| 1. |
- Antti, Henrik, et al.
(författare)
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Statistical experimental design and partial least squares regression analysis of biofluid metabonomic NMR and clinical chemistry data for screening of adverse drug effects
- 2004
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - : Elsevier BV. - 0169-7439. ; 73:1, s. 139-49
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Tidskriftsartikel (refereegranskat)abstract
- Metabonomic analysis is increasingly recognised as a powerful approach for delineating the integrated metabolic changes in biofluids and tissues due to toxicity, disease processes or genetic modification in whole animal systems. When dealing with complex biological data sets, as generated within metabonomics, as well as related fields such as genomics and proteomics, reliability and significance of identified biomarkers associated with specific states related to toxicity or disease are crucial in order to gain detailed and relevant interpretations of the metabolic fluxes in the studied systems. Since various physiological factors, such as diet, state of health, age, diurnal cycles, stress, genetic drift, and strain differences, affect the metabolic composition of biological matrices, it is of great importance to create statistically reliable decision tools for distinguishing between physiological and pathological responses in animal models. In the screening for new biomarkers or patterns of pathological dysfunction, methods providing statistically valid measures of effect-related changes will become increasingly important as the data within areas such as genomics, proteomics and metabonomics continues to grow in size and complexity. 1H NMR spectroscopy and mass spectrometry are the principal analytical platforms used to derive the data and, because extensively large data sets are required, as much consideration has to be given to optimum design of experiments (DoE) as for subsequent data analysis. Thus, statistical experimental design combined with partial least squares (PLS) regression is proposed as an efficient approach for undertaking metabonomic studies and for analysis of the results. The method was applied to data from a liver toxicology study in the rat using hydrazine as a model toxin. 1D projections of 2D J-resolved (J-RES) 1H NMR spectra and the corresponding clinical chemistry parameters of blood serum samples from control and dosed rats (30 and 90 mg/kg) collected at 48 and 168 h post dose were analysed. Confidence intervals for the PLS regression coefficients were used to create a statistical means for screening of biomarkers in the two combined data blocks (NMR and clinical chemistry data). PLS analysis was also used to reveal the correlation pattern between the two blocks of data as well as the within the two blocks according to dose, time and the interaction dose×time.
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| 2. |
- Ashrafian, Hutan, et al.
(författare)
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Metabolomics : The Stethoscope for the Twenty-First Century
- 2021
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Ingår i: Medical principles and practice. - : S. Karger. - 1011-7571 .- 1423-0151. ; 30:4, s. 301-310
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Tidskriftsartikel (refereegranskat)abstract
- Metabolomics encompasses the systematic identification and quantification of all metabolic products in the human body. This field could provide clinicians with novel sets of diagnostic biomarkers for disease states in addition to quantifying treatment response to medications at an individualized level. This literature review aims to highlight the technology underpinning metabolic profiling, identify potential applications of metabolomics in clinical practice, and discuss the translational challenges that the field faces. We searched PubMed, MEDLINE, and EMBASE for primary and secondary research articles regarding clinical applications of metabolomics. Metabolic profiling can be performed using mass spectrometry and nuclear magnetic resonance-based techniques using a variety of biological samples. This is carried out in vivo or in vitro following careful sample collection, preparation, and analysis. The potential clinical applications constitute disruptive innovations in their respective specialities, particularly oncology and metabolic medicine. Outstanding issues currently preventing widespread clinical use are scalability of data interpretation, standardization of sample handling practice, and e-infrastructure. Routine utilization of metabolomics at a patient and population level will constitute an integral part of future healthcare provision.
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| 3. |
- Blaise, Benjamin J., et al.
(författare)
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Statistical analysis in metabolic phenotyping
- 2021
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Ingår i: Nature Protocols. - : Nature Publishing Group. - 1754-2189 .- 1750-2799. ; 16:9, s. 4299-4326
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Forskningsöversikt (refereegranskat)abstract
- Metabolic phenotyping is an important tool in translational biomedical research. The advanced analytical technologies commonly used for phenotyping, including mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, generate complex data requiring tailored statistical analysis methods. Detailed protocols have been published for data acquisition by liquid NMR, solid-state NMR, ultra-performance liquid chromatography (LC-)MS and gas chromatography (GC-)MS on biofluids or tissues and their preprocessing. Here we propose an efficient protocol (guidelines and software) for statistical analysis of metabolic data generated by these methods. Code for all steps is provided, and no prior coding skill is necessary. We offer efficient solutions for the different steps required within the complete phenotyping data analytics workflow: scaling, normalization, outlier detection, multivariate analysis to explore and model study-related effects, selection of candidate biomarkers, validation, multiple testing correction and performance evaluation of statistical models. We also provide a statistical power calculation algorithm and safeguards to ensure robust and meaningful experimental designs that deliver reliable results. We exemplify the protocol with a two-group classification study and data from an epidemiological cohort; however, the protocol can be easily modified to cover a wider range of experimental designs or incorporate different modeling approaches. This protocol describes a minimal set of analyses needed to rigorously investigate typical datasets encountered in metabolic phenotyping.
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| 4. |
- Climaco Pinto, Rui, et al.
(författare)
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Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets.
- 2022
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Ingår i: Analytical chemistry. - : American Chemical Society (ACS). - 1520-6882 .- 0003-2700. ; 94:14, s. 5493-5503
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Tidskriftsartikel (refereegranskat)abstract
- Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography-mass spectrometry (LC-MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC-MS metabolomics experiments or batches using only the features' RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.
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| 5. |
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| 6. |
- Rantalainen, Mattias, et al.
(författare)
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Piecewise multivariate modelling of sequential metabolic profiling data
- 2008
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Ingår i: BMC Bioinformatics. - : BioMed Central. - 1471-2105. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints.Results: A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted.Conclusion: The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.
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| 7. |
- Rantalainen, Mattias, et al.
(författare)
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Piecewise multivariate modelling of sequential metabolic profiling data
- 2008
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Ingår i: BMC Bioinformatics. - : EMBO. - 1471-2105. ; 9, s. 105-
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Tidskriftsartikel (refereegranskat)abstract
- Background: Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. Results: A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. Conclusion: The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.
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| 8. |
- Salek, Reza M, et al.
(författare)
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COordination of Standards in MetabOlomicS (COSMOS) : facilitating integrated metabolomics data access
- 2015
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Ingår i: Metabolomics. - : Springer-Verlag New York. - 1573-3882 .- 1573-3890. ; 11:6, s. 1587-1597
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Tidskriftsartikel (refereegranskat)abstract
- Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative 'coordination of standards in metabolomics' (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities' participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.
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| 9. |
- van Rijswijk, Merlijn, et al.
(författare)
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The future of metabolomics in ELIXIR.
- 2017
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Ingår i: F1000 Research. - : F1000 Research Ltd. - 2046-1402. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.
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| 10. |
- Östman, Johnny R., et al.
(författare)
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Identification of prediagnostic metabolites associated with prostate cancer risk by untargeted mass spectrometry-based metabolomics : a case-control study nested in the Northern Sweden Health and Disease Study
- 2022
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Ingår i: International Journal of Cancer. - : John Wiley & Sons. - 0020-7136 .- 1097-0215. ; 151:12, s. 2115-2127
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Tidskriftsartikel (refereegranskat)abstract
- Prostate cancer (PCa) is the most common cancer form in males in many European and American countries, but there are still open questions regarding its etiology. Untargeted metabolomics can produce an unbiased global metabolic profile, with the opportunity for uncovering new plasma metabolites prospectively associated with risk of PCa, providing insights into disease etiology. We conducted a prospective untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis using prediagnostic fasting plasma samples from 752 PCa case-control pairs nested within the Northern Sweden Health and Disease Study (NSHDS). The pairs were matched by age, BMI, and sample storage time. Discriminating features were identified by a combination of orthogonal projection to latent structures-effect projections (OPLS-EP) and Wilcoxon signed-rank tests. Their prospective associations with PCa risk were investigated by conditional logistic regression. Subgroup analyses based on stratification by disease aggressiveness and baseline age were also conducted. Various free fatty acids and phospholipids were positively associated with overall risk of PCa and in various stratification subgroups. Aromatic amino acids were positively associated with overall risk of PCa. Uric acid was positively, and glucose negatively, associated with risk of PCa in the older subgroup. This is the largest untargeted LC-MS based metabolomics study to date on plasma metabolites prospectively associated with risk of developing PCa. Different subgroups of disease aggressiveness and baseline age showed different associations with metabolites. The findings suggest that shifts in plasma concentrations of metabolites in lipid, aromatic amino acid, and glucose metabolism are associated with risk of developing PCa during the following two decades.
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