Networks Analysis Options
KEGG Global Metabolic Network
Users can map metabolites and enzymes/KOs (KEGG Orthologs), and then visually explore the results in the KEGG global metabolic network (ko01100). This feature is especially suitable to integrate results from joint metabolomics and metagenomics studies.
Metabolite-Disease Interaction Network
The metabolite-disease interaction network enables exploration of disease-related metabolites. The associations were obtained from HMDB. Disease association have been added to HMDB via the Human Metabolome Project’s literature curation team.
Gene-Metabolite Interaction Network
The gene-metabolite interaction network enables exploration and visualization of interactions between functionally related metabolites and genes. The chemical and human gene associations were extracted from STITCH, such that only highly confident interactions are used. Most of associations in STITCH are based on co-mentions highlighted in PubMed Abstracts including reactions from similar chemical structures and similar molecular activities.
Metabolite-Metabolite Interaction Network
The metabolite-metabolite interaction network helps to highlight potential functional relationships between a wide set of annotated metabolites. The chemical-chemical associations for the metabolites network were extracted from STITCH, such that only highly confident interactions are used. Most of associations in STITCH are based on co-mentions highlighted in PubMed Abstracts including reactions from similar chemical structures and similar molecular activities.
Metabolite-Gene-Disease Interaction Network
The metabolite-gene-disease interaction network provides a global view of potential functional relationships between metabolites, connected genes, and target diseases. The network is an integration of gene-metabolite, metabolite-disease and gene-disease interaction networks.
Debiased Sparse Partial Correlation (DSPC) Network
Debiased Sparse Partial Correlation (DSPC) algorithm is based on the de-sparsified graphical lasso modeling procedure (Jankova, 2015). A key assumption is that the number of true connections among the metabolites is much smaller than the available sample size. DSPC reconstructs a graphical model and provides partial correlation coefficients and P-values for every pair of metabolic features in the dataset. Thus, DSPC allows discovering connectivity among large numbers of metabolites using fewer samples (Basu et al., 2017).