MMINP: A computational framework of microbe-metabolite interactions-based metabolic profiles predictor based on the O2-PLS algorithm
Wenli Tang,
Huimin Zheng,
Shuangbin Xu,
Pan Li,
Li Zhan,
Xiao Luo,
Zehan Dai,
Qianwen Wang,
Guangchuang Yu
Affiliations
Wenli Tang
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
Huimin Zheng
Department of Obstetrics and Gynecology, First People’s Hospital of Foshan, Foshan, Guangdong, China
Shuangbin Xu
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
Pan Li
Microbiome Research Centre, St. George and Sutherland Clinical School, Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
Li Zhan
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
Xiao Luo
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
Zehan Dai
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
Qianwen Wang
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
Guangchuang Yu
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
ABSTRACTThe gut metabolome acts as an intermediary between the gut microbiota and host, and has tremendous diagnostic and therapeutic potential. Several studies have utilized bioinformatic tools to predict metabolites based on the different aspects of the gut microbiome. Although these tools have contributed to a better understanding of the relationship between the gut microbiota and various diseases, most of them have focused on the impact of microbial genes on the metabolites and the relationship between microbial genes. In contrast, relatively little is known regarding the effect of metabolites on the microbial genes or the relationship between these metabolites. In this study, we constructed a computational framework of Microbe-Metabolite INteractions-based metabolic profiles Predictor (MMINP), based on the Two-Way Orthogonal Partial Least Squares (O2-PLS) algorithm to predict the metabolic profiles associated with gut microbiota. We demonstrated the predictive value of MMINP relative to that of similar methods. Additionally, we identified the features that would profoundly impact the prediction performance of data-driven methods (O2-PLS, MMINP, MelonnPan, and ENVIM), including the training sample size, host disease state, and the upstream data processing methods of the different technical platforms. We suggest that when using data-driven methods, similar host disease states and preprocessing methods, and a sufficient number of training samples are necessary to achieve accurate prediction.
