scholarly journals Automatic definition of robust microbiome sub-states in longitudinal data

2018 ◽  
Author(s):  
Beatriz García-Jiménez ◽  
Mark D Wilkinson
Keyword(s):  
Microbial Community ◽  
Longitudinal Data ◽  
State Transition ◽  
Transition Dynamics ◽  
Stable Cluster ◽  
Broad Applicability ◽  
State Identification ◽  
Community Evolution ◽  
Definition Of ◽  
Domain Independent

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.

scholarly journals Automatic definition of robust microbiome sub-states in longitudinal data

2018 ◽  
Author(s):  
Beatriz García-Jiménez ◽  
Mark D Wilkinson
Keyword(s):  
Microbial Community ◽  
Longitudinal Data ◽  
State Transition ◽  
Transition Dynamics ◽  
Stable Cluster ◽  
Broad Applicability ◽  
State Identification ◽  
Community Evolution ◽  
Definition Of ◽  
Domain Independent

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.

scholarly journals Robust and automatic definition of microbiome states

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6657 ◽  
Author(s):  
Beatriz García-Jiménez ◽  
Mark D. Wilkinson
Keyword(s):  
Microbial Populations ◽  
Ecological Niches ◽  
Rigorous Approach ◽  
State Identification ◽  
Individual Variations ◽  
Community Evolution ◽  
Final Output ◽  
Definition Of ◽  
Domain Independent ◽  
Monitoring Response

Analysis of microbiome dynamics would allow elucidation of patterns within microbial community evolution under a variety of biologically or economically important circumstances; however, this is currently hampered in part by the lack of rigorous, formal, yet generally-applicable approaches to discerning distinct configurations of complex microbial populations. Clustering approaches to define microbiome “community state-types” at a population-scale are widely used, though not yet standardized. Similarly, distinct variations within a state-type are well documented, but there is no rigorous approach to discriminating these more subtle variations in community structure. Finally, intra-individual variations with even fewer differences will likely be found in, for example, longitudinal data, and will correlate with important features such as sickness versus health. We propose an automated, generic, objective, domain-independent, and internally-validating procedure to define statistically distinct microbiome states within datasets containing any degree of phylotypic diversity. Robustness of state identification is objectively established by a combination of diverse techniques for stable cluster verification. To demonstrate the efficacy of our approach in detecting discreet states even in datasets containing highly similar bacterial communities, and to demonstrate the broad applicability of our method, we reuse eight distinct longitudinal microbiome datasets from a variety of ecological niches and species. We also demonstrate our algorithm’s flexibility by providing it distinct taxa subsets as clustering input, demonstrating that it operates on filtered or unfiltered data, and at a range of different taxonomic levels. The final output is a set of robustly defined states which can then be used as general biomarkers for a wide variety of downstream purposes such as association with disease, monitoring response to intervention, or identifying optimally performant populations.

scholarly journals Microbial Community Evolution Is Significantly Impacted by the Use of Calcium Isosaccharinic Acid as an Analogue for the Products of Alkaline Cellulose Degradation

PLoS ONE ◽  
2016 ◽  
Vol 11 (11) ◽  
pp. e0165832 ◽  
Author(s):  
Isaac A. Kyeremeh ◽  
Christopher J. Charles ◽  
Simon P. Rout ◽  
Andrew P. Laws ◽  
Paul N. Humphreys
Keyword(s):  
Microbial Community ◽  
Cellulose Degradation ◽  
Community Evolution ◽  
Isosaccharinic Acid

State Transition Dynamics

2011 ◽  
pp. 32-55 ◽  
Author(s):  
Vincenzo Manca ◽  
Giuditta Franco ◽  
Giuseppe Scollo
Keyword(s):  
Dynamical Systems ◽  
State Transition ◽  
Molecular Computing ◽  
Discrete Models ◽  
Classical Dynamics ◽  
Transition Systems ◽  
Transition Dynamics ◽  
Models Of Computation ◽  
Simple Set ◽  
State Transition Systems

Classical dynamics concepts are analysed in the basic mathematical setting of state transition systems where time and space are both completely discrete and no structure is assumed on the state’s space. Interesting relationships between attractors and recurrence are identified and some features of chaos are expressed in simple, set theoretic terms. String dynamics is proposed as a unifying concept for dynamical systems arising from discrete models of computation, together with illustrative examples. The relevance of state transition systems and string dynamics is discussed from the perspective of molecular computing.

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