Supplemental Results
Yeang CH*, Mak HC*, McCuine S, Workman C, Jaakola T, Ideker TI
Genome Biology (2005) 6:R62   [fulltext] [PDF]

 Computer Science and Artificial Intelligence Laboratory /
    Laboratory for Integrative Network Biology
MIT / UC San Diego
 ·  Home
Methods
 ·  Experimental Protocols
 ·  Internal Validation of Models
 ·  Knockout Data Reproducibility
·  Regulated Gene Selection
 ·  Building Physical Network Models
·  Protein-DNA Data
·  Protein-Protein Data
·  Knockout Data
 ·  Model Inference
 ·  Software Download
 ·  Evaluating for New Experiments
Data
 ·  Inferred Network Models
 ·  Download Network Model Data
 ·  References

Model Inference Software

We are making the model inference algorithm available as a plugin to Cytoscape, a network analysis and visualization software package. We provide a Java JAR file of compiled software as well as the interaction network and expression data used in this study.

Download & Installation
  1. This plugin requires Cytoscape version 2.1 or later. You can download the latest version of Cytoscape here
  2. Download the plugin (click here) and save it in the "plugins" directory in your Cytoscape installation.
  3. Download and save the three data files. Make sure they are in the same directory.


Generate models used in this study

To generate the models used in this study:
  1. Start Cytoscape from the directory where you saved the data files
  2. Select "Explain knockout data->Run algorithm" from the Plugins menu
  3. By default all of the algorithm parameters are preset to the values used in this study.
  4. Click "Run!"
  5. It will take 10-15 minutes for the algorithm to finish. Network models should appear in Cytoscape once the analysis is done. Model 0 in the output corresponds to Figure 1a. Model 4 corresponds to Figure 1b.

    Note: You may need to edit your Cytoscape "Visual Properties" to get the models to look more like Figure 1 and the supplemental figures. Exact replication of the published results, however, will not be possible because the results have been post-processed for publication.


Application to other data sets


Using your own expression data

Expression profiling experiments must measure the change in expression in a single-gene knockout versus wild-type. For each expression change the following data are required:
[KO ORF] The ORF name of the gene that is knocked out.
[Changing ORF] The ORF name of the gene that is differentially expressed
[p-value] The significance of the differential expression
[log ratio] The base 10 log ratio of the expression change (KO vs wild-type)
Each line in the input expression file must contain data for one differentially expressed gene in one knockout experiment. Each line must be in the following format:
[KO ORF] ko [Changing ORF] = [p-value] [log ratio]
See the expression file used in this study for an example.