Visualise Clusterings at Different Resolutions

Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases.

clustree

[]https://app.codecov.io/github/lazappi/clustree?branch=master)

Deciding what resolution to use can be a difficult question when approaching a clustering analysis. One way to approach this problem is to look at how samples move as the number of clusters increases. This package allows you to produce clustering trees, a visualisation for interrogating clusterings as resolution increases.

Installation

You can install the release version of clustree from CRAN with:

install.packages("clustree")

If you want to use the development version that can be installed from GitHub using the remotes package:

# install.packages("remotes")
remotes::install_github("lazappi/clustree@develop")

To also build the vignettes use:

# install.packages("remotes")
remotes::install_github("lazappi/clustree@develop", dependencies = TRUE,
                         build_vignettes = TRUE)

NOTE: Building the vignettes requires the installation of additional packages.

Documentation

The documentation for clustree is available from CRAN at https://cran.r-project.org/package=clustree.

To view the vignette and all the package documentation for the development version visit http://lazappi.github.io/clustree.

Citing clustree

If you use clustree or the clustering trees approach in your work please cite our publication "Zappia L, Oshlack A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. Gigascience. 2018;7. DOI:gigascience/giy083.

citation("clustree")
 
   Zappia L, Oshlack A. Clustering trees: a visualization for
   evaluating clusterings at multiple resolutions. GigaScience.
   2018;7. DOI:gigascience/giy083
 
A BibTeX entry for LaTeX users is
 
   @Article{,
     author = {Luke Zappia and Alicia Oshlack},
     title = {Clustering trees: a visualization for evaluating clusterings at
              multiple resolutions},
     journal = {GigaScience},
     volume = {7},
     number = {7},
     month = {jul},
     year = {2018},
     url = {http://dx.doi.org/10.1093/gigascience/giy083},
     doi = {10.1093/gigascience/giy083},
   }

Contributors

Thank you to everyone who has contributed code to the clustree package:

• @andreamrau - added the edge_arrow_ends option
• @mojaveazure - added support for Seurat v3 objects