Quantitative Arbor Analytics: Unsupervised Harmonic Co-Clustering of Populations of Brain Cell Arbors Based on L-Measure
Quantitative Arbor Analytics: Unsupervised Harmonic Co-Clustering of Populations of Brain Cell Arbors Based on L-Measure
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DOI：
10.1007/s12021-014-9237-2
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发表时间：
2015-01-01
期刊：
影响因子：
3
通讯作者：
Roysam, Badrinath
中科院分区：
文献类型：
--
作者：
Lu, Yanbin;Carin, Lawrence;Roysam, Badrinath
本文提出了一种鲁棒的无监督谐波共聚类方法，用于分析重建脑细胞集合的乔木形态（例如，神经元、小胶质细胞）。具体而言，该方法可以识别具有相似乔木形态的细胞的组和子组，并且同时识别定量乔木测量值之间的分层分组模式。所提出的算法的鲁棒性来自使用的扩散距离测量比较多变量数据点，谐波分析理论，和Haar样小波基的多变量数据平滑。该算法被设计为实际可用，并嵌入到免费开源FARSIGHT图像分析工具包中的主动链接三维（3-D）可视化和分析系统中，用于交互式探索性人群规模神经解剖学研究。对合成数据集的研究表明，与现有的层次聚类算法相比，该算法在聚类数据矩阵方面具有优越性。从NeuroMorpho数据库中提取的真实的神经元3-D重建的异质集成的研究表明，所提出的方法确定有意义的分组模式之间的神经元乔木形态的基础上，揭示了潜在的形态差异。
This paper presents a robust unsupervised harmonic co-clustering method for profiling arbor morphologies for ensembles of reconstructed brain cells (e.g., neurons, microglia) based on quantitative measurements of the cellular arbors. Specifically, this method can identify groups and sub-groups of cells with similar arbor morphologies, and simultaneously identify the hierarchical grouping patterns among the quantitative arbor measurements. The robustness of the proposed algorithm derives from use of the diffusion distance measure for comparing multivariate data points, harmonic analysis theory, and a Haar-like wavelet basis for multivariate data smoothing. This algorithm is designed to be practically usable, and is embedded into the actively linked three-dimensional (3-D) visualization and analytics system in the free and open source FARSIGHT image analysis toolkit for interactive exploratory population-scale neuroanatomic studies. Studies on synthetic datasets demonstrate its superiority in clustering data matrices compared to recent hierarchical clustering algorithms. Studies on heterogeneous ensembles of real neuronal 3-D reconstructions drawn from the NeuroMorpho database show that the proposed method identifies meaningful grouping patterns among neurons based on arbor morphology, and revealing the underlying morphological differences.