Clustering trees: a visualization for evaluating clusterings at multiple resolutions

methods
visualisation
clustering
software
Authors

Luke Zappia

Alicia Oshlack

Date

July 1, 2018

Links
Citation stats
Abstract

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 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 features of clustering trees using a series of simulations as well as two real examples, the classical iris dataset and a complex single-cell RNA-sequencing dataset. Clustering trees can be produced using the clustree R package, available from CRAN and developed on GitHub.

Citation

BibTeX citation:
@article{zappia2018,
  author = {Zappia, Luke and Oshlack, Alicia},
  title = {Clustering Trees: A Visualization for Evaluating Clusterings
    at Multiple Resolutions},
  journal = {GigaScience},
  volume = {7},
  number = {7},
  date = {2018-07-01},
  url = {https://lazappi.id.au/publications/2018-zappia-clustree/},
  doi = {10.1093/gigascience/giy083},
  issn = {2047-217X},
  langid = {en},
  abstract = {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 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 features of clustering
    trees using a series of simulations as well as two real examples,
    the classical iris dataset and a complex single-cell RNA-sequencing
    dataset. Clustering trees can be produced using the clustree R
    package, available from CRAN and developed on GitHub.}
}
For attribution, please cite this work as:
Zappia, Luke, and Alicia Oshlack. 2018. “Clustering Trees: A Visualization for Evaluating Clusterings at Multiple Resolutions.” GigaScience 7 (July). https://doi.org/10.1093/gigascience/giy083.