标题： 一种混合混合方法用于聚类和表征癌症数据 显示英文标题

标题： A Hybrid Mixture Approach for Clustering and Characterizing Cancer Data

摘要： 基于模型的聚类广泛用于识别和区分疾病类型。 然而，高维的现代生物医学数据使得在传统聚类分析中进行模型估计变得具有挑战性。 将因子分析器纳入混合模型提供了一种表征大量数据特征的方法，但当前的估计方法由于嵌入算法的固有缓慢收敛性以及无法调整因子分析器的大小，导致在大规模数据上计算上不可行，从而阻碍了广义因子分析器混合模型的实现以及对数据聚类的进一步表征。 我们提出了一种混合的无矩阵计算方案，以高效地估计基于高斯混合模型和广义因子分析器的聚类和模型参数，从而使用少量潜在因子总结大量变量。 我们的方法在保持高聚类准确性的前提下，比现有方法具有更快的收敛速度。 我们的算法被应用于基于大量肿瘤样本准确识别和区分乳腺癌类型，并利用大量基因记录为淋巴瘤亚型提供广义表征。 显示英文摘要

摘要： Model-based clustering is widely used for identifying and distinguishing types of diseases. However, modern biomedical data coming with high dimensions make it challenging to perform the model estimation in traditional cluster analysis. The incorporation of factor analyzer into the mixture model provides a way to characterize the large set of data features, but the current estimation method is computationally impractical for massive data due to the intrinsic slow convergence of the embedded algorithms, and the incapability to vary the size of the factor analyzers, preventing the implementation of a generalized mixture of factor analyzers and further characterization of the data clusters. We propose a hybrid matrix-free computational scheme to efficiently estimate the clusters and model parameters based on a Gaussian mixture along with generalized factor analyzers to summarize the large number of variables using a small set of underlying factors. Our approach outperforms the existing method with faster convergence while maintaining high clustering accuracy. Our algorithms are applied to accurately identify and distinguish types of breast cancer based on large tumor samples, and to provide a generalized characterization for subtypes of lymphoma using massive gene records.