Directional integration and pathway enrichment analysis for multi-omics data.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
07 Jul 2024
07 Jul 2024
Historique:
received:
29
09
2023
accepted:
26
06
2024
medline:
7
7
2024
pubmed:
7
7
2024
entrez:
6
7
2024
Statut:
epublish
Résumé
Omics techniques generate comprehensive profiles of biomolecules in cells and tissues. However, a holistic understanding of underlying systems requires joint analyses of multiple data modalities. We present DPM, a data fusion method for integrating omics datasets using directionality and significance estimates of genes, transcripts, or proteins. DPM allows users to define how the input datasets are expected to interact directionally given the experimental design or biological relationships between the datasets. DPM prioritises genes and pathways that change consistently across the datasets and penalises those with inconsistent directionality. To demonstrate our approach, we characterise gene and pathway regulation in IDH-mutant gliomas by jointly analysing transcriptomic, proteomic, and DNA methylation datasets. Directional integration of survival information in ovarian cancer reveals candidate biomarkers with consistent prognostic signals in transcript and protein expression. DPM is a general and adaptable framework for gene prioritisation and pathway analysis in multi-omics datasets.
Identifiants
pubmed: 38971800
doi: 10.1038/s41467-024-49986-4
pii: 10.1038/s41467-024-49986-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5690Informations de copyright
© 2024. The Author(s).
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