Predicting regulatory variants with Convolutional Neural Network
Gaoxiang Li
Auburn University
Advisor:Li Chen, Xiao Qin
How to use:
(1)After download and unzip the file, run: python PRVCNN.py in this directory. command: python PRVCNN.py --h (for help to show all command line instruction)
1.training:
--train training model
--model_name the model name you want to save default:PRVCNN
Path for training input
--training_feature default='./data/training_feature_all.csv'
--training_label default='./data/training_label_all.csv'
2.testing:
--test testing process
--test_name the model name you want to test default:PRVCNN
Path for testing input:
--testing_feature default='./data/t1_imbanlance_feature.csv'
--testing_label default='./data/t1_imbanlance_label.csv'
--result_score_file default='score1.csv' Result score file name you want to save
3.evaluation
--evaluation evaluation model
--eval_name the model name you want to eval default:PRVCNN
--eval_dataset default=0 The compared dataset to be evaluated,1 for allelic imbalance'
'2 for eQTL, 3 for dsQTL, 4 for validated
Path for eval input
--eval_score_file default=default='./result/score1.csv' The score file to evaluate
--eval_true_label default=default='./data/t1_imbanlance_label.csv' The true label to evaluate
--eval_name default='Your method' The name of curve showed in figure
4.Parameter
--batch_size default=16
--max_epoch default=2000
--learning_rate default=0.0001
--learning_rate_decay default=0.99
--dropout_rate default=1
--L2_regularizer default=0.05
--kernel_size default=5
(2)Fpr other test: Run: python other_test.py
--SVM SVM method
--RF Random Forest method
--training_feature default='./data/training_feature_all.csv'
--training_label default='./data/ML_training_label.csv' (only one column)
--testing_feature default='./data/t1_imbanlance_feature.csv'
--testing_label default='./data/t1_imbanlance_label.csv'
--result_score_file default='score1_svm.csv' Result score file name to be saved
Prediction Score:
file score1.csv Allelic imbalance
file socre2.csv eQTL
file socre3.csv dsQTL
file score4.csv validated regulatory
Training code: PRVCNN_train
Testing code: PRVCNN_test1-4
notes:input must be reshaped and change the string feature to one-hot code