Abstract
In this study, we apply non-linear kernelized canonical correlation analysis (KCCA) as well as primal-dual sparse canonical correlation analysis (SCCA) to the discovery of correlations between sulphate reducing bacterial taxa and their geochemical environment in the deep biosphere. For visualization of canonical patterns, we demonstrate the applicability of the correlation plot technique on kernelized data. Finally, we provide an extension to the visual analysis by clustergrams. The presented framework and visualization tools enabled extraction of latent canonical correlation patterns between the salinity of the groundwater and the bacterial taxonomic orders Desulfobacterales, Desulfovibrionales and Clostridiales.
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Acknowledgements
Microbiology data used in this article has been generated in several earlier projects. We acknowledge financial support from Finnish Research Programme on Nuclear Waste Management KYT2010 (2006–2010) (GEOMOL project) and KYT2014 (2011–2014) Geobioinfo and GEOMICRO projects. Finnish Academy is acknowledged for funding Deep Life project (2009–2014). The work by Viivi Uurtio has been supported in part by Helsinki Doctoral Network in Information and Communication Technology HICT.
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Uurtio, V., Bomberg, M., Nybo, K., Itävaara, M., Rousu, J. (2015). Canonical Correlation Methods for Exploring Microbe-Environment Interactions in Deep Subsurface. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_25
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DOI: https://doi.org/10.1007/978-3-319-24282-8_25
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