Automatic definition of robust microbiome sub-states in longitudinal data
A peer-reviewed article of this Preprint also exists.
Author and article information
Abstract
The analysis of microbiome dynamics would allow us to elucidate patterns within microbial community evolution; however, microbiome state-transition dynamics have been scarcely studied. This is in part because a necessary first-step in such analyses has not been well-defined: how to deterministically describe a microbiome’s ”state”. Clustering in states have been widely studied, although no standard has been concluded yet. We propose a generic, domain-independent and automatic procedure to determine a reliable set of microbiome sub-states within a specific dataset, and with respect to the conditions of the study. The robustness of sub-state identification is established by the combination of diverse techniques for stable cluster verification. We reuse four distinct longitudinal microbiome datasets to demonstrate the broad applicability of our method, analysing results with different taxa subset allowing to adjust it depending on the application goal, and showing that the methodology provides a set of robust sub-states to examine in downstream studies about dynamics in microbiome.
Cite this as
2018. Automatic definition of robust microbiome sub-states in longitudinal data. PeerJ Preprints 6:e26657v1 https://doi.org/10.7287/peerj.preprints.26657v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
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This is a submission to PeerJ for review.
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Supplemental Information
Robust clustering evaluation, with HCLUST algorithm, in different datasets
From top to bottom: (1) Human gut microbiome (David2014 et al.,2014), (2) Chick gut (Ballou et al.,2016), (3) Vagina (Gajer et al.,2012), (4) Preterm infant gut (La Rosa et al.,2014).
Clusters in Chick Gut with different number of taxa, represented as Principal Coordinates graphs
Top row: default taxonomic level (i.e. species), bottom row: genus aggregation. Columns from left to right: all, dominant and non-dominant.
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Beatriz García-Jiménez conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft.
Mark D Wilkinson conceived and designed the experiments, analyzed the data, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
Source code: https://github.com/wilkinsonlab/robust-clustering-metagenomics
Output data: http://doi.org/10.5281/zenodo.167376
Funding
This work was funded by the Isaac Peral and/or Marie Curie co-fund Programme at UPM and the Fundacion BBVA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
