Omics community detection using multi-resolution clustering.

ABSTRACT:

Motivation

The discovery of biologically interpretable and clinically actionable communities in heterogeneous omics data is a necessary first step toward deriving mechanistic insights into complex biological phenomena. Here, we present a novel clustering approach, omeClust, for community detection in omics profiles by simultaneously incorporating similarities among measurements and the overall complex structure of the data.

Results

We show that omeClust outperforms published methods in inferring the true community structure as measured by both sensitivity and misclassification rate on simulated datasets. We further validated omeClust in diverse, multiple omics datasets, revealing new communities and functionally related groups in microbial strains, cell line gene expression

SUBMITTER: Rahnavard A 

PROVIDER: S-EPMC8545346 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature