Model Training

An example script to train a model using asap has been defined in tutorials/train.py.

Create training and validation datasets

asap.training_datasets(signal_file, genome, train_chroms, val_chroms, generated, blacklist_file=None, unmap_file=None)

Creates training and validation datasets using genomic sequence as input and ATAC-seq signal as output.

Args:

• signal_file (str): Path to the ATAC-seq signal file.
• genome (str): Path to the genome file.
• train_chroms (List[int]): List of chromosomes for training.
• val_chroms (List[int]): List of chromosomes for validation.
• generated (str): Path to save the processed data.
• blacklist_file (List[str]): List of paths to blacklist files (including SNVs).
• unmap_file (str): Path to the unmapped regions file.

Returns:

• train_dataset (asap.dataloader.WGDataset): Training dataset
• val_dataset (asap.dataloader.WGDataset): Validation dataset

Train a model

asap.train_model(experiment_name, model, train_dataset, val_dataset, logs_dir, n_gpus=0, max_epochs=70, learning_rate=1e-3, batch_size=64, use_map=False)

Trains the selected model using training and validation datasets.

Args:

• experiment_name (str): The name of the experiment. This will be used to save model checkpoints.
• model (str): The name of the model to train. Choose from [cnn, lstm, dcnn, convnext_cnn, convnext_lstm, convnext_dcnn, convnext_transformer].
• train_dataset (asap.dataloader.WGDataset): The training dataset.
• val_dataset (asap.dataloader.WGDataset): The validation dataset.
• logs_dir (str): The directory where the model is saved.
• n_gpus (int): The number of GPUs to use for training. Set 0 for CPU.
• max_epochs (int): The maximum number of epochs to train the model.
• learning_rate (float): The learning rate for the optimizer.
• batch_size (int): The batch size for training.
• use_map (bool): Whether to additionally use mappability information for training.

Returns:

• None

Note: The Pearson's R for the trained model on the validation dataset is expected to be ~0.7.