Metabolites 2013, 3(3), 741-760; doi:10.3390/metabo3030741

Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile

1 Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, 98101, USA 2 Predictive Analytics, Seattle Children's, Seattle, 98101, USA 3 Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, 98101, USA 4 Department of Genetics, Stanford University School of Medicine, Palo Alto, CA, 94305, USA 5 Stanford Center for Genomics and Personalized Medicine, Palo Alto, CA, 94305, USA 6 Departments of Biomedical Informatics & Medical Education and Pediatrics, University of Washington, Seattle, WA, 98195, USA
* Author to whom correspondence should be addressed.
Received: 8 June 2013; in revised form: 30 July 2013 / Accepted: 5 August 2013 / Published: 3 September 2013
(This article belongs to the Special Issue Integrative Metabolomics)
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Abstract: The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling.
Keywords: metabolomics; integrative pathway analysis; DEAP; dendrogram sharpening; DELSA; iPOP; longitudinal design; multi-omics data; single linkage.

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MDPI and ACS Style

Stanberry, L.; Mias, G.I.; Haynes, W.; Higdon, R.; Snyder, M.; Kolker, E. Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile. Metabolites 2013, 3, 741-760.

AMA Style

Stanberry L, Mias GI, Haynes W, Higdon R, Snyder M, Kolker E. Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile. Metabolites. 2013; 3(3):741-760.

Chicago/Turabian Style

Stanberry, Larissa; Mias, George I.; Haynes, Winston; Higdon, Roger; Snyder, Michael; Kolker, Eugene. 2013. "Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile." Metabolites 3, no. 3: 741-760.

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