Multivariable Association Discovery in Population-scale Meta-omics Studies

@article{Mallick2021MultivariableAD,
  title={Multivariable Association Discovery in Population-scale Meta-omics Studies},
  author={Himel Mallick and Ali Rahnavard and L. McIver and Siyuan Ma and Yancong Zhang and L. Nguyen and Timothy L. Tickle and G. Weingart and Boyu Ren and Emma Schwager and Suvo Chatterjee and Kelsey N. Thompson and J. Wilkinson and Ayshwarya Subramanian and Yiren Lu and L. Waldron and J. Paulson and E. Franzosa and H. Bravo and C. Huttenhower},
  journal={bioRxiv},
  year={2021}
}
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… Expand
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