A Bayesian method for detecting pairwise associations in compositional data.
A Bayesian method for detecting pairwise associations in compositional data.
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DOI：
10.1371/journal.pcbi.1005852
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发表时间：
2017-11
影响因子：
4.3
通讯作者：
Huttenhower C
中科院分区：
文献类型：
--
作者：
Schwager E;Mallick H;Ventz S;Huttenhower C
成分数据由比例向量组成，比例向量根据未观察到的计数标准化为常数和。由于归一化的信息损失，求和约束使得对无约束特征之间的相关性的推断具有挑战性。然而，这种相关性在包括生态学在内的领域中具有长期的兴趣。我们提出了一种新的贝叶斯框架（BAnOCC：贝叶斯分析的成分协方差）估计稀疏精度矩阵通过LASSO先验。由此产生的后验，MCMC采样产生的，允许不确定性量化的精度矩阵，包括相关矩阵的任何功能。我们还使用了一阶泰勒展开近似的转换，从未观察到的计数的组成，以调查什么样的特性的未观察到的计数可以使相关性或多或少难以推断。在模拟数据集上，我们表明BAnOCC推断出真实的网络以及以前的方法，同时提供后验推理的优势。更大和更真实的模拟数据集进一步表明，BAnOCC表现良好，由I型和II型错误率测量。最后，我们将BAnOCC应用于来自人类微生物组项目的微生物生态学数据集，该数据集除了再现既定的生态结果外，还揭示了变形菌在多个不同栖息地中独特的，基于竞争的作用。来自许多领域的数据主要以比例的形式提供，也称为成分，这对识别基础系统中组件之间的相互作用施加了数学约束。特别是，相关性不能直接从比例或产生相关性的计数数据中计算出来。解决这一困难的方法通常是通过对基础数据或相关性的分布进行强有力的假设来实现的，而这些假设反过来又往往会阻止量化相关性估计中的不确定性。我们开发了一个统计模型（BAnOCC：成分协方差的贝叶斯分析），既估计计数或比例之间的相关性，又为每个相关性提供后验分布，量化估计的不确定性。BAnOCC在控制模拟数据中的假阳性数量方面做得很好，并且可以实际应用于广泛的比例数据类型。
Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats. Data from many fields are available primarily in the form of proportions, also referred to as compositions, which impose mathematical constraints on identifying interactions among components in the underlying systems. In particular, correlations cannot be calculated directly from proportions or from count data that give rise to them. Methods that work around this difficulty generally do so by imposing strong assumptions about the distribution of underlying data or associated correlations, and these in turn often prevent quantifying uncertainty in the resulting estimates of correlation. We developed a statistical model (BAnOCC: Bayesian Analysis of Compositional Covariance) that both estimates correlations between counts or proportions and provides a posterior distribution for each correlation that quantifies how uncertain the estimate is. BAnOCC does well at controlling the number of false positives in simulated data and can be practically applied to a wide range of proportional data types.
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影响因子：
12.3
作者：
Hogg JS;Hu FZ;Janto B;Boissy R;Hayes J;Keefe R;Post JC;Ehrlich GD
通讯作者：
Ehrlich GD
影响因子：
17.1
作者：
Gajer P;Brotman RM;Bai G;Sakamoto J;Schütte UM;Zhong X;Koenig SS;Fu L;Ma ZS;Zhou X;Abdo Z;Forney LJ;Ravel J
通讯作者：
Ravel J
影响因子：
4.3
作者：
Faust K;Sathirapongsasuti JF;Izard J;Segata N;Gevers D;Raes J;Huttenhower C
通讯作者：
Huttenhower C
DOI：
10.1007/bf01031393
发表时间：
1981-01-01
期刊：
JOURNAL OF THE INTERNATIONAL ASSOCIATION FOR MATHEMATICAL GEOLOGY
影响因子：
--
作者：
AITCHISON, J
通讯作者：
AITCHISON, J
DOI：
10.1126/science.1224820
发表时间：
2012-10-05
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Arthur JC;Perez-Chanona E;Mühlbauer M;Tomkovich S;Uronis JM;Fan TJ;Campbell BJ;Abujamel T;Dogan B;Rogers AB;Rhodes JM;Stintzi A;Simpson KW;Hansen JJ;Keku TO;Fodor AA;Jobin C
通讯作者：
Jobin C