Clustering with variable selection is a challenging but critical task for modern small-n-large-p data. Existing methods based on Gaussian mixture models or sparse K-means provide solutions to continuous data. With the prevalence of RNA-seq technology and lack of count data modeling for clustering, the current practice is to normalize count expression data into continuous measures and apply existing models with Gaussian assumption. In this paper, we develop a negative binomial mixture model with gene regularization to cluster samples (small $n$) with high-dimensional gene features (large $p$). EM algorithm and Bayesian information criterion are used for inference and determining tuning parameters. The method is compared with sparse Gaussian mixture model and sparse K-means using extensive simulations and two real transcriptomic applications in breast cancer and rat brain studies. The result shows superior performance of the proposed count data model in clustering accuracy, feature selection and biological interpretation by pathway enrichment analysis.
翻译:现有基于高斯混合模型或稀疏的K手段的方法为连续数据提供了解决办法。随着RNA-Seq技术的普及和集群缺乏计数数据模型的缺乏,目前的做法是将计数表达数据标准化为连续措施,并应用高斯假设的现有模型。在本文件中,我们开发了一个负二进制混合模型,将基因正规化为具有高维基因特征的集束样本(小美元)的基因(大额美元)。EM算法和贝叶斯信息标准用于推断和确定调试参数。该方法与稀有高斯混合模型和稀有K手段进行比较,使用广泛的模拟和乳腺癌和老鼠大脑研究中两种真正的超脱血组应用。结果显示,拟议的计数数据模型在集精度、特征选择和通过电路浓缩分析进行生物解释方面表现优异。