Feng Chenqionglu, Jia Huiqun, Wang Hui, Wang Jiaojiao, Lin Mengxuan, Hu Xiaoyan, Yu Chenjing, Song Hongbin, Wang Ligui
Department of Epidemiology and Health Statistics, School of Public Health, China Medical University, Shenyang 110122, China.
Department of Infectious Disease Prevention and Control, Chinese PLA Center for Disease Control and Prevention, Beijing 100071, China.
Bioinform Adv. 2024 Oct 28;4(1):vbae167. doi: 10.1093/bioadv/vbae167. eCollection 2024.
The human microbiome, comprises complex associations and communication networks among microbial communities, which are crucial for maintaining health. The construction of microbial networks is vital for elucidating these associations. However, existing microbial networks inference methods cannot solve the issues of zero-inflation and non-linear associations. Therefore, necessitating novel methods to improve the accuracy of microbial networks inference.
In this study, we introduce the Microbial Network based on Mutual Information and Markov Random Fields (MicroNet-MIMRF) as a novel approach for inferring microbial networks. Abundance data of microbes are modeled through the zero-inflated Poisson distribution, and the discrete matrix is estimated for further calculation. Markov random fields based on mutual information are used to construct accurate microbial networks. MicroNet-MIMRF excels at estimating pairwise associations between microbes, effectively addressing zero-inflation and non-linear associations in microbial abundance data. It outperforms commonly used techniques in simulation experiments, achieving area under the curve values exceeding 0.75 for all parameters. A case study on inflammatory bowel disease data further demonstrates the method's ability to identify insightful associations. Conclusively, MicroNet-MIMRF is a powerful tool for microbial network inference that handles the biases caused by zero-inflation and overestimation of associations.
The MicroNet-MIMRF is provided at https://github.com/Fionabiostats/MicroNet-MIMRF.
人类微生物组由微生物群落之间复杂的关联和通信网络组成，这些对于维持健康至关重要。构建微生物网络对于阐明这些关联至关重要。然而，现有的微生物网络推断方法无法解决零膨胀和非线性关联的问题。因此，需要新的方法来提高微生物网络推断的准确性。
在本研究中，我们引入了基于互信息和马尔可夫随机场的微生物网络（MicroNet-MIMRF）作为一种推断微生物网络的新方法。通过零膨胀泊松分布对微生物的丰度数据进行建模，并估计离散矩阵以进行进一步计算。基于互信息的马尔可夫随机场用于构建准确的微生物网络。MicroNet-MIMRF擅长估计微生物之间的成对关联，有效解决了微生物丰度数据中的零膨胀和非线性关联问题。在模拟实验中，它优于常用技术，所有参数的曲线下面积值均超过0.75。对炎症性肠病数据的案例研究进一步证明了该方法识别有见地的关联的能力。总之，MicroNet-MIMRF是一种强大的微生物网络推断工具，可处理由零膨胀和关联高估引起的偏差。
MicroNet-MIMRF可在https://github.com/Fionabiostats/MicroNet-MIMRF上获取。
