Sparse network modeling and Metscape-based visualization methods for the analysis of large-scale metabolomics data. Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data.īasu S, Duren W, Evans CR, Burant C, Michailidis G, Karnovsky A. Karnovsky A, Weymouth T, Hull T, Tarcea VG, Scardoni G, Laudanna C, Sartor MA, Stringer KA, Jagadish HV, Burant C, Athey B, Omenn GS. Please report any problems to Cite Metscape DSPC was developed in collaboration with George Michailidis and Sumanta Basu. DSPC is implemented as part of our CorrelationCalculator program. We developed a new method that uses a Debiased Sparse Partial Correlation (DSPC) algorithm to identify connectivity among large numbers of metabolites using fewer samples. MetScape 3.1 supports building and visualization of correlation networks, in addition to pathway networks. MetScape v3.1.3, for use with Cytoscape 3.4.0 and higher, is now available. NewsĬorrelationCalculator v1.0.1, featuring a revamped interface, is now available. MetScape uses an internal relational database that integrates data from KEGG and EHMN. Gene expression and/or compound concentration data can be loaded from file(s) (in CSV, TSV, or Excel formats), or the user can directly enter individual compounds/genes (using KEGG compound IDs or Entrez Gene IDs) to build metabolic networks without loading a file. It allows users to build and analyze networks of genes and compounds, identify enriched pathways from expression profiling data, and visualize changes in metabolite data.
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