基于贝叶斯社区检测的可靠数据聚类
Reliable data clustering with Bayesian community detection
AI总结：
本研究提出利用贝叶斯社区检测方法（度修正随机块模型和正则化映射方程）结合最小描述长度原则，实现抗噪声的可靠数据聚类，在合成数据和基因共表达数据上优于传统方法。
摘要：
从神经科学和基因组学到系统生物学和生态学，研究人员依赖对相似性数据进行聚类以揭示模块化结构。然而，广泛使用的聚类方法，如层次聚类、k-means和WGCNA，缺乏原则性的模型选择，使其易受噪声影响。一种常见的变通方法是在聚类前对相关矩阵表示进行稀疏化以去除噪声，但这一额外步骤引入了可能扭曲结构并导致不可靠结果的任意阈值。为了检测可靠的聚类，我们利用网络科学的最新进展，将稀疏化和聚类与原则性的模型选择相结合。我们测试了两种贝叶斯社区检测方法，即度修正随机块模型和正则化映射方程，两者均基于最小描述长度原则进行模型选择。在合成数据中，它们优于传统方法，能在高噪声条件和较少样本下检测到植入的聚类。与WGCNA在基因共表达数据上的比较表明，正则化映射方程识别出更稳健且功能更连贯的基因模块。我们的结果确立了贝叶斯社区检测作为一种原则性且抗噪声的框架，用于跨领域揭示高维数据中的模块化结构。
英文摘要：
From neuroscience and genomics to systems biology and ecology, researchers rely on clustering similarity data to uncover modular structure. Yet widely used clustering methods, such as hierarchical clustering, k-means, and WGCNA, lack principled model selection, leaving them susceptible to noise. A common workaround sparsifies a correlation matrix representation to remove noise before clustering, but this extra step introduces arbitrary thresholds that can distort the structure and lead to unreliable results. To detect reliable clusters, we capitalize on recent advances in network science to unite sparsification and clustering with principled model selection. We test two Bayesian community detection methods, the Degree-Corrected Stochastic Block Model and the Regularized Map Equation, both grounded in the Minimum Description Length principle for model selection. In synthetic data, they outperform traditional approaches, detecting planted clusters under high-noise conditions and with fewer samples. Compared to WGCNA on gene co-expression data, the Regularized Map Equation identifies more robust and functionally coherent gene modules. Our results establish Bayesian community detection as a principled and noise-resistant framework for uncovering modular structure in high-dimensional data across fields.
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