A cross-omics data analysis strategy for metabolite-microbe pair identification.
Proteomics
; : e2400035, 2024 Jul 12.
Article
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| MEDLINE
| ID: mdl-38994817
ABSTRACT
Given the pivotal roles of metabolomics and microbiomics, numerous data mining approaches aim to uncover their intricate connections. However, the complex many-to-many associations between metabolome-microbiome profiles yield numerous statistically significant but biologically unvalidated candidates. To address these challenges, we introduce BiOFI, a strategic framework for identifying metabolome-microbiome correlation pairs (Bi-Omics). BiOFI employs a comprehensive scoring system, incorporating intergroup differences, effects on feature correlation networks, and organism abundance. Meanwhile, it establishes a built-in database of metabolite-microbe-KEGG functional pathway linking relationships. Furthermore, BiOFI can rank related feature pairs by combining importance scores and correlation strength. Validation on a dataset of cesarean-section infants confirms the strategy's validity and interpretability. The BiOFI R package is freely accessible at https//github.com/chentianlu/BiOFI.
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Base de datos:
MEDLINE
Idioma:
En
Revista:
Proteomics
Asunto de la revista:
BIOQUIMICA
Año:
2024
Tipo del documento:
Article