Neural network-driven nonlinear analysis of beta diversity in microbial communities with DeepBeta

The analysis of beta diversity is pivotal in understanding microbial community dynamics and their responses to environmental shifts. Traditional linear methods often struggle with the nonlinear complexities of microbial datasets, limiting their effectiveness in ecological studies. We present DeepBeta, an innovative deep learning-based framework designed to enhance beta diversity analysis for bioinformatics applications. DeepBeta utilizes Bray-Curtis dissimilarities computed from species abundance data and encodes these distances into a latent feature space using an autoencoder. This approach effectively captures intricate, nonlinear ecological relationships, enabling advanced analysis. The resulting latent feature-derived distance matrix is applied to beta diversity studies, leveraging statistical tools such as PERMANOVA to examine factors like community composition and spatiotemporal variations. Enhanced variance representation in Principal Component Analysis (PCA) and improved ecological clustering reveal the framework’s capability to detect subtle patterns with greater resolution compared to conventional methods. Visualization of encoded features highlights clear ecological gradients, facilitating robust insights into microbial community structures. DeepBeta bridges machine learning and beta diversity analysis, offering a powerful tool for bioinformatics research and advancing ecological investigations in complex ecosystems.