## The data and Matlab code for paper "A Genome-wide Positioning Systems Network Algorithm for in silico Drug Repurposing".
### Data Description (Data_mat)
All data is saved as the .mat file because all the calculation is performed by Matlab.
### Cancer_Specific_PPI/ folder:
We used the p-value<0.05 co-expressed PPI interactions to build the cancer type-specific PPI.
### Mutation/ folder:
- There are two columns for the data Mutation. The first column represents the Gene ID, and the second column represents the times of the number of the tumors with the corresponding mutated gene (mutation frequency).
- Driver_Gene/ folder, Associated_Gene/ folder, Survival_Gene/ folder: The gene list of the driver gene, associated gene and survival gene for each cancer type.
### Net_PPI:
Net_PPI is the human interactome (total ppi), and there are four columns. The first and second column are the gene id, the third column represents the co-expression correlation, and the fourth column represent the P-value of the correlation.
### Gene_Distance:
There are two parameters Distance and Genes. Distance is a 15137x15137 matrix, and the element represents the distance between the corresponding genes in Net_PPI, and Genes is the corresponding gene ID.
### Gene_Length:
Gene_Length has two column. The first column is the gene ID, and the second is the corresponding gene length.
### Drug_Gene_10uM:
It is the drug gene interaction. The first column of Gene_Drug is the gene ID, and the second column of Gene_Drug is the drug ID, and the corresponding drug name in the Drug_List.
### Map_List:
Map_List illustrates the drug and its ATC contents.
### Gene_Drug:
It is the drug gene interaction. There are two parameters in this file (Gene_Drug and Drug_List). The first column of Gene_Drug is the gene ID, the second column of Gene_Drug is the Drug_ID, and the third colum of Gene_Drug is amplitude value. The corresponding drug name is in Drug_List, and there are 1309 drug in this dataset.
### Code (for Matlab):
- "Raw_Module_Generation.m" to generate the raw module.
- "Cancer_Module_Calculation.m" to obtain the final cancer module.
- "Module_Validation.m" to check the enrichment of the cancer module gene on several
- "Closet_Distance_ZScore.m" to calculate the network proximity between the drug targets and the cancer module gene.
- "Drug_Gene_Set_Enrichment.m" to calculate the overlap enrichment between the drug targets and the cancer module gene.
### Tutorial:
The code has been tested on the following systems: Linux Ubuntu 16.04 and Windows 7, with Matlab R2016b installed.
Put all data in the "Data_mat/" folder first and create a "Raw_Module/" folder under "Data_mat/" folder.
Run "Raw_Module_Generation.m" , and save raw module data in the "Data_mat/Raw_Module/". There are two parameters (Module and Score) in this intermediate result , where Module represents the gene set in each raw module and Score records the seed gene, final score and module size for the corresponding raw module. For we obtain about 60,000 raw module for each cancer type in our work, and this processes is very time-consuming. Than run ‘Module_Validation.m’ to check the cancer module from GPSnet.
Run "Drug_Gene_Set_Enrichment.m" and "Closet_Distance_ZScore.m" to get the new indications for approved drugs using both gene set enrichment analysis and network proximity approaches respectively.
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GPSnet-master.zip (95个子文件)
GPSnet-master
Data_mat
Gene_Drug.mat 6.78MB
Cancer_Specific_PPI
GBM.mat 197KB
COAD.mat 206KB
LUAD.mat 219KB
LAML.mat 217KB
OV.mat 164KB
LUSC.mat 205KB
UCEC.mat 173KB
BLCA.mat 199KB
STAD.mat 233KB
PRAD.mat 270KB
KIRC.mat 261KB
SKCM.mat 253KB
HNSC.mat 241KB
BRCA.mat 266KB
THCA.mat 275KB
KICH.mat 145KB
Mutation
GBM.mat 16KB
COAD.mat 38KB
LUAD.mat 35KB
LAML.mat 2KB
OV.mat 17KB
LUSC.mat 27KB
UCEC.mat 37KB
BLCA.mat 22KB
STAD.mat 31KB
PRAD.mat 6KB
KIRC.mat 21KB
SKCM.mat 33KB
HNSC.mat 25KB
BRCA.mat 22KB
THCA.mat 6KB
KICH.mat 2KB
Driver_Gene
GBM.mat 567B
COAD.mat 488B
LUAD.mat 819B
LAML.mat 303B
OV.mat 429B
LUSC.mat 655B
UCEC.mat 684B
BLCA.mat 592B
STAD.mat 609B
PRAD.mat 440B
KIRC.mat 505B
SKCM.mat 728B
HNSC.mat 589B
BRCA.mat 993B
THCA.mat 299B
Survival_Gene
GBM.mat 2KB
COAD.mat 2KB
LUAD.mat 5KB
LAML.mat 3KB
OV.mat 4KB
LUSC.mat 2KB
UCEC.mat 184B
BLCA.mat 3KB
STAD.mat 1KB
PRAD.mat 2KB
KIRC.mat 981B
SKCM.mat 2KB
HNSC.mat 2KB
BRCA.mat 6KB
THCA.mat 184B
Net_PPI.mat 2.9MB
Gene_Length.mat 64KB
Map_List.mat 3KB
Associated_Gene
GBM.mat 631B
COAD.mat 999B
LUAD.mat 1KB
LAML.mat 678B
OV.mat 781B
LUSC.mat 1KB
UCEC.mat 554B
BLCA.mat 815B
STAD.mat 809B
PRAD.mat 1KB
KIRC.mat 446B
SKCM.mat 513B
HNSC.mat 185B
BRCA.mat 1KB
THCA.mat 476B
Gene_Drug_10uM.mat 7KB
Gene_Distance.mat 46.27MB
Readme.md 3KB
code
Cancer_Module_Calculation.m 837B
network_smoothing.m 894B
Closet_Distance_Final.m 2KB
Closet_Distance_ZScore.m 3KB
Module_Selected.m 961B
Module_Forming_Process.m 2KB
Module_Treat.m 532B
Raw_Module_Generation.m 5KB
largest_component.m 519B
Drug_Gene_Set_Enrichment.m 905B
Module_Validation.m 1KB
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