Canonical correlation methods for exploring microbe-environment interactions in deep subsurface

Viivi Uurtio; Malin Bomberg; Kristian Nybo; Merja Itävaara; Juho Rousu

doi:10.1007/978-3-319-24282-8_25

Canonical correlation methods for exploring microbe-environment interactions in deep subsurface

Viivi Uurtio, Malin Bomberg, Kristian Nybo, Merja Itävaara, Juho Rousu

BA3607 Metals and materials recovery

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Discovery Science
Publisher	Springer
Pages	299-307
ISBN (Electronic)	978-3-319-24282-8
ISBN (Print)	978-3-319-24281-1
DOIs	https://doi.org/10.1007/978-3-319-24282-8_25
Publication status	Published - 2015
MoE publication type	A4 Article in a conference publication
Event	18th International Conference on Discovery Science, DS 2015 - Banff, Canada Duration: 4 Oct 2015 → 6 Oct 2015 Conference number: 18

Publication series

Series	Lecture Notes in Computer Science
Volume	9356

Conference

Conference	18th International Conference on Discovery Science, DS 2015
Abbreviated title	DS 2015
Country	Canada
City	Banff
Period	4/10/15 → 6/10/15

Keywords

Canonical correlation
Kernel methods
Sparsity
Deep biosphere

Access to Document

10.1007/978-3-319-24282-8_25

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Cite this

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title = "Canonical correlation methods for exploring microbe-environment interactions in deep subsurface",

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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author = "Viivi Uurtio and Malin Bomberg and Kristian Nybo and Merja It{\"a}vaara and Juho Rousu",

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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 Discovery Science. Springer, Lecture Notes in Computer Science, vol. 9356, pp. 299-307, 18th International Conference on Discovery Science, DS 2015, Banff, Canada, 4/10/15. https://doi.org/10.1007/978-3-319-24282-8_25

Canonical correlation methods for exploring microbe-environment interactions in deep subsurface. / Uurtio, Viivi; Bomberg, Malin; Nybo, Kristian; Itävaara, Merja; Rousu, Juho.

Discovery Science. Springer, 2015. p. 299-307 (Lecture Notes in Computer Science, Vol. 9356).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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T1 - Canonical correlation methods for exploring microbe-environment interactions in deep subsurface

AU - Uurtio, Viivi

AU - Bomberg, Malin

AU - Nybo, Kristian

AU - Itävaara, Merja

AU - Rousu, Juho

N1 - Conference code: 18

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N2 - 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.

AB - 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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KW - Kernel methods

KW - Sparsity

KW - Deep biosphere

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T2 - 18th International Conference on Discovery Science, DS 2015

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