D-MANOVA: fast distance-based multivariate analysis of variance for large-scale microbiome association studies
D-MANOVA: fast distance-based multivariate analysis of variance for large-scale microbiome association studies
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DOI：
10.1093/bioinformatics/btab498
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发表时间：
2021-07-13
期刊：
影响因子：
5.8
通讯作者：
Zhang, Xianyang
中科院分区：
文献类型：
--
作者：
Chen, Jun;Zhang, Xianyang
基于距离的置换多元方差分析（PERMANOVA）已被广泛用于检验微生物组与感兴趣的协变量之间的关联。通过置换来确定统计学意义，对于大样本量来说，这在计算上是非常密集的。随着诸如美国肠道项目（AGP）等大规模微生物组研究变得越来越流行，非常需要一种计算高效的PERMANOVA版本。为了实现这一目标，我们推导了PERMANOVA伪F统计量的渐近分布，并基于卡方近似提供了解析的P值计算。我们表明，即使在中等样本量下，渐近P值也接近PERMANOVA的P值。此外，它比无置换方法MDMR更准确，且快一个数量级。我们在AGP数据集上展示了我们的D - MANOVA程序的使用。
PERMANOVA (permutational multivariate analysis of variance based on distances) has been widely used for testing the association between the microbiome and a covariate of interest. Statistical significance is established by permutation, which is computationally intensive for large sample sizes. As large-scale microbiome studies, such as American Gut Project (AGP), become increasingly popular, a computationally efficient version of PERMANOVA is much needed. To achieve this end, we derive the asymptotic distribution of the PERMANOVA pseudo-F statistic and provide analytical P-value calculation based on chi-square approximation. We show that the asymptotic P-value is close to the PERMANOVA P-value even under a moderate sample size. Moreover, it is more accurate and an order-of-magnitude faster than the permutation-free method MDMR. We demonstrated the use of our procedure D-MANOVA on the AGP dataset.