kLDM: Inferring Multiple Metagenomic Association Networks Based on the Variation of Environmental Factors

Identification of significant biological relationships or patterns is central to many metagenomic studies. Methods that estimate association networks have been proposed for this purpose; however, they assume that associations are static, neglecting the fact that relationships in a microbial ecosystem may vary with changes in environmental factors (EFs), which can result in inaccurate estimations. Therefore, in this study, we propose a computational model, called the k-Lognormal-Dirichlet-Multinomial (kLDM) model, which estimates multiple association networks that correspond to specific environmental conditions, and simultaneously infers microbe-microbe and EF-microbe associations for each network. The effectiveness of the kLDM model was demonstrated on synthetic data, a colorectal cancer (CRC) dataset, the Tara Oceans dataset, and the American Gut Project dataset. The results revealed that the widely-used Spearman's rank correlation coefficient method performed much worse than the other methods, indicating the importance of separating samples by environmental conditions. Cancer fecal samples were then compared with cancer-free samples, and the estimation achieved by kLDM exhibited fewer associations among microbes but stronger associations between specific bacteria, especially five CRC-associated operational taxonomic units, indicating gut microbe translocation in cancer patients. Some EF-dependent associations were then found within a marine eukaryotic community. Finally, the gut microbial heterogeneity of inflammatory bowel disease patients was detected. These results demonstrate that kLDM can elucidate the complex associations within microbial ecosystems. The kLDM program, R, and Python scripts, together with all experimental datasets, are accessible at https://github.com/tinglab/kLDM.git.

Automated summary

The study proposes a computational model, the kLDM model, which estimates multiple association networks corresponding to specific environmental conditions in microbial ecosystems. Results demonstrate the effectiveness of the kLDM model on various datasets, showing better performance compared to other methods in analyzing microbial relationships. The model was able to reveal complex associations within microbial ecosystems, particularly showing advantages in studies regarding gut microbes and cancer patients.