@article{
 title = {Automatic definition of robust microbiome sub-states in longitudinal data},
 type = {article},
 year = {2018},
 keywords = {Clustering,Longitudinal dataset,Machine Learning,Metagenomics,Microbiome,Sub-states},
 pages = {e26657v1},
 volume = {6},
 websites = {https://doi.org/10.7287/peerj.preprints.26657v1},
 month = {3},
 id = {8d4d3cd8-ce00-3d2b-ad84-1ea7fa6fc109},
 created = {2018-10-15T08:36:22.612Z},
 file_attached = {false},
 profile_id = {17c87d5d-2470-32d7-b273-0734a1d9195f},
 last_modified = {2018-10-15T08:36:22.612Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.7287/peerj.preprints.26657v1},
 source_type = {article},
 private_publication = {false},
 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.
},
 bibtype = {article},
 author = {García-Jiménez, Beatriz and Wilkinson, Mark D},
 doi = {10.7287/peerj.preprints.26657v1},
 journal = {PeerJ Preprints}
}

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