Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism.

ABSTRACT:

Objective

Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce.

Methods

We present here a fully unsupervised and versatile correlation-based method - termed Correlation guided Network Integration (CoNI) - to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways.

Results

By applying our method on transcriptomics and metabolomics data from murine l

SUBMITTER: Klaus VS 

PROVIDER: S-EPMC8361260 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature