Limits...
Microbial community pattern detection in human body habitats via ensemble clustering framework.

Yang P, Su X, Ou-Yang L, Chua HN, Li XL, Ning K - BMC Syst Biol (2014)

Bottom Line: Therefore, these methods could not capture the real-world underlying microbial patterns effectively.From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern.The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively.

Results: To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender.

Conclusions: In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.

Show MeSH
The algorithm of Meta-EC for microbial community pattern detection.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4290728&req=5

Figure 4: The algorithm of Meta-EC for microbial community pattern detection.

Mentions: We summarized the whole algorithm in Figure 4.


Microbial community pattern detection in human body habitats via ensemble clustering framework.

Yang P, Su X, Ou-Yang L, Chua HN, Li XL, Ning K - BMC Syst Biol (2014)

The algorithm of Meta-EC for microbial community pattern detection.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4290728&req=5

Figure 4: The algorithm of Meta-EC for microbial community pattern detection.
Mentions: We summarized the whole algorithm in Figure 4.

Bottom Line: Therefore, these methods could not capture the real-world underlying microbial patterns effectively.From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern.The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial patterns effectively.

Results: To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender.

Conclusions: In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.

Show MeSH