聚类分析
计算机科学
层次聚类
粒度
协议(科学)
数据挖掘
人工智能
共识聚类
缺少数据
模式识别(心理学)
机器学习
相关聚类
CURE数据聚类算法
医学
替代医学
病理
操作系统
作者
Diek W. Wheeler,Giorgio A. Ascoli
标识
DOI:10.4103/nrr.nrr-d-24-00532
摘要
Many fields, such as neuroscience, are experiencing the vast proliferation of cellular data, underscoring the need for organizing and interpreting large datasets. A popular approach partitions data into manageable subsets via hierarchical clustering, but objective methods to determine the appropriate classification granularity are missing. We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters. Here we present the corresponding protocol to classify cellular datasets by combining data-driven unsupervised hierarchical clustering with statistical testing. These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values, including molecular, physiological, and anatomical datasets. We demonstrate the protocol using cellular data from the Janelia MouseLight project to characterize morphological aspects of neurons.
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