Abstract
The increasing availability of time series data from metagenomics and other molecular biological studies has enabled the analysis of large-scale microbial co-occurrence and association networks. Among the many available analytical techniques for detecting interactions, 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. We developed algorithms for LSA with/without replicates and statistical theory for evaluating its statistical significance based on the classical theory of Feller (1951) on the range of partial sums of Markov random variables with mean 0. We applied the LSA technique to microbial community and gene expression datasets, where unique time-dependent associations were identified. Recent large scale comparative studies of different methods for the identification of interactions among OTUs in metagenomics studies clearly showed the superior performance of LSA in most situations. We implemented the eLSA technique and theoretical p-value calculation into an easy-to-use analytic software package, which can be accessed at http://meta.usc.edu/softs/lsa.