A Distribution-Free Model for Longitudinal Metagenomic Count Data.

A Distribution-Free Model for Longitudinal Metagenomic Count Data.
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纵向宏基因组计数数据的无分布模型。

DOI:
10.3390/genes13071183
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发表时间:
2022-07-01
期刊:
影响因子:
3.5
通讯作者:
--
中科院分区:
生物学3区
文献类型:
--
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近十年来,纵向元基因组学得到了广泛的研究,为理解微生物动力学提供了有价值的见解。通过重复测量,可以观察到每个受试者之间的相关性。然而,以前假设独立相关性的方法可能会出现不正确的推断。此外,考虑样本内相关性的方法可能不适用于计数数据。我们提出了一种无分布的方法,即CorZIDF,它扩展了现有的方法来对相关的零膨胀元基因组计数数据进行建模,为检测重要性特征提供了一个强大而准确的解决方案。该方法可以处理不同的工作相关结构,而无需指定计数数据的每个裕度分布。通过仿真研究,证明了CorZIDF在选择工作相关结构进行重复测量学习时的稳健性,从而提高了估计效率。我们还使用两个真实的数据集对四种方法进行了比较,新提出的方法识别出了更多以前相关研究中报道的独特特征。
Longitudinal metagenomics has been widely studied in the recent decade to provide valuable insight for understanding microbial dynamics. The correlation within each subject can be observed across repeated measurements. However, previous methods that assume independent correlation may suffer from incorrect inferences. In addition, methods that do account for intra-sample correlation may not be applicable for count data. We proposed a distribution-free approach, namely CorrZIDF, which extends the current method to model correlated zero-inflated metagenomic count data, offering a powerful and accurate solution for detecting significance features. This method can handle different working correlation structures without specifying each margin distribution of the count data. Through simulation studies, we have shown the robustness of CorrZIDF when selecting a working correlation structure for repeated measures studies to enhance the efficiency of estimation. We also compared four methods using two real datasets, and the new proposed method identified more unique features that were reported previously on the relevant research.
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