子样本的非常准确的预测。我们量化了固定样本量对我们的预测区间的影响，并测试了我们的方法和文献中发现的其他方法对模拟环境的影响，我们设计这些模拟环境时考虑了来自人类肠道和手部微生物群的数据集。我们的方法适用于任何数据集，可以被概念化为一个样本与替换从一个瓮。特别是，它可以被应用于，例如，量化在随机RNA库中结合位点问题的所有看不见的解决方案的比例，或者重新评估对某个恐怖组织的监视，预测它在下一次攻击中部署新战术的条件概率。
The availability of high-throughput parallel methods for sequencing microbial communities is increasing our knowledge of the microbial world at an unprecedented rate. Though most attention has focused on determining lower-bounds on the -diversity i.e. the total number of different species present in the environment, tight bounds on this quantity may be highly uncertain because a small fraction of the environment could be composed of a vast number of different species. To better assess what remains unknown, we propose instead to predict the fraction of the environment that belongs to unsampled classes. Modeling samples as draws with replacement of colored balls from an urn with an unknown composition, and under the sole assumption that there are still undiscovered species, we show that conditionally unbiased predictors and exact prediction intervals (of constant length in logarithmic scale) are possible for the fraction of the environment that belongs to unsampled classes. Our predictions are based on a Poissonization argument, which we have implemented in what we call the Embedding algorithm. In fixed i.e. non-randomized sample sizes, the algorithm leads to very accurate predictions on a sub-sample of the original sample. We quantify the effect of fixed sample sizes on our prediction intervals and test our methods and others found in the literature against simulated environments, which we devise taking into account datasets from a human-gut and -hand microbiota. Our methodology applies to any dataset that can be conceptualized as a sample with replacement from an urn. In particular, it could be applied, for example, to quantify the proportion of all the unseen solutions to a binding site problem in a random RNA pool, or to reassess the surveillance of a certain terrorist group, predicting the conditional probability that it deploys a new tactic in a next attack.
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影响因子：
11
作者：
Quince, Christopher;Curtis, Thomas P.;Sloan, William T.
通讯作者：
Sloan, William T.
DOI：
10.1073/pnas.1000162107
发表时间：
2010-04-06
影响因子：
11.1
作者：
Fierer, Noah;Lauber, Christian L.;Knight, Rob
通讯作者：
Knight, Rob
DOI：
10.1073/pnas.0807920105
发表时间：
2008-11-18
影响因子：
11.1
作者：
Fierer, Noah;Hamady, Micah;Knight, Rob
通讯作者：
Knight, Rob
影响因子：
4.5
作者：
Mao, Chang Xuan;Lindsay, Bruce G.
通讯作者：
Lindsay, Bruce G.
影响因子：
2.7
作者：
GOOD, IJ
通讯作者：
GOOD, IJ