Epistatic Clustering: A Model-Based Approach for Identifying Links Between Clusters
上位作用聚类：识别簇间联系的模型方法AI 解读 ↓
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上位作用聚类：识别簇间联系的模型方法
- 研究问题
- 当观测可能同时属于多个簇且簇之间存在联系时，互斥聚类假设会怎样影响潜在簇识别，如何改善模型拟合？
- 方法与思路
- 作者在高斯混合模型中加入上位作用项，以广义EM算法求最大似然估计，用BIC选阶，并提出自助法拟合比较检验。
- 材料陈述的贡献
- 摘要称理论计算、模拟和真实数据揭示簇间联系会影响聚类准确性，所提方法能减轻这一影响，并考察估计量的渐近性质。
- 阅读关注点
- 应核对上位作用项如何表示簇间联系、模型可识别性、选阶与自助检验的条件，以及模拟比较采用的聚类评价指标。
- 阅读边界
- 材料为版本未注明的 OpenAlex 索引摘要；未给出渐近性质的具体条件或改善幅度，拟合改善不能外推为普遍优势。
原文摘要
Most clustering methods assume that the data can be represented by mutually exclusive clusters, although this assumption may not be the case in practice. For example, in gene expression microarray studies, investigators have often found that a gene can play multiple functions in a cell and may, therefore, belong to more than one cluster simultaneously, and that gene clusters can be linked to each other in certain pathways. This article examines the effect of the above assumption on the likelihood of finding latent clusters using theoretical calculations and simulation studies, for which the epistatic structures were known in advance, and on real data analyses. To explore potential links between clusters, we introduce an epistatic mixture model which extends the Gaussian mixture by including epistatic terms. A generalized expectation-maximization (EM) algorithm is developed to compute the related maximum likelihood estimators. The Bayesian information criterion is then used to determine the order of the proposed model. A bootstrap test is proposed for testing whether the epistatic mixture model is a significantly better fit to the data than a standard mixture model in which each data point belongs to one cluster. The asymptotic properties of the proposed estimators are also investigated when the number of analysis units is large. The results demonstrate that the epistatic links between clusters do have a serious effect on the accuracy of clustering and that our epistatic approach can substantially reduce such an effect and improve the fit.