PDGPCS is a driver genes prediction method; PDGPCS uesd gene expression data, gene mutation data and pathway data to predict driver genes. We obtained the source C code of PCST from M.Bailly-Bechet et al(M.Bailly-Bechet, C. Borgs, A. Braunstein, J. Chayes, A. Dagkessamanskaia, J.-M.François, and R. Zecchina. Finding undetected protein associations in cellsignaling by belief propagation. Proc Natl Acad Sci U S A 2011;108(2):882-887.). The matlab fronted ("MsgSteriner.mexa64") is compiled by Visual Studio Code.
Input:
- Gene expression data: tumor and normal expression data, the data format is same as example ('example_tumor.txt' and 'example_normal.txt' ,the patient's id in the two files must correspond).
- Gene mutation data: the data format is same as example ('example_snp.txt' or 'example_cnv.txt' )
- Pathway data: the data format is same as example ('KEGG_pathways_network.xlsx' ).
Run: run main_PDGPCS.m.
Output:
- cohort_driver_rank: driver genes ranking in the population.
- personalized_driver_rank: the ranking of each patient.