VARIABLE SELECTION FOR SPARSE DIRICHLET-MULTINOMIAL REGRESSION WITH AN APPLICATION TO MICROBIOME DATA ANALYSIS.
VARIABLE SELECTION FOR SPARSE DIRICHLET-MULTINOMIAL REGRESSION WITH AN APPLICATION TO MICROBIOME DATA ANALYSIS.
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DOI：
10.1214/12-aoas592
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发表时间：
2013-03-01
期刊：
影响因子：
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通讯作者：
Li H
中科院分区：
文献类型：
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作者：
Chen J;Li H
随着下一代测序技术的发展，研究人员现在已经能够使用直接测序来研究微生物组组成，其输出是每个微生物组样本的细菌分类群计数。微生物组研究的目标之一是将微生物组组成与环境协变量联系起来。我们建议使用狄利克雷多项式（DM）回归模型对分类单元计数进行建模，以解释观察到的计数的过度分散。 DM 回归模型可用于使用似然比检验来测试类群组成和协变量之间的关联。然而，当协变量的数量很大时，多次测试可能会导致功效损失。为了处理问题的高维性，我们开发了一种惩罚似然方法来估计回归参数，并通过施加稀疏组惩罚来选择变量，以鼓励组级和组内稀疏性。这种变量选择程序可以导致选择相关协变量及其相关细菌分类群。开发了一种有效的块坐标下降算法来解决优化问题。我们进行了广泛的模拟，以证明稀疏 DM 回归可以比忽略过度分散或仅考虑比例的模型更好地识别微生物组相关的协变量。我们在评估营养摄入对人类肠道微生物组组成影响的数据集分析中展示了我们的方法的强大功能。我们的结果清楚地表明，营养摄入量与人类肠道微生物组密切相关。
With the development of next generation sequencing technology, researchers have now been able to study the microbiome composition using direct sequencing, whose output are bacterial taxa counts for each microbiome sample. One goal of microbiome study is to associate the microbiome composition with environmental covariates. We propose to model the taxa counts using a Dirichlet-multinomial (DM) regression model in order to account for overdispersion of observed counts. The DM regression model can be used for testing the association between taxa composition and covariates using the likelihood ratio test. However, when the number of the covariates is large, multiple testing can lead to loss of power. To deal with the high dimensionality of the problem, we develop a penalized likelihood approach to estimate the regression parameters and to select the variables by imposing a sparse group penalty to encourage both group-level and within-group sparsity. Such a variable selection procedure can lead to selection of the relevant covariates and their associated bacterial taxa. An efficient block-coordinate descent algorithm is developed to solve the optimization problem. We present extensive simulations to demonstrate that the sparse DM regression can result in better identification of the microbiome-associated covariates than models that ignore overdispersion or only consider the proportions. We demonstrate the power of our method in an analysis of a data set evaluating the effects of nutrient intake on human gut microbiome composition. Our results have clearly shown that the nutrient intake is strongly associated with the human gut microbiome.