The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Analysis of compositions of microbiomes with bias correction
Open accessAbstract
Differential abundance (DA) analysis of microbiome data continues to be a challenging problem due to the complexity of the data. In this article we define the notion of “sampling fraction” and demonstrate a major hurdle in performing DA analysis of microbiome data is the bias introduced by differences in the sampling fractions across samples. We introduce a methodology called Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework. This formulation makes a fundamental advancement in the field because, unlike the existing methods, it (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement.
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Cites
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
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Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C. A., Turnbaugh, P. J., Fierer, N., & Knight, R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000080107 · Cited by 9,711
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Research works printed but not linked: 2
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Cited by
Derosa, L., Routy, B., Thomas, A. M., Iebba, V., Zalcman, G., Friard, S., Mazieres, J., Audigier-Valette, C., Moro-Sibilot, D., Goldwasser, F., et al. (2022) · Nature Medicine · doi:10.1038/s41591-021-01655-5 · Cited by 685
Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions
Narunsky-Haziza, L., Sepich-Poore, G. D., Livyatan, I., Asraf, O., Martino, C., Nejman, D., Gavert, N., Stajich, J. E., Amit, G., González, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.005 · Cited by 538
“We next applied differential abundance (DA) testing (Lin and Peddada, 2020) and ML between stage I and stage IV tumor mycobiomes.”
MethodLee, K. A., Thomas, A. M., Bolte, L. A., Björk, J. R., de Ruijter, L. K., Armanini, F., Asnicar, F., Blanco-Miguez, A., Board, R., Calbet-Llopart, N., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01695-5 · Cited by 464
Analysis of microbial compositions: a review of normalization and differential abundance analysis
Lin, H., & Peddada, S. D. (2020) · npj Biofilms and Microbiomes · doi:10.1038/s41522-020-00160-w · Cited by 341
“Extensive simulation studies using Poisson-Gamma model as well as some based on real data, were performed in 20 to evaluate the performance of various normalization methods.”
BackgroundMethodInfluentialSelf-citationscCODA is a Bayesian model for compositional single-cell data analysis
Büttner, M., Ostner, J., Müller, C. L., Theis, F. J., & Schubert, B. (2021) · Nature Communications · doi:10.1038/s41467-021-27150-6 · Cited by 388
Metadata
- Authors
- Lin, H., & Peddada, S. D.
- Venue
- Nature Communications
- Published
- 2020-07-14
- Volume
- 11
- Issue
- 1
- Pages
- 3514-3514
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3043062835
- Cited by
- 2,104
- References
- 33
- FWCI
- 48.06
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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