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Bioinformatics Advance Access published online on July 31, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl417
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 21, 2006
Revised July 22, 2006
Accepted July 26, 2006

Article

Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors

Quansong Ruan 1, Debojyoti Dutta 2, Michael S. Schwalbach 3, Joshua A. Steele 3, Jed A. Fuhrman 3, and Fengzhu Sun 2 *

1 Department of Mathematics, University of Southern California, 3620 Vermont Ave, KAP 108, Los Angeles, CA 90089-2532 USA
2 Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, 1050 Childs Way, MCB 201, Los Angeles, CA 90089-2910 USA
3 Department of Biological Sciences, University of Southern California, 3616 Trousdale Pkwy, AHF 107, Los Angeles, CA 90089-0371 USA

* To whom correspondence should be addressed.
Fengzhu Sun, E-mail: fsun{at}usc.edu


  Abstract

Motivation: Characterizing the diversity of microbial communities and understanding the environmental factors that influence community diversity are central tenets of microbial ecology. The development and application of cultivation independent molecular tools has allowed for rapid surveying of microbial community composition at unprecedented resolutions and frequencies. There is a growing need to discern robust patterns and relationships within these data sets which provide insight into microbial ecology. Pearson Correlation Analysis (PCC) is commonly used for identifying the linear relationship between two species, or species and environmental factors. However, this approach may not be able to capture more complex interactions which occur in situ, thus alternative analyses were explored.

Results: In this paper we introduced Local Similarity Analysis (LSA), which is a technique that can identify more complex dependence associations among species as well as associations between species and environmental factors without requiring significant data reduction. To illustrate its capability of identifying relationships that may not otherwise be identified by PCC, we first applied LSA to simulated data. We then applied LSA to a marine microbial observatory data set and identified unique, significant associations that were not detected by PCC. LSA results, combined with results from PCC analysis were used to construct a theoretical ecological network which allows for easy visualization of the most significant associations. Biological implications of the significant associations detected by LSA were discussed. We also identified additional applications where LSA analysis would be beneficial.

Availability: The algorithms are implemented in Splus/R and they are available upon request from the corresponding author.

Associate Editor: Chris Stoeckert
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