基于结果导向的稀疏K-means算法在疾病亚型发现中的应用
Outcome-guided Sparse K-means for Disease Subtype Discovery via Integrating Phenotypic Data with High-dimensional Transcriptomic Data
摘要：
疾病亚型的发现是发展精准医学的重要一步，通过组学数据进行疾病亚型分型已成为一种流行的方法。虽然有希望，但从现有方法获得的亚型不一定与临床结果相关。随着现代流行病学队列中丰富的临床数据和组学数据，迫切需要开发一种结果引导的聚类算法，将表型数据与高维组学数据充分融合。因此，我们将稀疏K均值方法扩展为结果引导的稀疏K均值（GuidedSparseKmeans）方法。提出了一个统一的目标函数，它包括：（i）加权K-均值，以执行样本聚类;（ii）套索正则化，以执行基因选择从高维组学数据;（iii）纳入一个表型变量从临床数据集，以促进生物学意义的聚类结果。通过迭代优化目标函数，我们将同时获得一个表型相关的样本聚类结果和基因选择结果。我们通过与现有的聚类方法在乳腺癌和阿尔茨海默病的高维转录组数据的模拟和应用中的比较，证明了GuidedSparseKmeans的优越性能。我们的算法已经实现到一个R包中，该包在GitHub上公开提供（https：//github.com/LingsongMeng/GuidedSparseKmeans）。
英文摘要：
The discovery of disease subtypes is an essential step for developing precision medicine, and disease subtyping via omics data has become a popular approach. While promising, subtypes obtained from existing approaches are not necessarily associated with clinical outcomes. With the rich clinical data along with the omics data in modern epidemiology cohorts, it is urgent to develop an outcome-guided clustering algorithm to fully integrate the phenotypic data with the high-dimensional omics data. Hence, we extended a sparse K-means method to an outcome-guided sparse K-means (GuidedSparseKmeans) method. An unified objective function was proposed, which was comprised of (i) weighted K-means to perform sample clusterings; (ii) lasso regularizations to perform gene selection from the high-dimensional omics data; (iii) incorporation of a phenotypic variable from the clinical dataset to facilitate biologically meaningful clustering results. By iteratively optimizing the objective function, we will simultaneously obtain a phenotype-related sample clustering results and gene selection results. We demonstrated the superior performance of the GuidedSparseKmeans by comparing with existing clustering methods in simulations and applications of high-dimensional transcriptomic data of breast cancer and Alzheimer's disease. Our algorithm has been implemented into an R package, which is publicly available on GitHub (https://github.com/LingsongMeng/GuidedSparseKmeans).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
