Main menu.
CoNet is a versatile tool to compute co-occurrences and mutual exclusions between items.
A basic run looks something like this:
- Open the Data menu and select the input matrix file.
- Open the Methods menu and select the methods to execute and their thresholds.
- Open the Merge menu and specify how the results of the different methods should be combined
- 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:
- Label The name of the node that is displayed on the node.
- abundance Each node corresponds to a row in the input matrix. The abundance is the row sum. When the input matrix was processed in any way, this refers to the row sum in the processed matrix.
- samplecount The samplecount is the number of values of the corresponding row that are neither zero nor missing values. When the input matrix was processed in any way, this refers to the number of occurrences in the processed matrix.
- degree The number of links of the node.
- posdegree The number of positive links of the node.
- negdegree The number of negative links of the node.
- unknowndegree The number of links of the node having unknown interaction type.
- impliesdegree The number of links of a node contributed by association mining.
- oriID The identifier of the row to which this node corresponds.
- canonicalName An attribute set by Cytoscape that stores the node identifier.
- isafeature This attribute is only set in case features are provided and allows to differentiate between feature nodes and other nodes.
- matrix_number This attribute is only set if two input matrices are provided and indicates the origin of the node from matrix 1 or 2.
- shiftgroup This attribute is only set if a lag is specified and groups all shifted versions of one row together.
Additional node attributes are added when metadata are provided, such as the lineage or the node group.
Edge attributes:
- Label The edge identifier.
- cooc_method The co-occurrence method that contributed this edge. There may be more than one co-occurrence method.
- weight The edge weight. When no randomization is run, this is the score of the co-occurrence method. In case of several methods per edge, the weight represents the combined score.
- interactionType The edge type. Can be either co-presence (positive interaction) or mutual exclusion (negative interaction). Co-presence edges are colored in green, mutual exclusion edges in red.
- method_number The number of methods supporting an edge. This attribute is not set for multi-graphs.
- method_scores The scores of methods supporting an edge. This attribute is not set for multi-graphs.
- methodname_scores The method name versus its score for all methods supporting an edge. This attribute is not set for multi-graphs.
- methodname_interactiontype The method name versus the interaction type (positive, negative or unknown) it predicted. This attribute is not set for multi-graphs.
- interaction An attribute set by Cytoscape that refers to the source of the edge. Here, it is set to the same value as cooc_method.
- canonicalName An attribute set by Cytoscape that stores the edge identifier.
When the network was randomized, the following edge attributes are added:
- pval The p-value computed from the random score distribution.
- qval The q-value computed from the p-value when benjaminihochberg or bonferroni are selected as multiple testing correction strategies.
- sig The significance computed from the p-value when compute_eval is selected as multiple testing correction strategy.
- methodname_pval The method name versus its p-value for all methods supporting an edge. This attribute is not set for multi-graphs.
- randdistribMean The mean of the random score distribution.
- randdistribMedian The median of the random score distribution.
- randdistribSD The standard deviation of the random score distribution.
- randdistribNumNonNaNScores The number of iterations that did not result in missing values.
- nulldistribmean When both permutation and bootstrap distribution were computed, the mean of the permutation distribution.
- nulldistribmedian When both permutation and bootstrap distribution were computed, the median of the permutation distribution.
- nulldistribsd When both permutation and bootstrap distribution were computed, the standard deviation of the permutation distribution.
- oriScore The value of the edge weight before randomization.
When the network is a randomized merged multigraph, the following edge attribute is added:
- pval-MERGE_STRATEGY The p-value computed by merging measure-specific p-values with the selected merging strategy (e.g. pval-brown-merge).
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.