Clustering trees: a visualization for evaluating clusterings at multiple resolutions
Luke Zappia, Alicia Oshlack
GigaScience | OXFORD UNIV PRESS | Published : 2018
Clustering techniques are widely used in the analysis of large datasets to group together samples with similar properties. For example, clustering is often used in the field of single-cell RNA-sequencing in order to identify different cell types present in a tissue sample. There are many algorithms for performing clustering, and the results can vary substantially. In particular, the number of groups present in a dataset is often unknown, and the number of clusters identified by an algorithm can change based on the parameters used. To explore and examine the impact of varying clustering resolution, we present clustering trees. This visualization shows the relationships between clusters at mul..View full abstract
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Awarded by National Health and Medical Research Council Career Development fellowship
L.Z. is supported by an Australian Government Research Training Program scholarship. A.O. is supported through a National Health and Medical Research Council Career Development fellowship (APP1126157). The Murdoch Children's Research Institute is supported by the Victorian Government's Operational Infrastructure Support Program.