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Hierarchical clustering is considered an unsupervised clustering method. Unsupervised clustering methods do not take the identity or attributes of samples into account when clustering. This means that all experimental variables such as treatment, phenotype, tissue, number of expected groups, etc. do not guide or bias cluster building. Alternatively, supervised clustering methods do consider experimental variables when building clusters.

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