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python train_path train_label -t test_path -o output_path

The following parameters are required to run pedia:

  • train_path is the folder which contains training data. It could be 1KG, ExAC, IRAN. For example, the path is PEDIA/3_simulation/jsons/real/train/1KG.

  • train_label is for annotating which training set we used (1KG, ExAC or IRAN).

  • -o output_path is the output folder. The default is current folder.

For the following parameters, you should assign exact one parameter to specify which mode you want to run.

  • -t test_path. The path could be either the folder which contains testing data or the JSON file.

  • -c fold number. This parameter enables k-fold cross validation (default 10).

  • -l Flag enables leave one group out cross validation. The grouping is based on the mutation gene.

The optional parameters are:

  • -e feature. Exclude specific features. 0: Feature match, 1: CADD, 2: Gestalt, 3: BOQA, 4: PHENO. If features are more than one, use _ to separate them.

  • -g Flag enables drawing manhattan plots and ranking plot

  • -s Flag enables server mode. This flag is for running on server.

  • -f feature. Filter cases which don't have specific features in pathogenic mutation gene. 0: Feature match, 1: CADD, 2: Gestalt, 3: BOQA, 4: PHENO. We only support using one feature.

If you want to run on server, the testing json file is from stdin, and the output pedia score is to stdout. Hence, you don't need to use -t in server mode. The following command is the example for running on server.

cat PEDIA/3_simulation/json_simulation/1KG/CV/131037.json | python PEDIA/3_simulation/json_simulation/real/train/1KG/ 1KG -s > pedia_score.csv

The pedia score for this json file is in pedia_score.csv


Execute the following command. The output will be in output/ directory. If you want to prioritize one case, then typing the file name in testing argument.

python /projects/PEDIA/3_simulation/jsons/real/train/1KG/ 1KG -t your_filename.json -o output/1KG

If you want to test several cases in the same folder, the command for testing is

python /projectsPEDIA/3_simulation/jsons/real/train/1KG/ 1KG -t /projects/PEDIA/3_simulation/jsons/real/test -o output/1KG

The command for 10-fold cross validation is

python /projects/PEDIA/3_simulation/jsons/1KG/CV/ 1KG -o output/cv/1KG -c 10

The command for loocv is

python /projects/PEDIA/3_simulation/jsons/1KG/CV/ 1KG -o output/loocv/1KG -l

The command for 10-fold cv and excluding gestalt and feature match score is

python /projects/PEDIA/3_simulation/jsons/1KG/CV/ 1KG -o output/exclude/1KG -c 10 -e 0_2


You can use snakemake to run different kind of experiments automatically.


  1. Install snakemake.

  2. Create an environment with the required software

conda env create --name classifier --file environment.yaml
  1. Activate the environment
source activate classifier


  • To obtain all the output files for a case which we want to upload to DPDL. We will get all the output files in folder output/test/1KG/case_id/.
snakemake -p output/test/1KG/case_id/case_id.out

case_id_pedia.json is the file which append PEDIA scores to genelist. case_id.vcf.gz is the file which includes the variants in the top 10 genes.

  • Run 10-fold cross validation on 1KG, ExAC and Iran data sets respectively.
snakemake -p CV_all --cores 3
  • Run loocv on 1KG, ExAC and Iran data sets respectively.
snakemake -p LOOCV_all --cores 3
  • Using all feature combination to run 10-fold cross validation on 1KG, ExAC and Iran data sets respectively.
snakemake -p CV_exclude_all --cores 3