Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data
Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic...
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Published in | Bioinformatics (Oxford, England) Vol. 33; no. 10; pp. 1545 - 1553 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Oxford University Press
15.05.2017
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Abstract | Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data.
Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds.
http://metscape.med.umich.edu.
Supplementary data are available at Bioinformatics online. |
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AbstractList | Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data.
Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds.
http://metscape.med.umich.edu.
Supplementary data are available at Bioinformatics online. MOTIVATIONRecent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data. RESULTSLeveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. AVAILABILITY AND IMPLEMENTATIONhttp://metscape.med.umich.edu. SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online. Abstract Motivation Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data. Results Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. Availability and Implementation http://metscape.med.umich.edu Supplementary information Supplementary data are available at Bioinformatics online. |
Author | Evans, Charles R Burant, Charles F Michailidis, George Karnovsky, Alla Duren, William Basu, Sumanta |
AuthorAffiliation | 5 Department of Statistics, University of Florida, Gainesville, FL, USA 4 Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA 3 Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA 2 Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA 1 Department of Statistics, University of California, Berkeley, CA, USA |
AuthorAffiliation_xml | – name: 5 Department of Statistics, University of Florida, Gainesville, FL, USA – name: 4 Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA – name: 3 Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA – name: 1 Department of Statistics, University of California, Berkeley, CA, USA – name: 2 Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA |
Author_xml | – sequence: 1 givenname: Sumanta surname: Basu fullname: Basu, Sumanta organization: Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA – sequence: 2 givenname: William surname: Duren fullname: Duren, William organization: Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA – sequence: 3 givenname: Charles R surname: Evans fullname: Evans, Charles R organization: Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA – sequence: 4 givenname: Charles F surname: Burant fullname: Burant, Charles F organization: Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA – sequence: 5 givenname: George surname: Michailidis fullname: Michailidis, George organization: Department of Statistics, University of Florida, Gainesville, FL, USA – sequence: 6 givenname: Alla surname: Karnovsky fullname: Karnovsky, Alla organization: Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28137712$$D View this record in MEDLINE/PubMed |
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Title | Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data |
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