The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data
Open accessAbstract
This paper presents new biostatistical methods for the analysis of microbiome data based on a fully parametric approach using all the data. The Dirichlet-multinomial distribution allows the analyst to calculate power and sample sizes for experimental design, perform tests of hypotheses (e.g., compare microbiomes across groups), and to estimate parameters describing microbiome properties. The use of a fully parametric model for these data has the benefit over alternative non-parametric approaches such as bootstrapping and permutation testing, in that this model is able to retain more information contained in the data. This paper details the statistical approaches for several tests of hypothesis and power/sample size calculations, and applies them for illustration to taxonomic abundance distribution and rank abundance distribution data using HMP Jumpstart data on 24 subjects for saliva, subgingival, and supragingival samples. Software for running these analyses is available.
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Citations
Cites
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828
UniFrac: a New Phylogenetic Method for Comparing Microbial Communities
Lozupone, C., & Knight, R. (2005) · Applied and Environmental Microbiology · doi:10.1128/aem.71.12.8228-8235.2005 · Cited by 8,227
Cited by
A framework for human microbiome research
The Human Microbiome Project Consortium (2012) · Nature · doi:10.1038/nature11209 · Cited by 2,650
Population-level analysis of gut microbiome variation
Falony, G., Joossens, M., Vieira-Silva, S., Wang, J., Darzi, Y., Faust, K., Kurilshikov, A., Bonder, M. J., Valles-Colomer, M., Vandeputte, D., et al. (2016) · Science · doi:10.1126/science.aad3503 · Cited by 2,400
Shotgun metagenomics, from sampling to analysis
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J., & Segata, N. (2017) · Nature Biotechnology · doi:10.1038/nbt.3935 · Cited by 2,202
Best practices for analysing microbiomes
Knight, R., Vrbanac, A., Taylor, B. C., Aksenov, A., Callewaert, C., Debelius, J., Gonzalez, A., Kosciolek, T., McCall, L.-I., McDonald, D., et al. (2018) · Nature Reviews Microbiology · doi:10.1038/s41579-018-0029-9 · Cited by 2,030
Analysis of composition of microbiomes: a novel method for studying microbial composition
Mandal, S., Van Treuren, W., White, R. A., Eggesbø, M., Knight, R., & Peddada, S. D. (2015) · Microbial Ecology in Health & Disease · doi:10.3402/mehd.v26.27663 · Cited by 1,942
Metadata
- Authors
- La Rosa, P. S., Brooks, J. P., Deych, E., Boone, E. L., Edwards, D. J., Wang, Q., Sodergren, E., Weinstock, G., & Shannon, W. D.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-12-20
- Volume
- 7
- Issue
- 12
- Pages
- e52078
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2034189943
- Cited by
- 325
- References
- 2
- FWCI
- 13.07
- Type
- journal article
- Language
- en
- Open access
- gold
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- La Rosa, P. S., Brooks, J. P., Deych, E., Boone, E. L., Edwards, D. J., Wang, Q., Sodergren, E., Weinstock, G., & Shannon, W. D. (2012). Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data. PLoS ONE. https://doi.org/10.1371/journal.pone.0052078
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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