NetGAM: Using generalized additive models to improve the predictive power of ecological network analyses constructed using time-series data.

ABSTRACT: Ecological network analyses are used to identify potential biotic interactions between microorganisms from species abundance data. These analyses are often carried out using time-series data; however, time-series networks have unique statistical challenges. Time-dependent species abundance data can lead to species co-occurrence patterns that are not a result of direct, biotic associations and may therefore result in inaccurate network predictions. Here, we describe a generalize additive model (GAM)-based data transformation that removes time-series signals from species abundance data prior to running network analyses. Validation of the transformation was carried out by generating mock, time-series datasets, with an underlying covariance structure, running network analyses on these datasets

SUBMITTER: Gleich SJ 

PROVIDER: S-EPMC9723797 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature