Train TAD predictor

This program can be used to train new classifiers for hicTADClassifier. These classifiers can later be run to call boundaries for TADs. By default, an EasyEnsembleClassifier as described in Liu et al.: “Exploratory Undersampling for Class-Imbalance Learning” will be trained, but you can pass any sklearn classifier that allows for a warm start. You may also vary the resampling method and a range of hyperparameters to fine tune the model. Do mind to set the correct normalization method and resolution for the classifier. The program will check and raise warnings, when resolutions and normalization methods are mixed up. Also, a protein track file in the narrowPeak format with a threshold value may be passed to filter out low quality boundaries.

The resulting classifier will be pickled at the specified out_file. A quick example can be seen here, where we varied the feature distance:

$ hicTrainTADClassifier -m 'train_new' -f 'my_test_matrix.cool' -d 'domains.bed' -o 'new_classifier.data' -n 'range' -r 10000 --distance 18