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

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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.

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Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

Charles Vaske, Stephen Benz, J Sanborn … (2010)

Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference. Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients. Availability:Source code available at http://sbenz.github.com/Paradigm Contact: jstuart@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis.

Ronglai Shen, Adam B. Olshen, Marc Ladanyi (2009)

The molecular complexity of a tumor manifests itself at the genomic, epigenomic, transcriptomic and proteomic levels. Genomic profiling at these multiple levels should allow an integrated characterization of tumor etiology. However, there is a shortage of effective statistical and bioinformatic tools for truly integrative data analysis. The standard approach to integrative clustering is separate clustering followed by manual integration. A more statistically powerful approach would incorporate all data types simultaneously and generate a single integrated cluster assignment. We developed a joint latent variable model for integrative clustering. We call the resulting methodology iCluster. iCluster incorporates flexible modeling of the associations between different data types and the variance-covariance structure within data types in a single framework, while simultaneously reducing the dimensionality of the datasets. Likelihood-based inference is obtained through the Expectation-Maximization algorithm. We demonstrate the iCluster algorithm using two examples of joint analysis of copy number and gene expression data, one from breast cancer and one from lung cancer. In both cases, we identified subtypes characterized by concordant DNA copy number changes and gene expression as well as unique profiles specific to one or the other in a completely automated fashion. In addition, the algorithm discovers potentially novel subtypes by combining weak yet consistent alteration patterns across data types. R code to implement iCluster can be downloaded at http://www.mskcc.org/mskcc/html/85130.cfm

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The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.

Melanie Swan (2013)

A key contemporary trend emerging in big data science is the quantified self (QS)-individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual's continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity-barriers to widespread adoption and a critique regarding scientific soundness-but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality.

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Author and article information

Journal

Journal ID (nlm-ta): Metabolites

Journal ID (iso-abbrev): Metabolites

Journal ID (publisher-id): metabolites

Title: Metabolites

Publisher: MDPI

ISSN (Electronic): 2218-1989

Publication date (Electronic): 03 September 2013

Publication date Collection: September 2013

Volume: 3

Issue: 3

Pages: 741-760

Affiliations

[1 ]Bioinformatics and High-throughput Analysis Laboratory, and High-throughput Analysis Core, Seattle Children’s Research Institute, Seattle, 98101, USA; E-Mails: winston.haynes@ 123456seattlechildrens.org (W.H.); roger.higdon@ 123456seattlechildrens.org (R.H.); eugene.kolker@ 123456seattlechildrens.org (E.K.)

[2 ]Predictive Analytics, Seattle Children’s, Seattle, 98101, USA

[3 ]Data-Enabled Life Sciences Alliance (DELSA Global), Seattle, 98101, USA; E-Mail: mpsnyder@ 123456stanford.edu (M.S.)

[4 ]Department of Genetics, Stanford University School of Medicine, Palo Alto, CA, 94305, USA; E-Mail: george.mias@ 123456stanford.edu (G.I.M.)

[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 notes

[* ] Author to whom correspondence should be addressed; E-Mail: larissa.stanberry@ 123456seattlechildrens.org ; Tel.: +206-884-1059.

Article

Publisher ID: metabolites-03-00741

DOI: 10.3390/metabo3030741

PMC ID: 3901289

PubMed ID: 24958148

SO-VID: 2b3a4c47-4e09-4c7f-a06e-9c006ad58c1f

License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

History

Date received : 08 June 2013

Date revision received : 30 July 2013

Date accepted : 05 August 2013

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

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Most cited references 47

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis.

The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery.

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