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Bioinformatics. 2017 May 15;33(10):1545-1553. doi: 10.1093/bioinformatics/btx012.

Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data.

Author information

1
Department of Statistics, University of California, Berkeley, CA, USA.
2
Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
3
Department of Computational Medicine and Bioinformatics.
4
Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
5
Department of Statistics, University of Florida, Gainesville, FL, USA.

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.

PMID:
28137712
PMCID:
PMC5860222
DOI:
10.1093/bioinformatics/btx012
[Indexed for MEDLINE]
Free PMC Article

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