ClusterONE ProCope plugin

Author: Tamás Nepusz, Haiyuan Yu, Alberto Paccanaro


This is the documentation of the ProCope plugin of ClusterONE, created by Tamás Nepusz, Haiyuan Yu and Alberto Paccanaro.

If you use results calculated by ClusterONE in a publication, please cite one of the suggested references.

The one-minute guide to using ClusterONE

ClusterONE is distributed in a Java archive file (JAR), whose name is likely to be something like cluster_one-X.Y.jar, where X.Y is the version number. This JAR file contains both the command line application and the ProCope plugin. In this document, we are assuming that you want to use ClusterONE from the ProCope graphical user interface. If you are interested in the ClusterONE command line interface, please refer to the documentation distributed with the command line interface itself.

ClusterONE embeds itself into ProCope as a custom clusterer. Since ProCope looks for plugins in its lib subfolder, you must place the downloaded JAR file in the lib subfolder of ProCope. You must also let ProCope know that it should look for a clustering algorithm named ClusterONE in the JAR files of the lib folder. This is done by creating a file called clusterers.xml in a subdirectory named .procope in your home folder. The contents of the file must be as follows:

<?xml version="1.0"?>
  <clusterer name="ClusterONE" class="">

The above file can also be found online, so it is enough to download it and place it in $HOME/.procope/clusterers.xml, where $HOME refers to your home directory.

After having installed ClusterONE, you can simply load your network into the ProCope GUI, then right-click (Ctrl-click on Mac) on it to bring up the network popup menu, and select the Cluster menu item. ClusterONE will be shown among the list of clustering methods available. Select ClusterONE and click OK. If you don't see ClusterONE in the list, the likely cause is that you have put clusterers.xml in the wrong place or it has an invalid format.

The next dialog you see will present the options of the ClusterONE algorithm itself. For the time being, just use the default settings and click on the Start button. ClusterONE will generate the predicted complexes, which will be listed in the Complex sets list box in the main ProCope window.

Description of the algorithm

ClusterONE strives to discover densely connected and possibly overlapping regions within the Cytoscape network you are working with. The interpretation of these regions depends on the context (i.e. what the network represents) and it is left up to you. For instance, in protein-protein interaction networks derived from high-throughput AP-MS experiments, these dense regions usually correspond to protein complexes or fractions of them. ClusterONE works by "growing" dense regions out of small seeds (typically one or two vertices), driven by a quality function called cohesiveness.

Before we move on to the formal definition of cohesiveness, let us introduce some terminology that classifies vertices and edges of a graph G according to their relationship to a selected group of vertices V0. Vertices of V0 are called internal vertices, while vertices not in V0 are called external vertices. An edge that is situated between two internal vertices is an internal edge, an edge going between an internal and an external vertex is a boundary edge, and an edge between two external vertices is an external edge. An internal vertex incident on at least one boundary edge is an internal boundary vertex, while an external vertex incident on at least one boundary edge is an external boundary vertex. The following figure illustrates these concepts:


Here, V0 itself is denoted by a shaded background, which delimits internal and external vertices. Thick black edges are internal, thin black edges are boundary edges, while thin gray dashed edges are completely external. Vertices marked by a letter are (internal or external) boundary vertices.

The quality of the group can be assessed by the number of internal edges divided by the sum of the number of internal and boundary edges. This quality measure is driven by the fact that a well-defined group should have many internal edges and only a few boundary edges; in other words, the boundary of the group should be sharp. If the edges have weights (i.e. a numeric value assigned to each edge that quantifies how reliable that edge is or how confident we are in its existence), the same guidelines apply, but the number of edges should be replaced by the total confidence associated to those edges. Whenever you have such confidence values, store them in a numeric edge attribute in Cytoscape and use that attribute to drive the cluster growth process. From now on, such confidence values will simply be called edge weights and the above mentioned quality measure will be referred to as cohesiveness.

ClusterONE essentially looks for groups of high cohesiveness. This is achieved by adopting a greedy strategy: starting from a single seed vertex (or a small set of vertices that are strongly bound together), one can extend the group step by step with new vertices so that the newly added vertex always increases the cohesiveness of a group as much as possible. Removals are also allowed if removing a vertex from the group increases its cohesiveness. The process stops when it is not possible to increase the cohesiveness of the group by adding another external boundary vertex or removing an internal boundary vertex. See the ClusterONE paper [1] for the description of the exact procedure. The growth process is repeated either for every vertex or every connected vertex pair to obtain an initial set of cohesive subgroups. Subgroups smaller than a given size or having a density less than a given threshold are thrown away. Finally, redundant cohesive subgroups (i.e. those that overlap significantly with each other) are merged to form larger subgroups to make the results easier to interpret.

Parameter settings

The parameters are grouped into basic and advanced ones. In most of the cases, the default values of the advanced parameters should be fine, but the basic parameters may need to be adjusted to your specific needs.

Basic parameters

Minimum size
The minimum size of clusters deemed relevant by ClusterONE. This is a hard threshold: whenever ClusterONE finds a cluster smaller than the minimum size, the cluster will be discarded immediately.
Minimum density
The minimum density of clusters deemed relevant by ClusterONE. The density of a cluster is the total sum of edge weights within the cluster, divided by the number of theoretically possible edges within the cluster. In other words, this is the average edge weight within the cluster if missing edges are assumed to have a weight of zero. Whenever ClusterONE finds a cluster that has a smaller density than the value given here, the cluster will be discarded immediately. Increase the minimum density if you get too many clusters and they seem too sparse, or decrease it if you are not getting enough clusters.

Advanced parameters

If you do not see these parameters in the ClusterONE parameters dialog, click on the Advanced parameters label to expand the container holding them.

Node penalty
Penalty value corresponding to each node. When you set this option to a specific value x, ClusterONE will assume that each node has an extra boundary weight of x when it considers the addition of the node to a cluster (see [1] for more details). It can be used to model the possibility of uncharted connections for each node, so nodes with only a single weak connection to a cluster will not be added to the cluster as the penalty value will outweigh the benefits of adding the node. The default penalty value is 2.
Merging method, Overlap threshold and Similarity function

After an initial set of clusters are found, ClusterONE tries to merge highly overlapping (and thus redundant) clusters in order to clean up the result. For each pair of clusters found, ClusterONE calculates a score that quantifies the overlap between them, and two clusters are merged if this overlap is larger than a given threshold (specified by the Overlap threshold textbox). There are four different ways to calculate the overlap score, as controlled by the Similarity function combobox:

  • The match coefficient takes the size of the overlap squared, divided by the product of the sizes of the two clusters being considered, as in the paper of Bader and Hogue [2].
  • The Simpson coefficient divides the size of the overlap by the size of the smaller cluster.
  • The Jaccard similarity divides the size of the overlap by the size of the union of the two clusters.
  • The Dice similarity divides twice the size of the overlap by the sum of the sizes of the two clusters.

Merging can be done in two different ways, as controlled by the Merging method combobox:

  • The single-pass method calculates similarity scores between all pairs of complexes and creates a graph where the nodes are the complexes and two nodes are connected if the corresponding complexes have a score higher than the overlap threshold. Complexes in the same connected component of the graph will then be merged.
  • The multi-pass method calculates similarity scores between all pairs of complexes and stores those pairs that have a score larger than the overlap threshold. The highest scoring pair is then merged and the similarity of the merged complex towards its neighbors is re-calculated. This is repeated until there are no more highly overlapping complexes in the result.

The default settings (match coefficient with a threshold of 0.8 using the single-pass algorithm) seem to be satisfactory for most use-cases Decreasing the threshold will result in more clusters being merged.

Seeding method

ClusterONE works by growing clusters from initial "seeds", driven by a goal function that is maximized greedily (see the Cluster ONE paper [1] for more details). A seed can be an arbitrary subgraph, but in most cases, it is either a single node or a single edge. The seeding method prescribes how the seeds are selected during the calculation:

  • From every node means that every node will be used as a seed.
  • From unused nodes means that nodes will be tried in the descending order of their weights (where the weight of a node is the sum of the weights on its incident edges), and whenever a cluster is found, the nodes in that cluster will be excluded from the list of potential seeds. In other words, the node with the largest weight that does not participate in any of the clusters found so far will be selected as the next seed.
  • From every edge means that every edge will be considered once, each yielding a seed consisting of the two endpoints of the edge.

In practical use-cases, the From unused nodes and From every node methods are almost equivalent, but the former one yields a smaller number of redundant clusters.


If you use results calculated by ClusterONE in a publication, please cite the following reference:

[1](1, 2, 3) Nepusz T, Yu H, Paccanaro A: Detecting overlapping protein complexes from protein-protein interaction networks. Nature Methods, Advance Online Publication, 2012. doi:10.1038/nmeth.1938

Some other papers that might be of interest (and were referenced earlier in this help file):

[2]Bader GD, Hogue CWV: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 2003, 4:2. doi:10.1186/1471-2105-4-2