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

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 Anal...

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Autores principales: Xia, Li C, Steele, Joshua A, Cram, Jacob A, Cardon, Zoe G, Simmons, Sheri L, Vallino, Joseph J, Fuhrman, Jed A, Sun, Fengzhu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287481/
https://www.ncbi.nlm.nih.gov/pubmed/22784572
http://dx.doi.org/10.1186/1752-0509-5-S2-S15
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author Xia, Li C
Steele, Joshua A
Cram, Jacob A
Cardon, Zoe G
Simmons, Sheri L
Vallino, Joseph J
Fuhrman, Jed A
Sun, Fengzhu
author_facet Xia, Li C
Steele, Joshua A
Cram, Jacob A
Cardon, Zoe G
Simmons, Sheri L
Vallino, Joseph J
Fuhrman, Jed A
Sun, Fengzhu
author_sort Xia, Li C
collection PubMed
description 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.
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spelling pubmed-32874812012-02-28 Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates Xia, Li C Steele, Joshua A Cram, Jacob A Cardon, Zoe G Simmons, Sheri L Vallino, Joseph J Fuhrman, Jed A Sun, Fengzhu BMC Syst Biol Proceedings 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. BioMed Central 2011-12-14 /pmc/articles/PMC3287481/ /pubmed/22784572 http://dx.doi.org/10.1186/1752-0509-5-S2-S15 Text en Copyright ©2011 Xia et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 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.
spellingShingle Proceedings
Xia, Li C
Steele, Joshua A
Cram, Jacob A
Cardon, Zoe G
Simmons, Sheri L
Vallino, Joseph J
Fuhrman, Jed A
Sun, Fengzhu
Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates
title Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates
title_full Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates
title_fullStr Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates
title_full_unstemmed Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates
title_short Extended local similarity analysis (eLSA) of microbial community and other time series data with replicates
title_sort extended local similarity analysis (elsa) of microbial community and other time series data with replicates
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287481/
https://www.ncbi.nlm.nih.gov/pubmed/22784572
http://dx.doi.org/10.1186/1752-0509-5-S2-S15
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