Main menu.

CoNet is a versatile tool to compute co-occurrences and mutual exclusions between items. A basic run looks something like this:
  1. Open the Data menu and select the input matrix file.
  2. Open the Methods menu and select the methods to execute and their thresholds.
  3. Open the Merge menu and specify how the results of the different methods should be combined
  4. Click the GO button
When the GO button is clicked, a progress menu appears allowing to cancel the network construction job. It disappears upon completion of the job. During computation, no further jobs can be launched.
In more advanced runs, randomizations can be carried out (Randomization menu).

Demo

The "Demo" button computes co-occurrences on an example data set (the oral samples collected in Costello et al., Science, 2009). Running the demo does not change the current configuration of CoNet, neither is the demo influenced by CoNet's current configuration. The demo data and settings accompany the CoNet distribution.

Command line call

CoNet is accompanied by a command line tool, which is recommended if thousands of randomizations have to be carried out on large matrices. The button "Generate command line call" displays a window that shows the command line call corresponding to the current setting of CoNet. Note that Rserve-related configuration cannot be set via command line directly, but need to be specified in the (optional) configuration file of the CoNet command line tool.

Settings loading/saving

CoNet settings can also be saved to or loaded from a file via the button "Settings loading/saving". This saves the user from repeating a tedious CoNet configuration.

GDL network loading

The "Load GDL network" box allows displaying networks in GDL format generated with the command line tool. Select the GDL network file by clicking the "Load" button and then push GO. To undo a GDL file selection, click "Load" again and then "Cancel". This will clear the GDL file selection.

Network display

Result networks are displayed with a number of attributes.
Network attribute:
The Comment stores details of the network generation as well as the command line call.

Node attributes:
Additional node attributes are added when metadata are provided, such as the lineage or the node group.

Edge attributes:
When the network was randomized, the following edge attributes are added:
When the network is a randomized merged multigraph, the following edge attribute is added:
In case of a merged multigraph, multiple-testing-corrected q-values are computed from the merged p-values. The q-values are also stored under the "weight" attribute.

So when you do not randomize, your edge attribute of choice to weight edges is "weight", when you randomize, it is "pval", when you merge p-values, it is pval-MERGE_STRATEGY and when you correct for multiple testing, it is either "qval" (for bonferroni and benjaminihochberg) or "sig" (for E-value computation).

Limitation

CoNet expects small-sized (hundreds of rows) to medium-sized (thousands of rows) matrices as input. If filtering steps are selected that reduce the matrix size, larger matrices can be provided as well. If you want to infer networks from matrices with more than five thousand rows, CoNet is not a suitable tool for you. You might however combine it with other tools by first clustering a huge input matrix with an external tool and then submitting aggregated clusters to CoNet. For matrices with a size close to CoNet's limit, it is best to avoid selecting slow inference methods such as mutual information or Kendall's correlation and to carry out the inference on command line.

Error report

No tool is without bugs. CoNet errors usually cause an error report to be generated, which you can send if you think it's relevant. You can also check the cytoscape-generated log file "output.log" in the Cytoscape application folder.