SPLANG-a synthetic poisson-lognormal-based abundance and network generative model for microbial interaction inference algorithms.

ABSTRACT: Microbes are pervasive and their interaction with each other and the environment can impact fields as diverse as health and agriculture. While network inference and related algorithms that use abundance data from pyrosequencing can infer microbial interaction networks, the ambiguity surrounding the actual underlying networks hampers the validation of these algorithms. This study introduces a generative model to synthesize both the underlying interactive network and observable abundance data, serving as a test bed for the existing and future network inference algorithms. We tested our generative model with four typical network inference algorithms; our results suggest that none of these algorithms demonstrate adequate accuracy for inferring ecologies of non-commensalistic species, either mu

SUBMITTER: Qian W 

PROVIDER: S-EPMC11499831 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature