Changes

  model: TensorGraph

    Currently only SequenceDNN will work, but other models may be added.

  X: ndarray

      Shape (N_sequences, 1, N_letters, sequence_length) 

    Shape (N_sequences, N_letters, sequence_length, 1) 

  Returns

  -------

    (num_task, N_sequences, 1, N_letters, sequence_length) ISM score array.

  (num_task, N_sequences, N_letters, sequence_length, 1) ISM score array.

  """

  #Shape (N_sequences, N_letters, sequence_length, 1, num_tasks)

  mutagenesis_scores = np.empty(X.shape + (model.num_tasks,), dtype=np.float32)

  # Shape (N_sequences, num_tasks)

  wild_type_predictions = model.predict(NumpyDataset(X))

  # Shape (N_sequences, num_tasks, 1, 1, 1)

  wild_type_predictions = wild_type_predictions[:, np.newaxis, np.newaxis,

                                                np.newaxis]

  for sequence_index, (

      sequence,

      wild_type_prediction) in enumerate(zip(X, wild_type_predictions)):

    # Mutates every position of the sequence to every letter

    # Shape (N_letters * sequence_length, N_letters, sequence_length, 1)

    # Breakdown:

    #  Shape of sequence[np.newaxis] (1, N_letters, sequence_length, 1)

    mutated_sequences = np.repeat(

        sequence[np.newaxis], np.prod(sequence.shape), axis=0)

    # remove wild-type

    # len(arange) = N_letters * sequence_length

    arange = np.arange(len(mutated_sequences))

    horizontal_cycle = np.tile(

        np.arange(sequence.shape[-1]), sequence.shape[-2])

    mutated_sequences[arange, :, :, horizontal_cycle] = 0

    # len(horizontal cycle) = N_letters * sequence_length

    horizontal_cycle = np.tile(np.arange(sequence.shape[1]), sequence.shape[0])

    mutated_sequences[arange, :, horizontal_cycle, :] = 0

    # add mutant

    vertical_repeat = np.repeat(

        np.arange(sequence.shape[-2]), sequence.shape[-1])

    mutated_sequences[arange, :, vertical_repeat, horizontal_cycle] = 1

    vertical_repeat = np.repeat(np.arange(sequence.shape[0]), sequence.shape[1])

    mutated_sequences[arange, vertical_repeat, horizontal_cycle, :] = 1

    # make mutant predictions

    mutated_predictions = model.predict(NumpyDataset(mutated_sequences))

    mutated_predictions = mutated_predictions.reshape(sequence.shape +

  def test_in_silico_mutagenesis(self):

    """Test in-silico mutagenesis returns correct shape."""

    # Construct and train SequenceDNN model

    X = np.random.rand(10, 1, 4, 50)

    y = np.random.randint(0, 2, size=(10, 1))

    dataset = dc.data.NumpyDataset(X, y)

    sequences = np.array(["ACGTA", "GATAG", "CGCGC"])

    sequences = dc.utils.save.seq_one_hot_encode(sequences, letters=LETTERS)

    labels = np.array([1, 0, 0])

    labels = np.reshape(labels, (3, 1))

    self.assertEqual(sequences.shape, (3, 4, 5, 1))

    #X = np.random.rand(10, 1, 4, 50)

    #y = np.random.randint(0, 2, size=(10, 1))

    #dataset = dc.data.NumpyDataset(X, y)

    dataset = dc.data.NumpyDataset(sequences, labels)

    model = dc.models.SequenceDNN(

        50, "binary_crossentropy", num_filters=[1, 1], kernel_size=[15, 15])

        5, "binary_crossentropy", num_filters=[1, 1], kernel_size=[15, 15])

    model.fit(dataset, nb_epoch=1)

    # Call in-silico mutagenesis

    mutagenesis_scores = in_silico_mutagenesis(model, X)

    self.assertEqual(mutagenesis_scores.shape, (1, 10, 1, 4, 50))

    mutagenesis_scores = in_silico_mutagenesis(model, sequences)

    self.assertEqual(mutagenesis_scores.shape, (1, 3, 4, 5, 1))

summary = Deep-learning models for drug discovery, quantum chemistry, and the life sciences.

home-page = https://github.com/deepchem/deepchem

license = MIT

version = 2.1.0

version = 2.1.1

classifier =

    Development Status :: 4 - Beta

    Environment :: Console