A novel method for clustering cellular data to improve classification  

作　　者：Diek W.Wheeler Giorgio A.Ascoli 

机构地区：[1]Center for Neural Informatics,Structures,&Plasticity,Krasnow Institute for Advanced Study [2]Bioengineering Department,Volgenau School of Engineering [3]George Mason University,Fairfax,VA,USA

出　　处：《Neural Regeneration Research》2025年第9期2697-2705,共9页中国神经再生研究（英文版）

基　　金：supported in part by NIH grants R01NS39600,U01MH114829;RF1MH128693(to GAA)。

摘　　要：Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r 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 datadriven 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 molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

关 键 词：cellular data clustering dendrogram data classification Levene's one-tailed statistical test unsupervised hierarchical clustering 

分 类 号：Q2-33[生物学—细胞生物学]