Multivariable association discovery in population-scale meta-omics studies

Multivariable association discovery in population-scale meta-omics studies

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 (zeroinflated), high-dimensional, extremely n...

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Bibliographic Details
Main Authors: Bravo, H.C (Author), Chatterjee, S. (Author), Franzosa, E.A (Author), Huttenhower, C. (Author), Lu, Y. (Author), Ma, S. (Author), Mallick, H. (Author), McIver, L.J (Author), Nguyen, L.H (Author), Paulson, J.N (Author), Rahnavard, A. (Author), Ren, B. (Author), Schwager, E.H (Author), Subramanian, A. (Author), Thompson, K.N (Author), Tickle, T.L (Author), Waldron, L. (Author), Weingart, G. (Author), Wilkinson, J.E (Author), Zhang, Y. (Author)
Format: Article
Language:English
Published: Public Library of Science 2021
Subjects:
Article
biology
cohort analysis
Computational Biology
computer simulation
Computer Simulation
controlled study
dysbiosis
Gastrointestinal Microbiome
genetics
human
Humans
inflammatory bowel disease
Inflammatory Bowel Diseases
intestine flora
measurement
metabolism
metadata
microbial community
microbiome
multiomics
multivariate analysis
Multivariate Analysis
observational study
pathology
population abundance
population research
simulation
statistical model
Online Access:View Fulltext in Publisher

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