Supporting data for "Clustering trees: a visualisation for evaluating clusterings at multiple resolutions"

Dataset type: Transcriptomic, Software
Data released on June 27, 2018

Zappia L; Oshlack A (2018): Supporting data for "Clustering trees: a visualisation for evaluating clusterings at multiple resolutions" GigaScience Database.


Clustering techniques are widely used in the analysis of large data sets 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 data set 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 visualisation shows the relationships between clusters at multiple resolutions allowing researchers to see how samples move as the number of clusters increases. In addition, meta-information can be overlaid on the tree to inform the choice of resolution and guide in identification of clusters. We illustrate the uses of clustering trees using two examples, the classical iris dataset and a complex single-cell RNA-sequencing dataset.

Sample IDTaxonomic IDCommon NameGenbank NameScientific NameSample Attributes
pbmc6k9606HumanhumanHomo sapiens Description:RNA-seq transcript sequences from 2700 Peripheral blood mononuclear cells (PBMCs) from a healthy donor
Sample source:10x Genomics
Cell type:Peripheral Blood Mononuclear Cells
Displaying 1-1 of 1 Sample(s).

File NameSample IDData TypeFile FormatSizeRelease Date 
GitHub archivearchive4.24 MB2018-06-23
GitHub archivearchive17.02 MB2018-06-23
pbmc6ktranscriptome sequenceTAR7.27 MB2018-06-23
ReadmeTEXT2 KB2018-06-23
Displaying 1-4 of 4 File(s).
Funding body Awardee Award ID Comments
Australian National Health and Medical Research Council A Oshlack APP1126157 Career Development Fellowship
Australian Government Department of Education L Zappia Research Training Program (RTP) Scholarship
Date Action
June 27, 2018 Dataset publish