Abstract
Fundamental endeavour to understand microbiome and its functions starts with detecting which microbes are present in the samples and continues with comparing different samples and finding similar based on their community compositions. Pervasive method to accomplish these steps is clustering. However clustering brings number of possibilities regarding algorithms, parameters, distance/similarity metrics, etc., that produce different outcomes making it hard to interpret results. The study presented here examines the stability of clusters in the context of various beta diversity metrics applied on human microbiome samples. We explored the effects of 24 different diversity metrics on clustering outcomes and their impact on the accuracy of the clustering of microbiome samples. To overcome obscure results coming from individual clusterings that rely on distinct beta diversity metrics we employed two ensemble approaches to integrate results of individual clusterings. Obtained results on human microbiome data imply that ensemble clustering approaches produce stable results in reconstructing clusters that correspond to the different host and body habitat.
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This work was partly supported by Serbian Ministry of Education and Science (Project III 44006).
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Brdar, S., Crnojević, V. (2017). Ensemble Approaches for Stable Assessment of Clusters in Microbiome Samples. In: Bracciali, A., Caravagna, G., Gilbert, D., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2016. Lecture Notes in Computer Science(), vol 10477. Springer, Cham. https://doi.org/10.1007/978-3-319-67834-4_16
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DOI: https://doi.org/10.1007/978-3-319-67834-4_16
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