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Himel Mallick, Ali Rahnavard, Lauren J. McIver, Siyuan Ma, Yancong Zhang, Long H. Nguyen, Timothy L. Tickle, George Weingart, Boyu Ren, Emma Schwager and 10 more
It is challenging to associate features such as human health outcomes, diet, environmental conditions, or other metadata to microbial community measurements, due in part to their quantitative properties. Microbiome multi-omics are typically noisy, sparse (zero-inflated), high-dimensional, extremely non-normal, and often in the form of count or compositional measurements. Here we introduce an optimized combination of novel and established methodology to assess multivariable association of microbial community features with complex metadata in population-scale observational studies. Our approach, MaAsLin 2 (Microbiome Multivariable Associations with Linear Models), uses generalized linear and mixed models to accommodate a wide variety of modern epidemiological studies, including cross-sectional and longitudinal designs, as well as a variety of data types (e.g., counts and relative abundances) with or without covariates and repeated measurements. To construct this method, we conducted a large-scale evaluation of a broad range of scenarios under which straightforward identification of meta-omics associations can be challenging. These simulation studies reveal that MaAsLin 2's linear model preserves statistical power in the presence of repeated measures and multiple covariates, while accounting for the nuances of meta-omics features and controlling false discovery. We also applied MaAsLin 2 to a microbial multi-omics dataset from the Integrative Human Microbiome (HMP2) project which, in addition to reproducing established results, revealed a unique, integrated landscape of inflammatory bowel diseases (IBD) across multiple time points and omics profiles.
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Mallick, H., Rahnavard, A., McIver, L. J., Ma, S., Zhang, Y., Nguyen, L. H., Tickle, T. L., Weingart, G., Ren, B., Schwager, E., Chatterjee, S., Thompson, K. N., Wilkinson, J. E., Subramanian, A., Lu, Y., Waldron, L., Paulson, J. N., Franzosa, E. A., Bravo, H. C., & Huttenhower, C. (2021). Multivariable association discovery in population-scale meta-omics studies. PLoS Computational Biology, 17(11), e1009442. https://doi.org/10.1371/journal.pcbi.1009442
Mallick, Himel, et al. "Multivariable association discovery in population-scale meta-omics studies." PLoS Computational Biology, vol. 17, no. 11, 2021, pp. e1009442, https://doi.org/10.1371/journal.pcbi.1009442.
Mallick, Himel, Ali Rahnavard, Lauren J. McIver, Siyuan Ma, Yancong Zhang, Long H. Nguyen, Timothy L. Tickle, et al. "Multivariable association discovery in population-scale meta-omics studies." PLoS Computational Biology 17, no. 11 (2021): e1009442. https://doi.org/10.1371/journal.pcbi.1009442.
Mallick H., Rahnavard A., McIver L. J. [и др.] Multivariable association discovery in population-scale meta-omics studies // PLoS Computational Biology. — 2021. — Vol. 17, № 11. — P. e1009442. — URL: https://doi.org/10.1371/journal.pcbi.1009442
@article{mallick2021multivariable,
title = {{Multivariable association discovery in population-scale meta-omics studies}},
author = {Himel Mallick and Ali Rahnavard and Lauren J. McIver and Siyuan Ma and Yancong Zhang and Long H. Nguyen and Timothy L. Tickle and George Weingart and Boyu Ren and Emma Schwager and Suvo Chatterjee and Kelsey N. Thompson and Jeremy E. Wilkinson and Ayshwarya Subramanian and Yiren Lu and Levi Waldron and Joseph Nathaniel Paulson and Eric A. Franzosa and Héctor Corrada Bravo and Curtis Huttenhower},
journal = {PLoS Computational Biology},
year = {2021},
volume = {17},
number = {11},
pages = {e1009442},
doi = {10.1371/journal.pcbi.1009442},
url = {https://doi.org/10.1371/journal.pcbi.1009442}
}TY - JOUR AU - Mallick, Himel AU - Rahnavard, Ali AU - McIver, Lauren J. AU - Ma, Siyuan AU - Zhang, Yancong AU - Nguyen, Long H. AU - Tickle, Timothy L. AU - Weingart, George AU - Ren, Boyu AU - Schwager, Emma AU - Chatterjee, Suvo AU - Thompson, Kelsey N. AU - Wilkinson, Jeremy E. AU - Subramanian, Ayshwarya AU - Lu, Yiren AU - Waldron, Levi AU - Paulson, Joseph Nathaniel AU - Franzosa, Eric A. AU - Bravo, Héctor Corrada AU - Huttenhower, Curtis TI - Multivariable association discovery in population-scale meta-omics studies JO - PLoS Computational Biology VL - 17 IS - 11 SP - e1009442 PY - 2021 DA - 2021/11/16 DO - 10.1371/journal.pcbi.1009442 UR - https://doi.org/10.1371/journal.pcbi.1009442 ER -
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