Phylogeny-guided microbiome OTU-specific association test (POST)

Abstract

Background The relationship between host conditions and microbiome profiles, typically characterized by operational taxonomic units (OTUs), contains important information about the microbial role in human health. Traditional association testing frameworks are challenged by the high dimensionality and sparsity of typical microbiome profiles. Phylogenetic information is often incorporated to address these challenges with the assumption that evolutionarily similar taxa tend to behave similarly. However, this assumption may not always be valid due to the complex effects of microbes, and phylogenetic information should be incorporated in a data-supervised fashion. Results In this work, we propose a local collapsing test called phylogeny-guided microbiome OTU-specific association test (POST). In POST, whether or not to borrow information and how much information to borrow from the neighboring OTUs in the phylogenetic tree are supervised by phylogenetic distance and the outcome-OTU association. POST is constructed under the kernel machine framework to accommodate complex OTU effects and extends kernel machine microbiome tests from community level to OTU level. Using simulation studies, we show that when the phylogenetic tree is informative, POST has better performance than existing OTU-level association tests. When the phylogenetic tree is not informative, POST achieves similar performance as existing methods. Finally, in real data applications on bacterial vaginosis and on preterm birth, we find that POST can identify similar or more outcome-associated OTUs that are of biological relevance compared to existing methods. Conclusions Using POST, we show that adaptively leveraging the phylogenetic information can enhance the selection performance of associated microbiome features by improving the overall true-positive and false-positive detection. We developed a user friendly R package POSTm which is freely available on CRAN (https://CRAN.R-project.org/package=POSTm).

mi-Mic: a novel multi-layer statistical test for microbiota-disease associations

“…Another assumption common to most existing methods, such as LEfSe, ANCOM, and DeSeq2 is the independence of tests over all taxa. However, often, similar taxa exhibit similar behavior, as extensively discussed [ 18 , 38 – 40 ] and further shown below. These intrinsic taxonomic relationships challenge the independence assumption.…”

“…The main current differential analysis methods include LEfSe [ 11 ], ANCOM [ 12 ], ANCOM-BC2 [ 13 ], DeSeq2 [ 14 ], ALDEx2 [ 15 , 16 ], and LINDA [ 17 ]. Other less used methods were also proposed [ 18 – 21 ]. LEfSe employs the Kruskal-Wallis sum-rank test [ 22 ] to identify features with significant differential abundance, followed by Wilcoxon rank-sum tests [ 23 , 24 ] for biological consistency.…”

miMic - a novel multi-layer statistical test for microbiome disease

“…is the independence of tests over all taxa. However, often, similar taxa exhibit similar behavior, as extensively discussed [34,35,36,16] and further shown below. These intrinsic taxonomic relationships challenge the independence assumption.…”

“…and LINDA [15]. Other less used methods were also proposed [16,17,18,19]. LEfSe employs the Kruskal-Wallis sum-rank test [20] to identify features with significant differential abundance, followed by Wilcoxon rank-sum tests [21,22] for biological consistency.…”

Supervised learning and model analysis with compositional data

“…For (i), it has been shown that modern machine learning methods are capable of creating highly predictive models by using microbiome data as covariates and phenotypes as responses [e.g., 26 – 29 ]. In particular, several approaches have been proposed where kernels are used to incorporate prior information [ 30 , 31 ], as a way to utilize the compositional structure [ 32 – 34 ] and to construct association tests [ 35 – 37 ]. Our proposed framework extends these works by providing new post-analysis techniques (e.g., the compositional feature influence) that respect the compositional structure.…”