title: Integrative analysis of longitudinal metabolomics data from a personal multi-omics profile.
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.
authors: Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, 98101, USA. larissa.stanberry@seattlechildrens.org.
authors: Larissa
authors: L
authors: Stanberry
authors: Department of Genetics, Stanford University School of Medicine, Palo Alto, CA, 94305, USA. george.mias@stanford.edu.
authors: George I
authors: GI
authors: Mias
authors: Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, 98101, USA. mpsnyder@stanford.edu.
authors: Winston
authors: W
authors: Haynes
authors: Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, 98101, USA. mpsnyder@stanford.edu.
authors: Roger
authors: R
authors: Higdon
authors: Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, 98101, USA. george.mias@stanford.edu.
authors: Michael
authors: M
authors: Snyder
authors: Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children's Research Institute, Seattle, 98101, USA. mpsnyder@stanford.edu.
authors: Eugene
authors: E
authors: Kolker