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

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

doi:10.1093/bioinformatics/btl417

Cited by 251 publications

(259 citation statements)

References 21 publications

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“…This notion is supported by the observation that phylogenetically related microbes have a tendency to positively co-occur (Lozupone et al, 2012). Recent studies suggest that the microbial relationships shown in correlation interaction networks can be used to determine drivers in environmental ecology (Ruan et al, 2006;Steele et al, 2011;Zhou et al, 2011;Lima-Mendez et al, 2015) or contribution to habitat niches or disease (Chaffron et al, 2010;Arumugam et al, 2011;Greenblum et al, 2012;Oakley et al, 2013;Goodrich et al, 2014;Buffie et al, 2015). Correlation is also a powerful tool to help researchers with hypothesis generation, such as determining which interactions might be biologically relevant in their system, and should be given further study (for example, through co-culturing or whole-genome sequencing).…”

Section: Introductionmentioning

confidence: 91%

“…For example, CoNet acknowledges that various techniques have different strengths and weaknesses and/or are designed to optimally detect different functional relationships, and thus uses an ensemble method with the ReBoot procedure for P-value computation to combine information from several different standard comparison metrics. Local Similarity Analysis (LSA) (Ruan et al, 2006;Beman et al, 2011;Steele et al, 2011;Xia et al, 2013) is optimized to detect non-linear, time-sensitive relationships and can be used to build correlation networks from time-series data. The Maximal Information Coefficient (MIC) (Reshef et al, 2011) is a non-parametric method designed to capture a wide range of associations without limitation to specific function types (such as linear or exponential) and to give similar scores to equally noisy relationships of different types.…”

Section: Introductionmentioning

confidence: 99%

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Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

et al. 2016

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Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

et al. 2016

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“…For the network analysis, we first used extended Local Similarity Analysis to find the timedependent correlations between species-level OTUs and environmental variables (Ruan et al, 2006;Xia et al, 2011). The Local Similarity Analysis calculates synchronous and time-delayed correlations based on the normalized ranked data and produces correlation coefficients that are analogous to a Spearman's ranked correlation (Ruan et al, 2006).…”

Section: Statistical and Network Analysismentioning

confidence: 99%

“…The Local Similarity Analysis calculates synchronous and time-delayed correlations based on the normalized ranked data and produces correlation coefficients that are analogous to a Spearman's ranked correlation (Ruan et al, 2006). Then, we used Cytopscape v2.8.3 (Shannon et al, 2003) for network visualization and topological analysis, as described in Supplementary Information S3.…”

Section: Statistical and Network Analysismentioning

confidence: 99%

Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant

2014

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Understanding environmental and biological influences on the dynamics of microbial communities has received great attention in microbial ecology. Here, utilizing large time-series 16S rRNA gene data, we show that in activated sludge of an environmentally important municipal wastewater treatment plant, 5-year temporal dynamics of bacterial community shows no significant seasonal succession, but is consistent with deterministic assemblage by taxonomic relatedness. Biological interactions are dominant drivers in determining the bacterial community assembly, whereas environmental conditions (mainly sludge retention time and inorganic nitrogen) partially explain phylogenetic and quantitative variances and indirectly influence bacterial assembly. We demonstrate a correlation-based statistical method to integrate bacterial association networks with their taxonomic affiliations to predict community-wide co-occurrence and co-exclusion patterns. The results show that although taxonomically closely related bacteria tend to positively co-occur (for example, out of a cooperative relationship), negative co-excluding correlations are deterministically observed between taxonomically less related species, probably implicating roles of competition in determining bacterial assembly. Overall, disclosures of the positive and negative species-species relations will improve our understanding of ecological niches occupied by unknown species and help to predict their biological functions in ecosystems.

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“…Networks were generated using the extended local similarity analysis (eLSA) program (Ruan et al, 2006;Xia et al, 2013) which implements the local similarity analysis with latest improvements for high-throughput data. We used eLSA to identify global, time-lagged, Spearman correlations between bacterial and environmental nodes at all depths.…”

Section: Network Analysismentioning

confidence: 99%

Cross-depth analysis of marine bacterial networks suggests downward propagation of temporal changes

et al. 2015

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Interactions among microbes and stratification across depths are both believed to be important drivers of microbial communities, though little is known about how microbial associations differ between and across depths. We have monitored the free-living microbial community at the San Pedro Ocean Time-series station, monthly, for a decade, at five different depths: 5 m, the deep chlorophyll maximum layer, 150 m, 500 m and 890 m (just above the sea floor). Here, we introduce microbial association networks that combine data from multiple ocean depths to investigate both within-and between-depth relationships, sometimes time-lagged, among microbes and environmental parameters. The euphotic zone, deep chlorophyll maximum and 890 m depth each contain two negatively correlated 'modules' (groups of many inter-correlated bacteria and environmental conditions) suggesting regular transitions between two contrasting environmental states. Two-thirds of pairwise correlations of bacterial taxa between depths lagged such that changes in the abundance of deeper organisms followed changes in shallower organisms. Taken in conjunction with previous observations of seasonality at 890 m, these trends suggest that planktonic microbial communities throughout the water column are linked to environmental conditions and/or microbial communities in overlying waters. Poorly understood groups including Marine Group A, Nitrospina and AEGEAN-169 clades contained taxa that showed diverse association patterns, suggesting these groups contain multiple ecological species, each shaped by different factors, which we have started to delineate. These observations build upon previous work at this location, lending further credence to the hypothesis that sinking particles and vertically migrating animals transport materials that significantly shape the time-varying patterns of microbial community composition.

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

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

Cited by 251 publications

References 21 publications

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant

Cross-depth analysis of marine bacterial networks suggests downward propagation of temporal changes

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