Sparse Network Modeling and Metscape-based Visualization Methods for the Analysis of Large-scale Metabolomics Data

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2017 Feb 1

PMID 28137712

Citations 94

Authors

Sumanta Basu

William Duren

Charles R Evans

Charles F Burant

George Michailidis

Alla Karnovsky

Affiliations

Soon will be listed here.

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.

Citing Articles

Attenuated growth factor signaling during cell death initiation sensitizes membranes towards peroxidation.

Gollowitzer A, Pein H, Rao Z, Waltl L, Bereuter L, Loeser K Nat Commun. 2025; 16(1):1774.

PMID: 40000627 PMC: 11861335. DOI: 10.1038/s41467-025-56711-2.

Untargeted metabolomics of volatiles: Implications in pathway enrichments for improved bioactivities.

Batra N, Dey P Heliyon. 2025; 11(3):e42268.

PMID: 39916824 PMC: 11795801. DOI: 10.1016/j.heliyon.2025.e42268.

Construction of Multi-Modal Transcriptome-Small Molecule Interaction Networks from High-Throughput Measurements to Study Human Complex Traits.

Akbary Moghaddam V, Acharya S, Schwaiger-Haber M, Liao S, Jung W, Thyagarajan B bioRxiv. 2025; .

PMID: 39896668 PMC: 11785221. DOI: 10.1101/2025.01.22.634403.

Extraction efficiency and bioactive evaluation of Tamarix nilotica and Arthrocnemum macrostachyum extracts for anti-cancer potential.

Al Kaabi M, ElNaker N, Jan N, Ochsenkuhn M, Amin S, Yousef L PLoS One. 2025; 20(1):e0311567.

PMID: 39836644 PMC: 11750082. DOI: 10.1371/journal.pone.0311567.

Essential oil and furanosesquiterpenes from myrrh oleo-gum resin: a breakthrough in mosquito vector management.

Spinozzi E, Ferrati M, Baldassarri C, Rossi P, Favia G, Cameli G Nat Prod Bioprospect. 2025; 15(1):12.

PMID: 39832119 PMC: 11753448. DOI: 10.1007/s13659-024-00492-6.

References

Schafer J, Strimmer K . A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol. 2006; 4:Article32. DOI: 10.2202/1544-6115.1175. View

Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita K, Itoh M, Kawashima S . From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2005; 34(Database issue):D354-7. PMC: 1347464. DOI: 10.1093/nar/gkj102. View

Ma H, Sorokin A, Mazein A, Selkov A, Selkov E, Demin O . The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol. 2007; 3:135. PMC: 2013923. DOI: 10.1038/msb4100177. View

Caspi R, Altman T, Dreher K, Fulcher C, Subhraveti P, Keseler I . The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2011; 40(Database issue):D742-53. PMC: 3245006. DOI: 10.1093/nar/gkr1014. View

Zuo Y, Yu G, Tadesse M, Ressom H . Biological network inference using low order partial correlation. Methods. 2014; 69(3):266-73. PMC: 4194134. DOI: 10.1016/j.ymeth.2014.06.010. View

Kotze H, Armitage E, Sharkey K, Allwood J, Dunn W, Williams K . A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions. BMC Syst Biol. 2013; 7:107. PMC: 3874763. DOI: 10.1186/1752-0509-7-107. View

de la Fuente A, Bing N, Hoeschele I, Mendes P . Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics. 2004; 20(18):3565-74. DOI: 10.1093/bioinformatics/bth445. View

Romero P, Wagg J, Green M, Kaiser D, Krummenacker M, Karp P . Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2005; 6(1):R2. PMC: 549063. DOI: 10.1186/gb-2004-6-1-r2. View

Zamboni N, Saghatelian A, Patti G . Defining the metabolome: size, flux, and regulation. Mol Cell. 2015; 58(4):699-706. PMC: 4831058. DOI: 10.1016/j.molcel.2015.04.021. View

10.

Karnovsky A, Weymouth T, Hull T, Tarcea V, Scardoni G, Laudanna C . Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data. Bioinformatics. 2011; 28(3):373-80. PMC: 3268237. DOI: 10.1093/bioinformatics/btr661. View

11.

Li S, Park Y, Duraisingham S, Strobel F, Khan N, Soltow Q . Predicting network activity from high throughput metabolomics. PLoS Comput Biol. 2013; 9(7):e1003123. PMC: 3701697. DOI: 10.1371/journal.pcbi.1003123. View

12.

Alonso A, Julia A, Beltran A, Vinaixa M, Diaz M, Ibanez L . AStream: an R package for annotating LC/MS metabolomic data. Bioinformatics. 2011; 27(9):1339-40. DOI: 10.1093/bioinformatics/btr138. View

13.

Duarte N, Becker S, Jamshidi N, Thiele I, Mo M, Vo T . Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A. 2007; 104(6):1777-82. PMC: 1794290. DOI: 10.1073/pnas.0610772104. View

14.

Leclerc R . Survival of the sparsest: robust gene networks are parsimonious. Mol Syst Biol. 2008; 4:213. PMC: 2538912. DOI: 10.1038/msb.2008.52. View

15.

Venkatraman J, Pehowich D, Singh B, Rajotte R, Thomson A, Clandinin M . Effect of dietary fat on diabetes-induced changes in liver microsomal fatty acid composition and glucose-6-phosphatase activity in rats. Lipids. 1991; 26(6):441-4. DOI: 10.1007/BF02536070. View

16.

Kuhl C, Tautenhahn R, Bottcher C, Larson T, Neumann S . CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem. 2011; 84(1):283-9. PMC: 3658281. DOI: 10.1021/ac202450g. View

17.

Petsalaki E, Helbig A, Gopal A, Pasculescu A, Roth F, Pawson T . SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets. Nucleic Acids Res. 2015; 43(W1):W276-82. PMC: 4489257. DOI: 10.1093/nar/gkv459. View

18.

Thiele I, Swainston N, Fleming R, Hoppe A, Sahoo S, Aurich M . A community-driven global reconstruction of human metabolism. Nat Biotechnol. 2013; 31(5):419-25. PMC: 3856361. DOI: 10.1038/nbt.2488. View

19.

Schafer J, Strimmer K . An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics. 2004; 21(6):754-64. DOI: 10.1093/bioinformatics/bti062. View

20.

Sartor M, Ade A, Wright Z, States D, Omenn G, Athey B . Metab2MeSH: annotating compounds with medical subject headings. Bioinformatics. 2012; 28(10):1408-10. PMC: 3348562. DOI: 10.1093/bioinformatics/bts156. View