コード例 #1
0
ファイル: interpret.py プロジェクト: ychuang6/bmi-776
def main(args):
    '''
    args - parsed arguments that include pos_sequences, neg_sequences,
    arch_file, and weights_file
    '''
    # encode fasta
    print('Loading sequence data...')
    pos_seq = encode_fasta_sequences(args.pos_sequences)
    print('{} positive test sequences'.format(len(pos_seq)))
    neg_seq = encode_fasta_sequences(args.neg_sequences)
    print('{} negative test sequences\n'.format(len(neg_seq)))

    # load model
    prefix = args.arch_file.replace('.arch.json', '')
    print('Loading {} model...'.format(prefix))
    model = SequenceDNN.load(args.arch_file, args.weights_file)

    # predict binding probability on test sequences
    print('Getting predictions...')
    pos_predictions = model.predict(pos_seq)
    for index, pred in enumerate(pos_predictions):
        print('positive_test_{}\tP(bound)={}'.format(index, pred[0]))
    print('')
    neg_predictions = model.predict(neg_seq)
    for index, pred in enumerate(neg_predictions):
        print('negative_test_{}\tP(bound)={}'.format(index, pred[0]))
    print('')

    # visualize trained model and motifs
    print('Plotting deeplift scores on positive sequences...')
    model.plot_deeplift(pos_seq, '{}_deeplift_positive'.format(prefix))

    print('Plotting true motifs...')
    motif_names = ['IRF_known1', 'NFKB_known1']
    for index, motif in enumerate(motif_names):
        fig = plot_motif(motif, figsize=(10, 4), ylab=motif)
        fig.savefig('motif{}.png'.format(index + 1), bbox_inches='tight')

    print('Plotting architecture...')
    model.plot_architecture('{}_architecture.png'.format(prefix))

    print('Plotting convolutional filters...')
    plot_sequence_filters(model, prefix)
コード例 #2
0
def plot_motifs(simulation_data):
    for motif_name in simulation_data.motif_names:
        plot_motif(motif_name, figsize=(10, 4), ylab=motif_name)
コード例 #3
0
ファイル: tutorial_utils.py プロジェクト: anushkanair/dragonn
def plot_motifs(simulation_data):
    for motif_name in simulation_data.motif_names:
        plot_motif(motif_name, figsize=(10, 4), ylab=motif_name)