OneNet-One network to rule them all: Consensus network inference from microbiome data.

ABSTRACT: Modeling microbial interactions as sparse and reproducible networks is a major challenge in microbial ecology. Direct interactions between the microbial species of a biome can help to understand the mechanisms through which microbial communities influence the system. Most state-of-the art methods reconstruct networks from abundance data using Gaussian Graphical Models, for which several statistically grounded and computationnally efficient inference approaches are available. However, the multiplicity of existing methods, when applied to the same dataset, generates very different networks. In this article, we present OneNet, a consensus network inference method that combines seven methods based on stability selection. This resampling procedure is used to tune a regularization parameter by c

SUBMITTER: Champion C 

PROVIDER: S-EPMC11654977 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature