DISRUPT is a software to predict disrupted interactions in gene regulatory or protein-protein interaction networks. Required input data is a list of interactions, describing a normal network, and expression data for the normal network and the network with disruptions (e.g. matched normal and cancer expression data). Based on those inputs DISRUPT ranks the interactions of the normal network according to their likelihood of being disrupted in the disease-related network.
DISRUPT itself can be downloaded from here. Unpack the zip-file to a folder of your choice and run the command example.bat to ensure that everything is working properly. Compare the program output to the example output provided here.
DISRUPT is invoked from the command line using the following parameters:
disrupt.bat network_normal expression_normal expression_disrupted ex_type predictionsnetwork_normal is a file that contains a list of interactions within the normal network. Interactions are given as tab-separated pairs of gene names. See the following example:
YJL206C YNL004W YJL206C YHL008C YJL206C YDR166C YJL206C YDR167W YJL206C YGL262W YJL206C YBR235W YJL206C YNR071C YJL206C YMR259C YJL206C YNR072WNote that DISRUPT also allows to provide "non-interactions" within the network_normal file. They are not marked in any special way and it is the responsibility of the user to keep track which gene pairs define interactions and which of them are non-interactions. This allows to infer losses and gains of interactions, since the predictions file (see below) will contain all gene pairs listed in network_normal.
GENE sample_0 sample_1 sample_2 sample_3 sample_4 sample_5 sample_6 sample_7 sample_8 sample_9 YNL004W 0.0000 0.7181569 0.0829788 0.0939352 0.0921688 0.1362978 0.0848851 0.1432751 0.0812507 0.0671806 YJL206C 0.6693 0.0000000 0.7539569 0.6497819 0.7073736 0.7908985 0.7002137 0.6431129 0.7935542 0.6487457 YHL008C 0.0033 0.6617605 0.0000000 0.0040327 0.0186448 0.0195510 0.0180854 0.0074746 0.0023201 0.0053902 YDR166C 0.2626 0.6669589 0.4270926 0.0000000 0.3185876 0.2416608 0.3402738 0.2608482 0.2611949 0.2991349 YDR167W 0.5505 0.0035929 0.4914464 0.5676906 0.0000000 0.4568083 0.4026544 0.5978579 0.5653026 0.5356704 YGL262W 0.7360 0.0638349 0.7396615 0.7035809 0.7824290 0.0000000 0.7011108 0.7824157 0.5624576 0.7891701 YBR235W 0.7189 0.0427199 0.6966874 0.5924989 0.8905074 0.7029194 0.0000000 0.8780427 0.6040577 0.6762094 YNR071C 0.0713 0.6311608 0.0653176 0.1145613 0.0952396 0.0871489 0.1102488 0.0000000 0.0793149 0.1488523 YMR259C 0.7328 0.0090925 0.7027191 0.5981570 0.7818790 0.6853868 0.7193278 0.6575753 0.0000000 0.7506075 YNR072W 0.3369 0.0366842 0.3642304 0.2860091 0.3850221 0.3693415 0.3950807 0.3554340 0.4025031 0.0000000expression_disrupted is a file that contains expression data for the network in the disrupted state. expression_normal and expression_disrupted need to be of the same experimental type (e.g. knock-out or multi-factorial) and of course both files need to contain expression data for all genes in network_normal. However, the number of samples in expression_normal and expression_disrupted can be different.
YJL206C YNL004W 0.368 YJL206C YGL262W 0.084 YJL206C YDR166C 0.046 YJL206C YMR259C 0.043 YJL206C YNR071C 0.023 YJL206C YDR167W 0.013 YJL206C YHL008C 0.008 YJL206C YBR235W 0.007 YJL206C YNR072W 0.002
The predictions file contains all gene pairs provided in network_normal but now ranked according to their score of being different (e.g. disrupted) to the normal network. The number after each pair of interacting genes is that difference score. For expression data normalized to [0,1] the score will range between 0 and 1 as well. The higher the score the higher the likelihood that the corresponding interaction is lost (or gained for non-interactions).
Note that the reported score is not a probability and a score of 0.368 can not be interpreted as a 36% chance that an interaction is disrupted. In the given example, the highest ranking interaction with the score of 0.368 actually has been disrupted and all following, lower-ranking interactions, with much lower scores, have not been disrupted. Generally, the differences between scores is more indicative of disruptions than the score itself. Here all interactions, apart from the first one, show very low score.
DISRUPT also reports the node-to-edge ratio of the normal network and generally predictions for networks with node-to-edge ratios close to 1.0 are trustworthy while predictions for node-to-edge ratios close to zero are not.
Here an example output of DISRUPT when running example.bat. The computation should finish within seconds and the data folder should then contain a new predictions file: predictions.tsv
DISRUPT Detection of disruptions in gene regulatory networks. Version 1.0 parameters: data/network_normal.tsv data/expression_normal.tsv data/expression_disrupted.tsv knockout data/predictions.tsv running ... Node/Edge = 1.111 YJL206C YNL004W 0.368 YJL206C YGL262W 0.084 YJL206C YDR166C 0.046 YJL206C YMR259C 0.043 YJL206C YNR071C 0.023 YJL206C YDR167W 0.013 YJL206C YHL008C 0.008 YJL206C YBR235W 0.007 YJL206C YNR072W 0.002 finished.
To run your own experiment call disrupt.bat or python disrupt.py with the parameters described before.
None so far.
|1.01||13.11.12||Minor changes to doc string comments|
|1.00||12.11.12||First public version|