CaltechAUTHORS
  A Caltech Library Service

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

Xia, Li C. and Steele, Joshua A. and Cram, Jacob A. and Cardon, Zoe G. and Simmons, Sheri L. and Vallino, Joseph J. and Fuhrman, Jed A. and Sun, Fengzhu (2011) Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates. BMC Systems Biology, 5 (S2). Art. No. S15. ISSN 1752-0509. https://resolver.caltech.edu/CaltechAUTHORS:20120424-080111138

[img]
Preview
PDF - Published Version
Creative Commons Attribution.

2093Kb

Use this Persistent URL to link to this item: https://resolver.caltech.edu/CaltechAUTHORS:20120424-080111138

Abstract

Background: The 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. Results: We 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. Conclusions: The 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1186/1752-0509-5-S2-S15DOIUNSPECIFIED
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287481/PubMed CentralUNSPECIFIED
http://www.biomedcentral.com/1752-0509/5/S2/S15 PublisherUNSPECIFIED
Additional Information:© 2011 Xia et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Published online 2011 December 14. The authors would like to thank Cheryl Chow, Rohan Sachdeva, Barbara Campbell, Anders Andersson and Stefan Bertilsson for testing the eLSA software packages and web services and providing valuable suggestions. We thank Jun Zhao of PIBBS at University of Southern California for helpful discussion of C. elegans dataset analysis. We thank an anonymous reviewer for suggesting the “Med” and the “MAD” approaches. We also thank the Molecular Computational Biology Program at University of Southern California for providing computing resources. This research is partially supported by the National Science Foundation (NSF) DMS-1043075 and OCE 1136818. This article has been published as part of BMC Systems Biology Volume 5 Supplement 2, 2011: 22nd International Conference on Genome Informatics: Systems Biology. The full contents of the supplement are available online at http://www.biomedcentral.com/1752-0509/5?issue=S2. Authors’ contributions: LCX, JAS, JAC, ZGC, SLS, JJV, JAF, FS designed the study. LCX, ZGC, JAF and FS developed the methods. LCX, JAS, JAC developed and tested the software. LCX, JAS and JAC collected and analyzed the data. LCX, JAS, JAC, ZGC, JAF and FS wrote the paper
Funders:
Funding AgencyGrant Number
NSFDMS-1043075
NSFOCE-1136818
Issue or Number:S2
Record Number:CaltechAUTHORS:20120424-080111138
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20120424-080111138
Official Citation:Xia et al.: Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates. BMC Systems Biology 2011 5(Suppl 2):S15.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:30268
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:24 Apr 2012 16:47
Last Modified:03 Oct 2019 03:49

Repository Staff Only: item control page