Local Similarity Analysis Reveals Unique Associations Among Marine Bacterioplankton Species and Environmental Factors

Quansong Ruan, Debojyoti Dutta, Michael S. Schwalbach, Joshua A. Steele, Jed A. Fuhrman, Fengzhu Sun

Abstract: 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. Here, we introduced Local Similarity Analysis (LSA) to identify complex dependence associations among species as well as associations between species and environmental factors. To illustrate its capability, 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 Pearson Correlation Coefficient (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.

Supplementary Information:

       Quansong Ruan, Debojyoti Dutta, Michael S. Schwalbach, Joshua A. Steele, Jed A. Fuhrman, Fengzhu Sun (2006), Local Similarity Analysis Reveals Unique Associations Among Marine Bacterioplankton Species and Environmental Factors. Bioinformatics (accepted) (pdf). 

       Software. The algorithm is implemented in R and the code can be downloaded:  here.

Contact Information: Please send your questions/suggestions to Prof. F. Sun (fsun AT usc DOT edu) or Prof. Jed Fuhrman (fuhrman AT usc DOT edu).

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Last updated: 07/22/2006.