Understanding complex interactions within microbiomes is essential for exploring their roles in health and disease. However, constructing reliable microbiome networks often poses a challenge due to variations in the output of different network inference algorithms. To address this issue, we present CMiNet, an R package designed to generate a consensus microbiome network by integrating results from multiple established network construction methods. CMiNet incorporates nine widely used algorithms, including Pearson, Spearman, Biweight Midcorrelation (Bicor), SparCC, SpiecEasi, SPRING, GCoDA, and CCLasso, along with a novel algorithm based on conditional mutual information (CMIMN). By combining the strengths of these algorithms, CMiNet generates a single, weighted consensus network that provides a more stable and comprehensive representation of microbial interactions. The package includes customizable functions for network construction, visualization, and analysis, allowing users to explore network structures at different threshold levels and assess connectivity and reliability. CMiNet is designed to handle both quantitative and compositional data, ensuring broad applicability for researchers aiming to understand the intricate relationships within microbiome communities. Availability: Source code is freely available at https://github.com/solislemuslab/CMiNet.

翻译：理解微生物组内部的复杂相互作用对于探索其在健康和疾病中的作用至关重要。然而，由于不同网络推断算法的输出存在差异，构建可靠的微生物组网络常常面临挑战。为解决这一问题，我们提出了CMiNet，这是一个R软件包，旨在通过整合多种成熟网络构建方法的结果来生成共识微生物组网络。CMiNet整合了九种广泛使用的算法，包括Pearson、Spearman、双权重中值相关（Bicor）、SparCC、SpiecEasi、SPRING、GCoDA和CCLasso，以及一种基于条件互信息的新算法（CMIMN）。通过结合这些算法的优势，CMiNet生成一个单一的加权共识网络，从而提供更稳定、更全面的微生物相互作用表征。该软件包包含可自定义的网络构建、可视化和分析功能，允许用户在不同阈值水平下探索网络结构，并评估连接性和可靠性。CMiNet设计用于处理定量数据和组成型数据，确保对旨在理解微生物群落内复杂关系的研究者具有广泛的适用性。可用性：源代码可在https://github.com/solislemuslab/CMiNet免费获取。