A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random
缺失非随机下基于模型聚类的变量选择统一框架
Model-based clustering integrated with variable selection is a powerful tool for uncovering latent structures within complex data. However, its effectiveness is often hindered by challenges such as identifying relevant variables that define heterogeneous subgroups and handling data that are missing not at random, a prevalent issue in fields like transcriptomics. While several notable methods have been proposed to address these problems, they typically tackle each issue in…