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DiSCaGe (Discovering Significant Cancer Genes)

All sources to reproduce paper results are available.

The following libraries and versions were used:

  • Python 3.7.7
  • pandas 1.0.5
  • numpy 1.18.5
  • networkx 2.4
  • matplotlib 3.2.2

Usage

DiSCaGe has 3 options for use:

Type 1:

  • Mutation data on MAF file format
  • Gene interaction network in edge lists

Type 2:

  • Binary mutation matrix
  • Weighted mutation matrix
  • Gene interaction network in edge lists

Type 3:

  • Mutation score for each gene
  • Gene interaction network in edge lists

A input file must be filled with data and the weights for each Variant_Classification. A running example is set on the file EXAMPLE_SNV_InDel_input_parameters.in, which is run using the follow command

python DiSCaGe.py EXAMPLE_SNV_InDel_input_parameters.in

A set of output files is generated. The final list of prioritized genes are found in file OUTPUT.score.mutatedGenes

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