Extraction and Integration of Genetic Networks from Short-Profile Omic Data Sets.
ionomics
mahalanobis cosine
metabolomics
multi-omics integration
multiplex networks
similarity measures
Journal
Metabolites
ISSN: 2218-1989
Titre abrégé: Metabolites
Pays: Switzerland
ID NLM: 101578790
Informations de publication
Date de publication:
29 Oct 2020
29 Oct 2020
Historique:
received:
25
08
2020
revised:
13
10
2020
accepted:
23
10
2020
entrez:
3
11
2020
pubmed:
4
11
2020
medline:
4
11
2020
Statut:
epublish
Résumé
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
Identifiants
pubmed: 33137869
pii: metabo10110435
doi: 10.3390/metabo10110435
pmc: PMC7693762
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : Medical Research Council
ID : MR/S019669/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 202952/D/16/Z
Pays : United Kingdom
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