MCODE User's Manual

TableOfContents()

Installation

To use the MCODE PlugIn, you must first obtain a copy of Cytoscape. The compatible MCODE and Cytoscape versions are outlined in the downloads section on the [:Software/MCODE: MCODE website]. The lastest MCODE, version 1.2, requires Cytoscape 2.3.2 or later.

• You can download a copy of Cytoscape from: http://www.cytsoscape.org.

Once you have downloaded Cytoscape and verified that it works, proceed with the next steps:

1. Copy the MCODE.jar file to your [Cytoscape_Home]/plugins directory.
2. Start Cytoscape. This can be done by double-clicking the newly created Cytoscape icon or via commandline:
   • On Unix/Linux or MacOS X, run: cytoscape.sh
   • On Windows, run: cytoscape.bat
3. Check that MCODE appears in the Plugins menu of Cytoscape
   • If it does not, then you likely placed the MCODE.jar file in the wrong directory. Repeat step one. You will have to restart Cytoscape to reload the plugin.

Running MCODE

MCODE is an extension for Cytoscape and can only be accessed through Cytoscape.

1. Start Cytoscape
2. Go to the Plugins Menu
3. Move the mouse over MCODE

4. Click Start MCODE

   • The main MCODE interface will appear as a tab in the left-hand panel of Cytoscape

Note: If MCODE does not appear in the Plugins Menu, then the installation of MCODE was not successfull. Please refer to the previous section of the User's Manual.

This menu provides two additional options:

• About MCODE

  • A quick reference to MCODE acknowledgements, citation information, and a link to the MCODE paper.

• Help

  • A link that will open the MCODE website at [:Home: www.baderlab.org] in your default browser for quick access to downloads, contact information, and the User's Manual with help and tutorials.

New to Version 1.2: MCODE can now be seen as a console for the underlying algorithm. It can be started independent of network loading. This provides for a more easily accessible, repeatable and modifyable analysis.

Getting and Interpreting Results

The MCODE Main Panel is the starting point for analysis. It contains two main sections: Find Cluster(s) and Advanced Options. The latter is intended for fine-tuning of results by experienced users who are familiar with the MCODE Paper. These options will be discussed in the next secton. This section will cover some of the basic steps of running MCODE on a network.

1. LOAD YOUR NETWORK. To begin, make sure the network to be analyzed is loaded into Cytoscape. You can load as many networks as your computer system can handle, large or small. MCODE will recognize which network you wish to analyze either by noting which network view is on top or by your selection of the network in the Network Tab on the left-hand panel.

2. CHOOSE THE SCOPE. Cluster results can be reported in two fundamental ways with MCODE. This can be referred to as the scope of the process and can be opted in the Find Cluster(s) section of the main panel.

   1. Find Clusters in Whole Network
      • MCODE will find and report all clusters in the entire network.
   2. Find Clusters from Selection
      • Only those clusters which include the selected node(s) as within them will be reported.
      • Selections can be made either in the view directly, the native Node Attribute Browser or Cytoscape's handy search tool.
   3. The choice of scope is dependent on your familiarity with the network in question and the desired outcome. Having a particular protein of interest within a network, for example, it may be appropriate to search for only those clusters involving such a protein. On the other hand, uniformed, exploratory work will most benefit from a whole network scope.

3. ANALYZE YOUR NETWORK. Next, press Analyze. This will display a task monitor reporing the progress of the scoring, finding, and drawing steps, provided that the task is not too quick.

   • You may see several different messages at this step:
     1. The "No network" message
        • This means that MCODE failed to detect a loaded network for analysis. You must load a network, and make sure it is selected, before you can anaylze it.
     2. The "No selection" message
        • This message appears when the Selection scope is used without an actual selection. You must select the desired node(s) before MCODE can attempt to find clusters.
     3. The "Parameters unchanged" message
        • Parameters are discussed in some detail in the following section. For now, you should know that if you attempt to analyze a network twice without changing any of the settings, such as the scope of the analysis, MCODE will let you know that this analysis was previously conducted and will consequently display the previously attained results.

4. BROWSE YOUR RESULTS. If everything goes to plan, a new tab will appear in the right-hand panel displaying the results as "Result 1"

   • Cluster Browser:

     • On the left side, in the Network column, is a graphical representation of the cluster.

       • Cluster members are coloured red.

       • The highest scoring node in the cluster is called the Seed. It is the node from which the cluster was derived and is represented by a square shape.

       • Other cluster members are circular.
       • Edges, representing interactions for example, are blue.
       • Edge directionality is represented by cyan arrows.

     • On the right side, in the Details column, is a statisical summary of the cluster.

       • Rank is based on the cluster's computed Score and is used to ID the clusters within each result set.

         • For example, Cluster 1 is the highest ranked cluster in a given result set, and thus, at the top of the list.

       • Nodes and Edges is a simple enumeration of the cluster's members and their interconnections.

     • These results can be discarded at any time by pressing the Discard Result button at the bottom of the panel.

   • Network View:

     • If the network being analyzed has a view, MCODE will apply a custom visual style utilizing two MCODE generated node attributes as soon as the network is analyzed.

       • MCODE_Node_Status: Node shapes indicate the cluster status of the nodes.

         • Square: seed (highest score in the cluster)
         • Circle: clustered
         • Diamond: unclustered

       • MCODE_Score: Node colors represent the node score.

         • A range from black to red indicates the MCODE computed node score (lowest to highest, respectively).
         • White indicates a score of zero.

       • MCODE_Cluster: This is an additional list type attribute that indicates which cluster the node belongs to. The MCODE visual style does not make use of this attribute, but it is there in case it may be of some use. Note that if the Fluff parameter (discussed in the following section) is turned on, some nodes may belong to more than one cluster.

   • Cluster Selection:

     • The clusters in the cluster browser table are selectable and will automatically select the corresponding nodes in the network view (if it exists). If no network view is available, the selected nodes can be reviewed in the Cytoscape native Node Attribute Browser.

     • Secondly, a new Cluster Exploration Panel will appear below the Cluster Browser titled "Explore: Cluster [Rank]". This panel can be collapsed for now -- it's use will be discussed in the Exploring Results section of this Manual.

This is a screeen shot of the MCODE Main Panel and MCODE Result Panel containing the Cluster Browser

• attachment:main_panel.gif attachment:cluster_browser.gif

Fine-Tuning Your Analysis

By default, MCODE analyzes networks using scoring and finding parameters that have been optimized to produce the best results for the average user and network. However, you may benefit greatly by familiarizing yourself with these parameters. Sometimes even slight customizations can produce considerable differences, reduce unwanted or false results, and increase relevance to your network. This is only an overview -- for a detailed insight into how these parameters function, it is best to consult the MCODE paper.

Scoring Parameters

1. Include Loops

   • When turned on, MCODE will include loops (self-edges) in the neighbourhood density calculation. This is expected to make a small difference in the results.

2. Degree Cutoff

   • This value controls the minimum degree (number of connections) necessary in order for a node to be scored. For example, nodes that share only one connection with one other node have a degree of 1. Valid values are 2 or higher to prevent singly connected nodes from getting an artificially high node score.

Finding Parameters

1. Haircut

   • Once a cluster has been found, some nodes which may have satisfied the Degree Cutoff parameter during scoring may only be connected to the cluster by one edge. When haircut is turned on, MCODE removes all such singly-connected nodes from clusters.

2. Fluff

   • When turned on, MCODE expands cluster cores by one neighbour shell outwards. This is applied after the optional haircut step and within the confines of the Node Density Cutoff parameter.

   • Node Density Cutoff

     • Node density is calculated by dividing the node's connections by the maximum number of connections possible for that node. If Fluff is turned on, this parameter controls the neighbour inclusion criteria. Fluff expansion occurs after the cluster has already been defined by the algorithm and thus allows clusters to overlap at their edges. A higher value will expand clusters more.

3. Node Score Cutoff

   • This is the most influential parameter for cluster size and is the basis for the Size Threshold Slider in the Exploring Results section. During cluster expansion, this cutoff prevents new members from being added if their node score deviates from the cluster's seed node's score by more than the parameter allows. It is taken as a percentage where a value of 0.2 allows for new members' node scores to be no more than 20% less than that of the seed node. Thus, smaller values create smaller clusters and vice versa.

4. K-Core

   • This parameter filters out clusters that do not contain a maximally inter-connected sub-cluster of at least k degrees. For example, a triangle (3 nodes, 3 edges) is a 2-core (2 connections per node). Two nodes with 2 edges between them satisfy the 2-core rule as well. Since the default value is 2, this ensures that clusters must in the very least contain one of these two sub-clusters. Increasing this value will exclude smaller clusters.

5. Max. Depth

   • Maximum depth limits the distance from the seed node whithin which MCODE can search for cluster members. By default this is set to an arbitrarily large number so that clusters are virtually unlimited. To limit cluster size, set this parameter to a small number.

New to Version 1.2: The user can now analyze a network as many times as desired by modifying the parameters. Each result is stored sequentially for reference and comparison. Viewing different results will automatically rewrite the MCODE node attributes and revisualize the network. Note that MCODE can independently determine which portion of the algorithm needs to be conducted based on the user's parameter modifications. If the scoring parameters are altered, the given network will be rescored. If only the cluster finding parameters are altered, only the cluster finding portion will be conducted.

Exploring Results in Real-Time

In addition to fine-tuning a multitude of parameters to enhance the analysis process, MCODE provides a real-time cluster exploration feature. This can be divided into two components: exploring cluster boundaries and exploring cluster content. The first exploration allows you to expand or reduce the cluster based on the node score using the Size Threshold Slider. The second is the Node Attribute Enumerator which provides a summary of the cluster's node attributes and their occurrences in the cluster. Together they can inform the user about the cluster's "natural" boundaries in the context of the network and ensure functional consistancy. These are both explained in greater detail bellow.

Size Threshold Slider

The slider scale ranges from Min to Max and has a marker (^) for the initial position. The main parameter being manipulated is the Node Score Cutoff which, as previously mentioned, is the most influential cluster size modifier. As such, the initial position marker indicates the Node Score Cutoff value you have entered in the Finding Parameters. In moving the slider, the Node Score Cutoff is set to 0 at Min and 100 at Max, however there are several notable differences between the functions of the Size Threshold slider and the Node Score Cutoff Finding Parameter.

1. During exploration, the cluster is reevaluated without the requirements of satisfying the K-Core parameter. Thus, moving the slider leftwards of the initial position allows the cluster to be reduced to only the seed node.
2. During exploration in the Max direction, the cluster is "unaware" of other clusters. Unlike in the analysis where every subsequent attempt at finding a cluster is only allowed to expand around previously found clusters, the slider expands the cluster despite adjacent cluster borders. Thus, moving the slider rightwards of the initial position allows the cluster to be expanded to as much as the whole network.
   • However, the "awareness" of other clusters is intact in the range between the marker and Min to allow the cluster to return to its original content.

Haircut and Fluff are applied afterwards if they were turned on in the production of the given result set.

In response to the slider, the Cluster Browser will be updated with the new cluster's network graphic and details (number of nodes and edges and new cluster score). The node selection in the main network view will also be updated. Since clusters can expand to large and sometimes unreasonable sizes, the layouter may need extra time to complete its task. When this occurs, a loader and progress bar will appear in the Cluster Browser. There is no need to wait for the cluster to be drawn, the details and node selections will remain responsive to the slider's movements. If the new cluster exceeds 300 nodes, a place holder ("Too big to show") will be drawn instead since the graphic representation will take too long to compute and will be too populous to be of any real value.

Several peculiarities may arise during size exploration:

1. Cluster Size Explosion

   • When exploring a lower ranked cluster (further down the list) it is likely that the cluster's content is quite dependent on the content of higher ranked clusters. This is merely a probability and not a rule since the finding process starts at the highest scoring nodes while clusters are ranked afterwards based on their size and connectivity -- higher scoring seed nodes may not produce higher scoring clusters. Given that, when expanding a cluster, there may be an unexpected initial discontinuity in size since the Size Threshold Slider will ignore the presence of other clusters. If the cluster was produced around a low-scoring seed node then more nodes are likely to satisfy the Node Score Cutoff parameter. Such situations can indicate that the cluster in question may be part of a larger cluster ???????WHAT ELSE??????????.

2. Slider Dead-Zone

   • Sometimes, on the other hand, moving the Size Threshold Slider a long distance may produce no changes in cluster size. In such cases, the seed node's score is so high compared to its proximal neighbourhood that the Node Score Cutoff must be increased greatly to include much lower scoring members. This indicates that the cluster is more or less well separated from the surrounding network by a local peak in node scores and as such, it is likely a well defined cluster.

3. No Change

   • Lastly, if no changes occur during size exploration, the cluster in question must not be connected to the larger network and as such cannot expand.

Node Attribute Enumerator

The Enumerator provides a numerical summary of node attribute values possessed by the currently explored cluster's members. It is meant to inform the user of the cluster's contents and aid in determining the cluster's functional relevance. All node attributes that are available for the loaded network are listed in the select box. When an attribute selection is made in one exploration, it persists for all cluster explorations within the given result.

The table below the select box has two columns, Value and Occurrance. The Value column lists all node attribute values that are possessed by the cluster being explored. With a simple string type attribute, such as MCODE_Node_Status, this list well never exceed the number of cluster members since every member can have only one value and some values may be shared by several members. However, list type attributes such as GO terms may outnumber the cluster members since each member can have numerous values. The Occurance column simply displays the number of nodes possessing the particular attribute value listed in each row. The Enumerator table orders the list by the Occurrance number in descending order where the most commonly occurring attribute value is listed on top.

The Occurrance numbers are best interpreted when compared with the number of nodes in the cluster. For example, when enumerating Biological Process GO Terms, it may be a good indicator that the given cluster is biologically relevant if 9 of the 10 cluster members share some acute value.

In combination with the Size Threshold Slider, the Enumerator can be used to optimize clusters based on functional relevance. As the slider is being manipulated the Enumerator will automatically report changes in cluster content for the selected attribute. As such one can hone in on a size that, for example, reduces nodes with attribute values that are unrelated to some particular value of interest which is simultaneously maximized.

At this stage of MCODE development, the Node Attribute Enumerater is a precursor to more automated methods of accomplishing similar attribute-enhanced clustering and statistical reporting.

Outputting Results

Create Sub-Network

Export as Text

Tutorials (Coming Soon)

BiNGO Validation