Advertisement

Host Phenotype Prediction from Differentially Abundant Microbes Using RoDEO

  • Anna Paola Carrieri
  • Niina Haiminen
  • Laxmi ParidaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10477)

Abstract

Metagenomics is the study of metagenomes which are mixtures of genetic material from several organisms. Metagenomic sequencing is increasingly used in human and animal health, food safety, and environmental studies. In these high-dimensional (metagenomic) data, the phenotype of the host organism, e.g., human, may not be obvious to detect and then the ability to predict it becomes a powerful analytic tool. For example, consider predicting the disease status of an individual from their gut microbiome.

In this study, we compare various normalization methods for metagenomic count data and their impact on phenotype prediction. The methods include RoDEO, Robust Differential Expression Operator, originally developed for gene expression studies. The best prediction accuracy is observed for RoDEO-processed count data with linear kernel support vector machines in most cases, for a variety of real datasets including human, mouse, and environmental samples.

We also address the problem of identifying the most relevant microbial features that could give insight into the structure and function of the differential communities observed between phenotypes. Interestingly, we obtain similar or better phenotype prediction accuracy with a small subset of features as with the complete set of sequenced features.

Keywords

Metagenomics Phenotype prediction Differential abundance Feature selection 

References

  1. 1.
    Anastas, P., et al.: 2020 visions. Nature 463(7277), 26–32 (2010). https://www.nature.com/nature/journal/v463/n7277/full/463026a.html
  2. 2.
    Paulson, J.N., Stine, O.C., Bravo, H.C., Pop, M.: Robust methods for differential abundance analysis in marker gene surveys. Nat. Methods 10, 1200–1202 (2013)CrossRefGoogle Scholar
  3. 3.
    Parida, L., Haiminen, N., Haws, D., Suchodolski, J.: Host trait prediction of metagenomic data for topology-based visualization. In: Natarajan, R., Barua, G., Patra, M.R. (eds.) ICDCIT 2015. LNCS, vol. 8956, pp. 134–149. Springer, Cham (2015). doi: 10.1007/978-3-319-14977-6_8 Google Scholar
  4. 4.
    Jonsson, V., Österlund, T., Nerman, O., Kristiansson, E.: Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics. BMC Genomics 17(78), 1–14 (2016)Google Scholar
  5. 5.
    Haiminen, N., Klaas, M., Zhou, Z., Utro, F., Cormican, P., Didion, T., Jensen, C., Mason, C.E., Barth, S., Parida, L.: Comparative exomics of Phalaris cultivars under salt stress. BMC Genomics 15(6), 1–12 (2014)Google Scholar
  6. 6.
    Klaas, M., Haiminen, N., Grant, J., Cormican, P., Finnan, J., Krishna, S., Utro, F., Vellani, T., Parida, L., Barth, S.: Characterizing differentially expressed genes under flooding and drought stress in the biomass grasses Phalaris arundinacea and Dactylis glomerata. Under submission (2017)Google Scholar
  7. 7.
    Karlsson, F.H., Tremaroli, V., Nookaew, I., Bergström, G., Behre, C.J., Fagerberg, B., Nielsen, J., Bäckhed, F.: Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013)CrossRefGoogle Scholar
  8. 8.
    Ross, E.M., Moate, P.J., Marett, L.C., Cocks, B.G., Hayes, B.: Metagenomic predictions: from microbiome to complex health and environmental phenotypes in humans and cattle. PLoS ONE 8, e73056 (2013)CrossRefGoogle Scholar
  9. 9.
    Pasolli, E., Tin, D., Truong, F.K., Waldron, L., Segata, N.: Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLoS Comput. Biol. 12(7), e1004977 (2016)CrossRefGoogle Scholar
  10. 10.
    Love, M.I., Huber, W., Anders, S.: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15(12), 550 (2014)CrossRefGoogle Scholar
  11. 11.
    Weimann, A., Mooren, K., Frank, J., Pope, P.B., Bremges, A., McHardy, A.C., Segata, N.: From genomes to phenotypes: traitar, the microbial trait analyzer. mSystems 1(6), 1–19 (2016)Google Scholar
  12. 12.
    Ho, T.K.: Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282 (1995)Google Scholar
  13. 13.
    Statnikov, A., Henaff, M., Narendra, V., Konganti, K., Li, Z., Yang, L., Pei, Z., Blaser, M.J., Aliferis, C.F., Alekseyenko, A.V.: A comprehensive evaluation of multicategory classification methods for microbiomic data. Microbiome 1, 11 (2013)CrossRefGoogle Scholar
  14. 14.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. JMLR 3(11), 57–82 (2013)zbMATHGoogle Scholar
  15. 15.
    Metcalf, J.L., Xu, Z.Z., Weiss, S., Lax, S., Van Treuren, W., Hyde, E.R., Song, S.J., Amir, A., Larsen, P., Sangwan, N., Haarmann, D., Humphrey, G.C., Ackermann, G., Thompson, L.R., Lauber, C., Bibat, A., Nicholas, C., Gebert, M.J., Petrosino, J.F., Reed, S.C., Gilbert, J.A., Lynne, A.M., Bucheli, S.R., Carter, D.O., Knight, R.: Microbial community assembly and metabolic function during mammalian corpse decomposition. Science 351(6269), 158–162 (2016)CrossRefGoogle Scholar
  16. 16.
    Caporaso, J.G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F.D., Costello, E.K., Fierer, N., Gonzalez Peña, A.G., Goodrich, J.K., Gordon, J.I., Huttley, G.A., Kelley, S.T., Knights, D., Koenig, J.E., Ley, R.E., Lozupone, C.A., McDonald, D., Muegge, B.D., Pirrung, M., Reeder, J., Sevinsky, J.R., Turnbaugh, P.J., Walters, W.A., Widmann, J., Yatsunenko, T., Zaneveld, J., Knight, R.: QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7(5), 335–336 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anna Paola Carrieri
    • 1
  • Niina Haiminen
    • 2
  • Laxmi Parida
    • 2
    Email author
  1. 1.IBM Research UKWarringtonUK
  2. 2.IBM T.J. Watson Research CenterYorktown HeightsUSA

Personalised recommendations