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A unified framework for unconstrained and constrained ordination of microbiome read count data

(2019) PLOS ONE. 14(2).
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Abstract
Explorative visualization techniques provide a first summary of microbiome read count datasets through dimension reduction. A plethora of dimension reduction methods exists, but many of them focus primarily on sample ordination, failing to elucidate the role of the bacterial species. Moreover, implicit but often unrealistic assumptions underlying these methods fail to account for overdispersion and differences in sequencing depth, which are two typical characteristics of sequencing data. We combine log-linear models with a dispersion estimation algorithm and flexible response function modelling into a framework for unconstrained and constrained ordination. The method is able to cope with differences in dispersion between taxa and varying sequencing depths, to yield meaningful biological patterns. Moreover, it can correct for observed technical confounders, whereas other methods are adversely affected by these artefacts. Unlike distance-based ordination methods, the assumptions underlying our method are stated explicitly and can be verified using simple diagnostics. The combination of unconstrained and constrained ordination in the same framework is unique in the field and facilitates microbiome data exploration. We illustrate the advantages of our method on simulated and real datasets, while pointing out flaws in existing methods. The algorithms for fitting and plotting are available in the R-package RCM.
Keywords
LATENT VARIABLE MODELS, MULTIVARIATE ANALYSES

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Citation

Please use this url to cite or link to this publication:

MLA
Hawinkel, Stijn, et al. “A Unified Framework for Unconstrained and Constrained Ordination of Microbiome Read Count Data.” PLOS ONE, vol. 14, no. 2, 2019, doi:10.1371/journal.pone.0205474.
APA
Hawinkel, S., Kerckhof, F.-M., Bijnens, L., & Thas, O. (2019). A unified framework for unconstrained and constrained ordination of microbiome read count data. PLOS ONE, 14(2). https://doi.org/10.1371/journal.pone.0205474
Chicago author-date
Hawinkel, Stijn, Frederiek-Maarten Kerckhof, Luc Bijnens, and Olivier Thas. 2019. “A Unified Framework for Unconstrained and Constrained Ordination of Microbiome Read Count Data.” PLOS ONE 14 (2). https://doi.org/10.1371/journal.pone.0205474.
Chicago author-date (all authors)
Hawinkel, Stijn, Frederiek-Maarten Kerckhof, Luc Bijnens, and Olivier Thas. 2019. “A Unified Framework for Unconstrained and Constrained Ordination of Microbiome Read Count Data.” PLOS ONE 14 (2). doi:10.1371/journal.pone.0205474.
Vancouver
1.
Hawinkel S, Kerckhof F-M, Bijnens L, Thas O. A unified framework for unconstrained and constrained ordination of microbiome read count data. PLOS ONE. 2019;14(2).
IEEE
[1]
S. Hawinkel, F.-M. Kerckhof, L. Bijnens, and O. Thas, “A unified framework for unconstrained and constrained ordination of microbiome read count data,” PLOS ONE, vol. 14, no. 2, 2019.
@article{8616989,
  abstract     = {{Explorative visualization techniques provide a first summary of microbiome read count datasets through dimension reduction. A plethora of dimension reduction methods exists, but many of them focus primarily on sample ordination, failing to elucidate the role of the bacterial species. Moreover, implicit but often unrealistic assumptions underlying these methods fail to account for overdispersion and differences in sequencing depth, which are two typical characteristics of sequencing data. We combine log-linear models with a dispersion estimation algorithm and flexible response function modelling into a framework for unconstrained and constrained ordination. The method is able to cope with differences in dispersion between taxa and varying sequencing depths, to yield meaningful biological patterns. Moreover, it can correct for observed technical confounders, whereas other methods are adversely affected by these artefacts. Unlike distance-based ordination methods, the assumptions underlying our method are stated explicitly and can be verified using simple diagnostics. The combination of unconstrained and constrained ordination in the same framework is unique in the field and facilitates microbiome data exploration. We illustrate the advantages of our method on simulated and real datasets, while pointing out flaws in existing methods. The algorithms for fitting and plotting are available in the R-package RCM.}},
  articleno    = {{e0205474}},
  author       = {{Hawinkel, Stijn and Kerckhof, Frederiek-Maarten and Bijnens, Luc and Thas, Olivier}},
  issn         = {{1932-6203}},
  journal      = {{PLOS ONE}},
  keywords     = {{LATENT VARIABLE MODELS,MULTIVARIATE ANALYSES}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{20}},
  title        = {{A unified framework for unconstrained and constrained ordination of microbiome read count data}},
  url          = {{http://doi.org/10.1371/journal.pone.0205474}},
  volume       = {{14}},
  year         = {{2019}},
}

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