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Extraction and Integration of Genetic Networks from Short-Profile Omic Data Sets
| Content Provider | MDPI |
|---|---|
| Author | Iacovacci, Jacopo Peluso, Alina Ebbels, Timothy Ralser, Markus Glen, Robert |
| Copyright Year | 2020 |
| Description | Mass spectrometry technologies are widely used in the fields of ionomics and metabolomics to simultaneously profile the intracellular concentrations of, e.g., amino acids or elements in genome-wide mutant libraries. These molecular or sub-molecular features are generally non-Gaussian and their covariance reveals patterns of correlations that reflect the system nature of the cell biochemistry and biology. Here, we introduce two similarity measures, the Mahalanobis cosine and the hybrid Mahalanobis cosine, that enforce information from the empirical covariance matrix of omics data from high-throughput screening and that can be used to quantify similarities between the profiled features of different mutants. We evaluate the performance of these similarity measures in the task of inferring and integrating genetic networks from short-profile ionomics/metabolomics data through an analysis of experimental data sets related to the ionome and the metabolome of the model organism S. cerevisiae. The study of the resulting ionome–metabolome Saccharomyces cerevisiae multilayer genetic network, which encodes multiple omic-specific levels of correlations between genes, shows that the proposed measures can provide an alternative description of relations between biological processes when compared to the commonly used Pearson’s correlation coefficient and have the potential to guide the construction of novel hypotheses on the function of uncharacterised genes. |
| Starting Page | 435 |
| e-ISSN | 22181989 |
| DOI | 10.3390/metabo10110435 |
| Journal | Metabolites |
| Issue Number | 11 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-10-29 |
| Access Restriction | Open |
| Subject Keyword | Metabolites Biochemical Research Similarity Measures Mahalanobis Cosine Multiplex Networks Multi-omics Integration Ionomics Metabolomics |
| Content Type | Text |
| Resource Type | Article |
