Data-display networks are a means of visualizing complex patterns in multivariate data. One particular use is for displaying the patterns in a set of trees. For example, Consensus Networks and SuperNetworks are splits graphs that display the patterns common to some specified subset of a collection of trees (eg. a set of equally optimal trees, or a set of trees sampled by a bayesian or bootstrap analysis). Alternatively, Parsimony Networks try to simultaneously display all of the trees in a collection of most-parsimonious trees for a single dataset.
Another display method for multiple trees is what has been called a Cloudogram (see the post Cloudograms and data-display networks). These superimpose the set of all trees arising from an analysis, so that dark areas in such a diagram will be those parts where many of the trees agree on the topology, while lighter areas will indicate disagreement.
Yet another method for combining trees into a graph while retaining all of the original information from the source trees is the Tree Alignment Graph (TAG), an idea introduced by Stephen A. Smith, Joseph W. Brown and Cody E. Hinchliff (2013. Analyzing and synthesizing phylogenies using tree alignment graphs. PLoS Computational Biology 9: e1003223).
The authors note:
These methods address the problem of identifying common nodes and edges across sets of phylogenetic trees and constructing a data structure that efficiently contains this information while retaining original source information ... Mapping trees into a TAG exploits the fact that rooted phylogenetic trees are in fact a specific type of graph: they are directed, acyclic, and require that each node has, at most, one parent. By relaxing these requirements, we can combine multiple trees into a common graph, while minimizing changes to the semantic interpretations of nodes and edges in the trees. Because they contain nodes and edges directly analogous to those from their source trees, TAGs have the desirable quality of retaining the full identifiability of the original source trees they contain. Additionally, because they are not restricted to the bifurcating model of evolution, TAGs may represent conflict among source trees as reticulations in the graph.The basic principal is illustrated in the first figure (about). Internal nodes represent collections of terminal nodes, and arcs (directed edges) represent their relationships. Nodes and arcs are added to the growing TAG, each of which represents one relationship shown in one of the original trees. TAG A in the figure shows the result of combining the black, blue and orange trees, while TAG B shows the result of then adding the gray and green trees to TAG A (the arcs are colour-coded). The resulting TAG is thus a database of all of the original information, which can then be queried in any way to provide summaries of the data. In particular, standard network summaries can be used, such as node degree, which will highlight parts of the TAG with interesting characteristics.
The authors provide two empirical examples of applications. The one shown here involves 100 bootstrap trees for 640 species representing the majority of known lineages from the Angiosperm Tree of Life dataset (chloroplast, mitochondrial, and ribosomal data). The TAG is shown lightly in the background. Superimposed on this, the nodes are coloured to represent the effective number of parent nodes, and their size represents node bootstrap support. Highly supported nodes with a low number of effective parents (large blue nodes) are frequently recovered and confidently placed in the source trees, while highly supported nodes with a low number of effective parents (large and pink or orange) are frequently resolved in the source trees but their placement varies among bootstrap replicates. So, the three largest problem areas as illustrated in the TAG correspond to the Malpighiales, Lamiales and Ericales.
For comparison, a NeighborNet analysis of the same data is shown in the blog post When is there support for a large phylogeny? This simply shows an unresolved blob.