Manta: a Clustering Algorithm for Weighted Ecological Networks.

ABSTRACT: Microbial network inference and analysis have become successful approaches to extract biological hypotheses from microbial sequencing data. Network clustering is a crucial step in this analysis. Here, we present a novel heuristic network clustering algorithm, manta, which clusters nodes in weighted networks. In contrast to existing algorithms, manta exploits negative edges while differentiating between weak and strong cluster assignments. For this reason, manta can tackle gradients and is able to avoid clustering problematic nodes. In addition, manta assesses the robustness of cluster assignment, which makes it more robust to noisy data than most existing tools. On noise-free synthetic data, manta equals or outperforms existing algorithms, while it identifies biologically relevant subcompo

SUBMITTER: Rottjers L 

PROVIDER: S-EPMC7029223 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature