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A powerful adaptive microbiome-based association test for microbial association signals with diverse sparsity levels
Journal of Genetics and Genomics ( IF 5.9 ) Pub Date : 2021-08-16 , DOI: 10.1016/j.jgg.2021.08.002
Han Sun 1 , Xiaoyun Huang 2 , Lingling Fu 1 , Ban Huo 3 , Tingting He 4 , Xingpeng Jiang 4
Affiliation  

The dysbiosis of microbiome may have negative effects on a host phenotype. The microbes related to the host phenotype are regarded as microbial association signals. Recently, statistical methods based on microbiome-phenotype association tests have been extensively developed to detect these association signals. However, the currently available methods do not perform well to detect microbial association signals when dealing with diverse sparsity levels (i.e., sparse, low sparse, non-sparse). Actually, the real association patterns related to different host phenotypes are not unique. Here, we propose a powerful and adaptive microbiome-based association test to detect microbial association signals with diverse sparsity levels, designated as MiATDS. In particular, we define probability degree to measure the associations between microbes and the host phenotype and introduce the adaptive weighted sum of powered score tests by considering both probability degree and phylogenetic information. We design numerous simulation experiments for the task of detecting association signals with diverse sparsity levels to prove the performance of the method. We find that type I error rates can be well-controlled and MiATDS shows superior efficiency on the power. By applying to real data analysis, MiATDS displays reliable practicability too. The R package is available at https://github.com/XiaoyunHuang33/MiATDS.



中文翻译:

一种强大的基于自适应微生物组的关联测试,用于具有不同稀疏度水平的微生物关联信号

微生物组的生态失调可能对宿主表型产生负面影响。与宿主表型相关的微生物被视为微生物关联信号。最近,基于微生物组-表型关联测试的统计方法已被广泛开发以检测这些关联信号。然而,当前可用的方法在处理不同的稀疏度水平(即稀疏、低稀疏、非稀疏)时不能很好地检测微生物关联信号。实际上,与不同宿主表型相关的真正关联模式并不是唯一的。在这里,我们提出了一种强大且自适应的基于微生物组的关联测试,以检测具有不同稀疏度水平的微生物关联信号,称为 MiATDS。特别是,我们定义概率度来衡量微生物与宿主表型之间的关联,并通过考虑概率度和系统发育信息来引入幂分检验的自适应加权和。我们为检测具有不同稀疏度级别的关联信号的任务设计了许多模拟实验,以证明该方法的性能。我们发现 I 类错误率可以得到很好的控制,并且 MiATDS 显示出卓越的功率效率。MiATDS应用于真实数据分析,也显示出可靠的实用性。R 包可在 https://github.com/XiaoyunHuang33/MiATDS 获得。我们为检测具有不同稀疏度级别的关联信号的任务设计了许多模拟实验,以证明该方法的性能。我们发现 I 类错误率可以得到很好的控制,并且 MiATDS 显示出卓越的功率效率。MiATDS应用于真实数据分析,也显示出可靠的实用性。R 包可在 https://github.com/XiaoyunHuang33/MiATDS 获得。我们为检测具有不同稀疏度级别的关联信号的任务设计了许多模拟实验,以证明该方法的性能。我们发现 I 类错误率可以得到很好的控制,并且 MiATDS 显示出卓越的功率效率。MiATDS应用于真实数据分析,也显示出可靠的实用性。R 包可在 https://github.com/XiaoyunHuang33/MiATDS 获得。

更新日期:2021-10-19
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