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.
Highlights
Modern high-throughput technologies enable rapid and efficient simultaneous acquisition of multi-omics data in the course of a single experiment
To integrate metabolomics and proteomics data, we extended the Differential Expression Analysis for Pathways (DEAP) to include multi-omics measurements and to account for the longitudinal design
We applied dendrogram sharpening to the complete metabolomics data set containing 1,088 compounds
Summary
Modern high-throughput technologies enable rapid and efficient simultaneous acquisition of multi-omics data in the course of a single experiment. The combination of genomics, transcriptomics, proteomics, lipidomics and metabolomics data provides a snapshot of biological processes in an organism. Compared to single omics approaches, multi-omics data provide a comprehensive view of biochemical, biophysical, genetic and epigenetic processes in an organism. Data vary considerably between each different omics, with respect to the biological processes the data represent, and the associated noise levels, identification accuracy, coverage and temporal resolution of the data. These differences complicate integration and joint modeling of multi-omics data
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