kLDM:Inferring Multiple Metagenomic Association Networks Based on the Variation of Environmental Factors
- Author:
Yang YUQING
1
;
Wang XIN
;
Xie KAIKUN
;
Zhu CONGMIN
;
Chen NING
;
Chen TING
Author Information
1. Department of Computer Science and Technology and Institute of Artificial Intelligence,Tsinghua University,Beijing 100084,China;Sogou Inc.,Beijing 100084,China
- Keywords:
Metagenomics;
Association inference;
Environmental condition;
Bayesian model;
Clustering
- From:
Genomics, Proteomics & Bioinformatics
2021;19(5):834-847
- CountryChina
- Language:Chinese
-
Abstract:
Identification of significant biological relationships or patterns is central to many metagenomic studies.Methods that estimate association networks have been proposed for this pur-pose;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 ecosys-tems.The kLDM program,R,and Python scripts,together with all experimental datasets,are accessible at https://github.com/tinglab/kLDM.git.
