Compositionally aware phylogenetic beta-diversity measures better resolve microbiomes associated with phenotype.
Published in Msystems, 2022
Recommended citation: Martino, et al. (2022). "Compositionally aware phylogenetic beta-diversity measures better resolve microbiomes associated with phenotype.." Msystems, 7(3):e00050-22. https://doi.org/10.1128/msystems.00050-22
Microbiome data have several specific characteristics (sparsity and compositionality) that introduce challenges in data analysis. The integration of prior information regarding the data structure, such as phylogenetic structure and repeated-measure study designs, into analysis, is an effective approach for revealing robust patterns in microbiome data. Past methods have addressed some but not all of these challenges and features: for example, robust principal-component analysis (RPCA) addresses sparsity and compositionality; compositional tensor factorization (CTF) addresses sparsity, compositionality, and repeated measure study designs; and UniFrac incorporates phylogenetic information. Here we introduce a strategy of incorporating phylogenetic information into RPCA and CTF. The resulting methods, phylo-RPCA, and phylo-CTF, provide substantial improvements over state-of-the-art methods in terms of discriminatory power of underlying clustering ranging from the mode of delivery to adult human lifestyle. We demonstrate quantitatively that the addition of phylogenetic information improves effect size and classification accuracy in both data-driven simulated data and real microbiome data.
Recommended citation: Martino C, McDonald D, Cantrell K, Dilmore AH, Vázquez-Baeza Y, Shenhav L, Shaffer JP, Rahman G, Armstrong G, Allaband C, Song SJ. Compositionally aware phylogenetic beta-diversity measures better resolve microbiomes associated with phenotype. Msystems. 2022 Jun 28;7(3):e00050-22.