Implementation of the ResponseNet algorithm. The algorithm can be found in this paper:
Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity by Yeger-Lotem et al., Nature Genetics 2009.
This method has appeared in various webservers (most recently as ResponseNet v3), but the underlying algorithm is not publicly available.
usage: responsenet.py [-h] --edges_file EDGES_FILE --sources_file SOURCES_FILE --targets_file TARGETS_FILE --output OUTPUT
[--gamma GAMMA] [-st] [-v] [-o]
options:
-h, --help show this help message and exit
--edges_file EDGES_FILE
Network file. File should be in SIF file format.
--sources_file SOURCES_FILE
File which denotes source nodes, with one node per line.
--targets_file TARGETS_FILE
File which denotes source nodes, with one node per line.
--output OUTPUT Prefix for all output files.
--gamma GAMMA The size of the output graph. Default = 10
-st, --include_st Determines whether output should include artificial Source and Target nodes
-v, --verbose Include verbose console output
-o, --output_log Create output log
We have provided a small example of a directed weighted graph, with a source A and a target E:
You can run the example with the command:
python responsenet.py --edges_file data/inputs/test-edges.txt --sources_file data/inputs/test-sources.txt --targets_file data/inputs/test-targets.txt --output out -v -o
This will output the final file of edges with non-zero flow as well as a log file (-o) with the LP. It will also output some information to the console (-v). The ouput edges file will look like
A B 1.0
B E 1.0
Which corresponds to these edges in the graph:
- Edge weights are positive are should range between 0 and 1. Similar to the paper, edge weights greater than 0.7 are truncated to 0.7.
- The gamma variable determines the size of the network. The default setting is 10, same as the paper.