Consensus clustering has been widely used in bioinformatics and other applications to improve the accuracy, stability and reliability of clustering results. This approach ensembles cluster co-occurrences from multiple clustering runs on subsampled observations. For application to large-scale bioinformatics data, such as to discover cell types from single-cell sequencing data, for example, consensus clustering has two significant drawbacks: (i) computational inefficiency due to repeatedly applying clustering algorithms, and (ii) lack of interpretability into the important features for differentiating clusters. In this paper, we address these two challenges by developing IMPACC: Interpretable MiniPatch Adaptive Consensus Clustering. Our approach adopts three major innovations. We ensemble cluster co-occurrences from tiny subsets of both observations and features, termed minipatches, thus dramatically reducing computation time. Additionally, we develop adaptive sampling schemes for observations, which result in both improved reliability and computational savings, as well as adaptive sampling schemes of features, which leads to interpretable solutions by quickly learning the most relevant features that differentiate clusters. We study our approach on synthetic data and a variety of real large-scale bioinformatics data sets; results show that our approach not only yields more accurate and interpretable cluster solutions, but it also substantially improves computational efficiency compared to standard consensus clustering approaches.
翻译:在生物信息学和其他应用中广泛使用共识群集,以提高群集结果的准确性、稳定性和可靠性。这一方法将多组群集的共同现象混为一谈,在次级抽样观测中进行。对于大规模生物信息数据的应用,例如从单细胞测序数据中发现细胞类型,共识群集有两个重大缺点:(一) 反复应用群集算法导致的计算效率低下,以及(二) 缺乏对不同群集重要特征的解释性。在本文件中,我们通过开发IMACC来应对这两个挑战:可解释的小型可适应共识群集。我们的方法采用了三大创新。我们共同使用两种观测和特征的微小组群群群群群群群群群群群群群,称为微型群群,从而大大缩短了计算时间。此外,我们为观察制定了适应性抽样计划,这既提高了可靠性,也提高了计算节省了计算率,也降低了特征的适应性抽样计划,通过快速学习最相关的群集群集群集群集特征,从而导致可以解释的解决办法。我们的研究方法采用了三大创新方法,我们只对合成数据和大规模数据计算结果进行精确的计算,我们只进行对比,我们的方法也只是比较了实际的分类群集计算。