Skip to main content
SpringerLink
Log in
Book cover

International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics

CIBB 2016: Computational Intelligence Methods for Bioinformatics and Biostatistics pp 199–208Cite as

Ensemble Approaches for Stable Assessment of Clusters in Microbiome Samples

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10477))

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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Zhou, J., He, Z., Yang, Y., Deng, Y., Tringe, S.G., Alvarez-Cohen, L.: High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. MBio 6(1), e02288–14 (2015)

    Article  Google Scholar 

  2. Mendoza, M.L.Z., Sicheritz-Pontn, T., Gilbert, M.T.P.: Environmental genes and genomes: understanding the differences and challenges in the approaches and software for their analyses. Briefings Bioinform. 6(5), 745–758 (2015)

    Article  Google Scholar 

  3. He, Y., et al.: Stability of operational taxonomic units: an important but neglected property for analyzing microbial diversity. Microbiome 3(1), 20 (2015)

    Article  Google Scholar 

  4. Kuczynski, J., et al.: Microbial community resemblance methods differ in their ability to detect biologically relevant patterns. Nature Methods 7(10), 813–819 (2010)

    Article  Google Scholar 

  5. Koren, O., et al.: A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets. PLoS Comput. Biol. 9(1), e1002863 (2013)

    Article  Google Scholar 

  6. Yang, P., et al.: Microbial community pattern detection in human body habitats via ensemble clustering framework. BMC Syst. Biol. 8(Suppl 4), S7 (2014)

    Article  Google Scholar 

  7. Legendre, P., Cáceres, M.: Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol. Lett. 16(8), 951–963 (2013)

    Article  Google Scholar 

  8. Ng, A., Jordan, M., Weiss, Y.: On spectral clustering: analysis and an algorithm. Adv. Neural Inf. Process. Syst. 2, 849–856 (2002)

    Google Scholar 

  9. Brdar, S., Crnojević, V., Zupan, B.: Integrative clustering by nonnegative matrix factorization can reveal coherent functional groups from gene profile data. IEEE J. Biomed. Health Inf. 19(2), 698–708 (2015)

    Article  Google Scholar 

  10. Monti, S., Tamayo, P., Golub, T.: Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 52(1), 91–118 (2003)

    Article  MATH  Google Scholar 

  11. Caporaso, J.G., et al.: Moving pictures of the human microbiome. Genome Biol. 12(5), R50 (2011)

    Article  MathSciNet  Google Scholar 

  12. Wilke, A., et al.: A RESTful API for accessing microbial community data for MG-RAST. PLoS Comput. Biol. 11(1), e1004008 (2015)

    Article  Google Scholar 

  13. Caporaso, J.G., et al.: QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7(5), 335–336 (2010)

    Article  Google Scholar 

  14. Rideout, J.R., et al.: Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences. PeerJ 2, e545 (2014)

    Article  Google Scholar 

  15. Edgar, R.: Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19), 2460–2461 (2010)

    Article  Google Scholar 

  16. DeSantis, T.Z., et al.: Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72(7), 5069–5072 (2006)

    Article  Google Scholar 

  17. Rosenberg, A., Hirschberg, J.: V-measure: a conditional entropy-based external cluster evaluation measure. In: EMNLP-CoNLL, vol. 7 (2007)

    Google Scholar 

  18. Hubert, L., Phipps, A.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)

    Article  MATH  Google Scholar 

  19. Wagner, S., Wagner, D.: Comparing clusterings: an overview. Universität Karlsruhe, Fakultät für Informatik Karlsruh (2007)

    Google Scholar 

Download references

Acknowledgements

This work was partly supported by Serbian Ministry of Education and Science (Project III 44006).

Author information

Authors and Affiliations

  1. BioSense Institute, University of Novi Sad, Dr Zorana Djindjica 1, Novi Sad, Serbia

    Sanja Brdar & Vladimir Crnojević

Authors
  1. Sanja Brdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vladimir Crnojević
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sanja Brdar .

Editor information

Editors and Affiliations

  1. Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom

    Andrea Bracciali

  2. School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

    Giulio Caravagna

  3. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, United Kingdom

    David Gilbert

  4. Department of Management and Innovation Systems DISA-MIS, University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67834-4_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67833-7

  • Online ISBN: 978-3-319-67834-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation