  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.
