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The project formerly known as treescape has been renamed to treespace to avoid confusion with another software with similar remit, TreeScaper.
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> Input

> Analysis

> Aesthetics

Label / point color















> Input

>> Tree view

>> Tree selection

> Aesthetics

Tip label colour

Label colour for tips with same ancestry

Label colour for tips with smaller ancestral differences

Label colour for tips with greater ancestral differences

Edge colour















Tree viewer



> Copy of scatter plot



> Input

> Aesthetics















densiTree viewer




[statistical exploration of landscapes of phylogenetic trees]

treescape implements statistical tools for the exploration of sets of phylogenetic trees describing the evolutionary relationships between the same taxa. This web interface provides an easy access to the resources implemented in the package. Each tab is made of two panels: a sidebar used to choose inputs, analysis tools and aesthetics, and a main panel displaying results.

Tree landscape explorer

The Tree landscape explorer tab is where the whole tree space can be explored. Choose between a two- or three-dimensional plot to visualise the trees using Metric Multidimensional Scaling (MDS, a.k.a. Principal Coordinates Analysis, PCoA), which calculates the best reduced-spaced visualisation of the distances between trees. The sidebar contains the following sections:
  • Input: to upload the set of trees to analyse
  • Analysis: to customize the analysis
  • Aesthetics: to customize the graphics

Input

treescape takes a list of phylogenetic trees as input. The user can choose between data distributed with the package, or provide input files. Two types of input files can be used:
  • R objects saved using the function save(x, file="x.RData") where 'x' is a list of trees of the class multiphylo (from the ape package. Accepted extensions are ".RData" and ".rda".
  • list of trees saved in a nexus file, e.g. using ape's function write.nexus(x, file="x.nex") in R.

Analysis

Tree summary / metric: the method to be used to measure distances between tips of the trees. Choose from:
  • Kendall Colijn: the tree metric developed by Kendall & Colijn; used by default
  • Billera, Holmes, Vogtmann: the Billera, Holmes & Vogtmann tree metric (also known as the 'geodesic distance')
  • Robinson Foulds (unrooted): the Robinson Foulds tree metric. Note that this implementation of the metric (from the package phangorn) treats the trees as unrooted and uses the unweighted edge-count distance (Robinson Foulds 1981).
  • Tip-tip path distance (unrooted): metric by Steel and Penny which counts the number of internal nodes on the shortest path between each pair of tips. Along with its weighted version (below), this is also known as the tip distance, nodal distance, patristic distance and dissimilarity measure. Trees are treated as unrooted. (see ?nNodes in the package adephylo)
  • Tip-tip branch-length distance (unrooted): similar to the tip-tip path distance, but using the branch lengths instead of counting the edges. (see ?distTips in the package adephylo)
  • Abouheif test: the Abouheif test as presented in Pavoine et al. (2008) (see ?distTips in the package adephylo)
  • Sum of direct descendents: another test related to the Abouheif test (see ?distTips in the package adephylo)
Lambda: The value of lambda used in Kendall & Colijn's metric.
Number of MDS axes retained : The number of principal components to retain in the Metric Multidimensional Scaling (MDS).
Assess quality of projection (Shepard plot)? It is important to be aware of how well or otherwise the Multidimensional Scaling (MDS) plot represents the tree space. Euclidean metrics lend themselves to MDS plotting, whereas other metrics and summaries may prove difficult to accurately project into a small number of dimensions. A Shepard plot is a scatter plot of the actual distances in tree space (x-axis) versus the projected distances in the plot (y-axis). The stronger the correlation, the better the MDS plot represents the true distances.
Identi