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Grant Details

Grant Number: 5U01CA235488-04 Interpret this number
Primary Investigator: Kechris-Mays, Katherina
Organization: University Of Colorado Denver
Project Title: Addressing Sparsity in Metabolomics Data Analysis
Fiscal Year: 2021


Project Summary Comprehensive profiling of the small molecule repertoire in a sample is referred to as metabolomics, and is being used to address a variety of scientific questions in biomedical studies. Metabolomics offers more immediate measures of the physiology of an individual, and more direct examination of the effects of exposures such as nutrition, smoking and bacterial infections. For human health, metabolomics studies are being used to investigate disease mechanisms, discover biomarkers, diagnose disease, and monitor treatment responses. Metabolomics is increasingly recognized as an important component of precision medicine initiatives to complement and enhance collected genomic data. This is critical as the metabolome cannot be predicted from knowledge of the genome, transcriptome or proteome, but provides important information on the phenotype. Recent technological advances in mass spectrometry-based metabolomics have allowed for more comprehensive and sensitive measurements of metabolites. We focus on untargeted ultra-high pressure liquid chromatography coupled to mass spectrometry, which is one of the more commonly used methods. Despite the technological advances, the bottleneck for taking full advantage of metabolomics data is often the paucity and incompleteness of analytical tools and databases. Our goal is to develop novel statistical methods and software for the research community to improve the utilization of metabolomics data. There are many steps in a metabolomics data analysis pipeline, and we will focus on the downstream steps of normalization, and univariate, multivariate and pathway analyses. In particular, we will address the high levels of sparsity, which is one of the more unique aspects of metabolomics data compared to other –omics data sets. For metabolomics data, there is sparsity in individual metabolites due to a large percentage of missing data for biological or technical reasons, and sparsity in connections between metabolites due to high collinearity and sparsely connected networks in metabolic pathways. The methods and software we develop will maximize the potential of metabolomics to provide new discoveries in disease etiology, diagnosis, and drug development.


TreeKernel: interpretable kernel machine tests for interactions between -omics and clinical predictors with applications to metabolomics and COPD phenotypes.
Authors: Carpenter C.M. , Gillenwater L. , Bowler R. , Kechris K. , Ghosh D. .
Source: BMC bioinformatics, 2023-10-25; 24(1), p. 398.
EPub date: 2023-10-25.
PMID: 37880571
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Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics.
Authors: Dekermanjian J.P. , Shaddox E. , Nandy D. , Ghosh D. , Kechris K. .
Source: BMC bioinformatics, 2022-05-16; 23(1), p. 179.
EPub date: 2022-05-16.
PMID: 35578165
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Profiling Parkinson's disease cognitive phenotypes via resting-state magnetoencephalography.
Authors: Simon O.B. , Rojas D.C. , Ghosh D. , Yang X. , Rogers S.E. , Martin C.S. , Holden S.K. , Kluger B.M. , Buard I. .
Source: Journal of neurophysiology, 2022-01-01; 127(1), p. 279-289.
EPub date: 2021-12-22.
PMID: 34936515
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MSCAT: A Machine Learning Assisted Catalog of Metabolomics Software Tools.
Authors: Dekermanjian J. , Labeikovsky W. , Ghosh D. , Kechris K. .
Source: Metabolites, 2021-10-02; 11(10), .
EPub date: 2021-10-02.
PMID: 34677393
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PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes.
Authors: Carpenter C.M. , Zhang W. , Gillenwater L. , Severn C. , Ghosh T. , Bowler R. , Kechris K. , Ghosh D. .
Source: PLoS computational biology, 2021 Oct; 17(10), p. e1008986.
EPub date: 2021-10-22.
PMID: 34679079
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Reproducibility of mass spectrometry based metabolomics data.
Authors: Ghosh T. , Philtron D. , Zhang W. , Kechris K. , Ghosh D. .
Source: BMC bioinformatics, 2021-09-07; 22(1), p. 423.
EPub date: 2021-09-07.
PMID: 34493210
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A Mediation Approach to Discovering Causal Relationships between the Metabolome and DNA Methylation in Type 1 Diabetes.
Authors: Vigers T. , Vanderlinden L.A. , Johnson R.K. , Carry P.M. , Yang I. , DeFelice B.C. , Kaizer A.M. , Pyle L. , Rewers M. , Fiehn O. , et al. .
Source: Metabolites, 2021-08-14; 11(8), .
EPub date: 2021-08-14.
PMID: 34436483
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Metabolomic Profiling Reveals Sex Specific Associations with Chronic Obstructive Pulmonary Disease and Emphysema.
Authors: Gillenwater L.A. , Kechris K.J. , Pratte K.A. , Reisdorph N. , Petrache I. , Labaki W.W. , O'Neal W. , Krishnan J.A. , Ortega V.E. , DeMeo D.L. , et al. .
Source: Metabolites, 2021-03-11; 11(3), .
EPub date: 2021-03-11.
PMID: 33799786
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A Practical Guide to Metabolomics Software Development.
Authors: Chang H.Y. , Colby S.M. , Du X. , Gomez J.D. , Helf M.J. , Kechris K. , Kirkpatrick C.R. , Li S. , Patti G.J. , Renslow R.S. , et al. .
Source: Analytical chemistry, 2021-02-02; 93(4), p. 1912-1923.
EPub date: 2021-01-19.
PMID: 33467846
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Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules.
Authors: Gillenwater L.A. , Pratte K.A. , Hobbs B.D. , Cho M.H. , Zhuang Y. , Halper-Stromberg E. , Cruickshank-Quinn C. , Reisdorph N. , Petrache I. , Labaki W.W. , et al. .
Source: Network and systems medicine, 2020-12-01; 3(1), p. 159-181.
EPub date: 2020-12-31.
PMID: 33987620
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Identifying Protein-metabolite Networks Associated with COPD Phenotypes.
Authors: Mastej E. , Gillenwater L. , Zhuang Y. , Pratte K.A. , Bowler R.P. , Kechris K. .
Source: Metabolites, 2020-03-25; 10(4), .
EPub date: 2020-03-25.
PMID: 32218378
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Predictive Modeling for Metabolomics Data.
Authors: Ghosh T. , Zhang W. , Ghosh D. , Kechris K. .
Source: Methods in molecular biology (Clifton, N.J.), 2020; 2104, p. 313-336.
PMID: 31953824
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Pre-analytic Considerations for Mass Spectrometry-Based Untargeted Metabolomics Data.
Authors: Reinhold D. , Pielke-Lombardo H. , Jacobson S. , Ghosh D. , Kechris K. .
Source: Methods in molecular biology (Clifton, N.J.), 2019; 1978, p. 323-340.
PMID: 31119672
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