Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates

Abstract

BackgroundThe increasing availability of time series microbial community data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many analytical techniques available, the Local Similarity Analysis (LSA) method is unique in that it captures local and potentially time-delayed co-occurrence and association patterns in time series data that cannot otherwise be identified by ordinary correlation analysis. However LSA, as originally developed, does not consider time series data with replicates, which hinders the full exploitation of available information. With replicates, it is possible to understand the variability of local similarity (LS) score and to obtain its confidence interval.ResultsWe extended our LSA technique to time series data with replicates and termed it extended LSA, or eLSA. Simulations showed the capability of eLSA to capture subinterval and time-delayed associations. We implemented the eLSA technique into an easy-to-use analytic software package. The software pipeline integrates data normalization, statistical correlation calculation, statistical significance evaluation, and association network construction steps. We applied the eLSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified.ConclusionsThe extended LSA analysis technique was demonstrated to reveal statistically significant local and potentially time-delayed association patterns in replicated time series data beyond that of ordinary correlation analysis. These statistically significant associations can provide insights to the real dynamics of biological systems. The newly designed eLSA software efficiently streamlines the analysis and is freely available from the eLSA homepage, which can be accessed at http://meta.usc.edu/softs/lsa.

Temporal variability and coherence of euphotic zone bacterial communities over a decade in the Southern California Bight

“…We used Local Similarity Analysis (eLSA) to identify intervals of correlation between OTUs and environmental parameters: analysis settings included a minimum occurrence of 5 months, normalization of variables by 'percentileZ' method, use of p mix (determined theoretical P-value followed by permutation testing (n ¼ 2000) for any P o0.1 to decrease computation time while maintaining accuracy) and linear interpolation of missing values (Ruan et al, 2006a;Xia et al, 2011Xia et al, , 2013. eLSA correlations with P o0.05 and q o0.10 were visualized in Cytoscape v2.8.2 (Shannon 2003;Smoot et al, 2011); q-values were calculated to determine a false-discovery rate (Storey 2002).…”

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

“…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).…”

The legacy of the past: effects of historical processes on microbial metacommunities

“…dispersal barriers, colonisation history, past environmental conditions) or more contemporary species sorting processes. Other methods, such as extended local similarity analysis (eLSA) (Xia et al 2011(Xia et al , 2013 and similar network approaches (e.g. Faust et al 2015) can identify time-delayed associations between occurences of taxa and environmental variables.…”