期刊
BIOINFORMATICS
卷 31, 期 15, 页码 2461-2468出版社
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv183
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资金
- National Institutes of Health T32 training grants [T32 AI055435, T32 HL758627]
- Penn Center for AIDS Research [5P30AI045008-15]
- National Institutes of Health [U01 HL098957, K24-DK078228, UH3-DK083981, GM097505, CA127334]
- NATIONAL CANCER INSTITUTE [R01CA127334] Funding Source: NIH RePORTER
- NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [U01HL098957, R01HL113252, T32HL007586, U01HL112712] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ALLERGY AND INFECTIOUS DISEASES [T32AI055435, P30AI045008] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [UH3DK083981, K24DK078228] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM097505] Funding Source: NIH RePORTER
Motivation: The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA. Results: We present a framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a novel method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared (omega(2)). From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study.
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