Imperial College London
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ProfessorTimothyEbbels

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Professor of Biomedical Data Science
 
 
 
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Contact

 

+44 (0)20 7594 3160t.ebbels Website

 
 
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Location

 

315DBurlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Iacovacci:2020:10.3390/metabo10110435,
author = {Iacovacci, J and Peluso, A and Ebbels, T and Ralser, M and Glen, R},
doi = {10.3390/metabo10110435},
journal = {Metabolites},
title = {Extraction and integration of genetic networks from short-profile omic data sets},
url = {http://dx.doi.org/10.3390/metabo10110435},
volume = {10},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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
AU  - Iacovacci,J
AU  - Peluso,A
AU  - Ebbels,T
AU  - Ralser,M
AU  - Glen,R
DO  - 10.3390/metabo10110435
PY  - 2020///
SN  - 2218-1989
TI  - Extraction and integration of genetic networks from short-profile omic data sets
T2  - Metabolites
UR  - http://dx.doi.org/10.3390/metabo10110435
UR  - http://hdl.handle.net/10044/1/85007
VL  - 10
ER  -
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