Unverified Commit 767ed31a authored by Bharath Ramsundar's avatar Bharath Ramsundar Committed by GitHub
Browse files

Merge pull request #1455 from rbharath/dnasim_soft 

Make simdna a soft dependency
parents f6e80071 69d71f1b

deepchem/metrics/genomic_metrics.py

+4 −1
Original line number Diff line number Diff line
@@ -2,7 +2,6 @@ 


import numpy as np

from deepchem.data import NumpyDataset

from deepchem.utils.genomics import loaded_motifs

from scipy.signal import correlate2d
@@ -29,6 +28,10 @@ def get_motif_scores(encoded_sequences, 
  If max_scores and return_positions, (N_sequences, 2*num_motifs*max_scores)

  array with max scores and their positions.

  """

  import simdna

  from simdna import synthetic

  loaded_motifs = synthetic.LoadedEncodeMotifs(

      simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001)

  num_samples, _, seq_length, _ = encoded_sequences.shape

  scores = np.ones((num_samples, len(motif_names), seq_length))

  for j, motif_name in enumerate(motif_names):

deepchem/molnet/dnasim.py

+85 −77
Original line number Diff line number Diff line 
from __future__ import absolute_import, division, print_function

from collections import OrderedDict

import numpy as np

import simdna

from simdna.synthetic import (

    RepeatedEmbedder,

    SubstringEmbedder,

    ReverseComplementWrapper,

    UniformPositionGenerator,

    InsideCentralBp,

    LoadedEncodeMotifs,

    PwmSamplerFromLoadedMotifs,

    UniformIntegerGenerator,

    ZeroOrderBackgroundGenerator,

    EmbedInABackground,

    GenerateSequenceNTimes,

    RandomSubsetOfEmbedders,

    IsInTraceLabelGenerator,

    EmbeddableEmbedder,

    PairEmbeddableGenerator,

)

from simdna.util import DiscreteDistribution



loaded_motifs = LoadedEncodeMotifs(

    simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001)





def get_distribution(GC_fraction):

  from simdna.util import DiscreteDistribution

  return DiscreteDistribution({

      'A': (1 - GC_fraction) / 2,

      'C': GC_fraction / 2,
@@ -56,19 +35,26 @@ def simple_motif_embedding(motif_name, seq_length, num_seqs, GC_fraction): 
  embedding_arr: 1darray

      Array of embedding objects.

  """

  import simdna

  from simdna import synthetic

  if motif_name is None:

    embedders = []

  else:

    substring_generator = PwmSamplerFromLoadedMotifs(loaded_motifs, motif_name)

    loaded_motifs = synthetic.LoadedEncodeMotifs(

        simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001)

    substring_generator = synthetic.PwmSamplerFromLoadedMotifs(

        loaded_motifs, motif_name)

    embedders = [

        SubstringEmbedder(ReverseComplementWrapper(substring_generator))

        synthetic.SubstringEmbedder(

            synthetic.ReverseComplementWrapper(substring_generator))

    ]

  embed_in_background = EmbedInABackground(

      ZeroOrderBackgroundGenerator(

  embed_in_background = synthetic.EmbedInABackground(

      synthetic.ZeroOrderBackgroundGenerator(

          seq_length, discreteDistribution=get_distribution(GC_fraction)),

      embedders)

  generated_sequences = tuple(

      GenerateSequenceNTimes(embed_in_background, num_seqs).generateSequences())

      synthetic.GenerateSequenceNTimes(embed_in_background,

                                       num_seqs).generateSequences())

  sequence_arr = np.array(

      [generated_seq.seq for generated_seq in generated_sequences])

  embedding_arr = [
@@ -87,24 +73,30 @@ def motif_density(motif_name, 
  """

  Returns sequences with motif density, along with embeddings array.

  """

  substring_generator = PwmSamplerFromLoadedMotifs(loaded_motifs, motif_name)

  import simdna

  from simdna import synthetic

  loaded_motifs = synthetic.LoadedEncodeMotifs(

      simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001)

  substring_generator = synthetic.PwmSamplerFromLoadedMotifs(

      loaded_motifs, motif_name)

  if central_bp is not None:

    position_generator = InsideCentralBp(central_bp)

    position_generator = synthetic.InsideCentralBp(central_bp)

  else:

    position_generator = UniformPositionGenerator()

  quantity_generator = UniformIntegerGenerator(min_counts, max_counts)

    position_generator = synthetic.UniformPositionGenerator()

  quantity_generator = synthetic.UniformIntegerGenerator(min_counts, max_counts)

  embedders = [

      RepeatedEmbedder(

          SubstringEmbedder(

              ReverseComplementWrapper(substring_generator),

      synthetic.RepeatedEmbedder(

          synthetic.SubstringEmbedder(

              synthetic.ReverseComplementWrapper(substring_generator),

              position_generator), quantity_generator)

  ]

  embed_in_background = EmbedInABackground(

      ZeroOrderBackgroundGenerator(

  embed_in_background = synthetic.EmbedInABackground(

      synthetic.ZeroOrderBackgroundGenerator(

          seq_length, discreteDistribution=get_distribution(GC_fraction)),

      embedders)

  generated_sequences = tuple(

      GenerateSequenceNTimes(embed_in_background, num_seqs).generateSequences())

      synthetic.GenerateSequenceNTimes(embed_in_background,

                                       num_seqs).generateSequences())

  sequence_arr = np.array(

      [generated_seq.seq for generated_seq in generated_sequences])

  embedding_arr = [
@@ -270,23 +262,34 @@ def simulate_multi_motif_embedding(motif_names, seq_length, min_num_motifs, 
      Array of embedding objects.

  """



  import simdna

  from simdna import synthetic

  loaded_motifs = synthetic.LoadedEncodeMotifs(

      simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001)



  def get_embedder(motif_name):

    substring_generator = PwmSamplerFromLoadedMotifs(loaded_motifs, motif_name)

    return SubstringEmbedder(

        ReverseComplementWrapper(substring_generator), name=motif_name)

    substring_generator = synthetic.PwmSamplerFromLoadedMotifs(

        loaded_motifs, motif_name)

    return synthetic.SubstringEmbedder(

        synthetic.ReverseComplementWrapper(substring_generator),

        name=motif_name)



  embedders = [get_embedder(motif_name) for motif_name in motif_names]

  quantity_generator = UniformIntegerGenerator(min_num_motifs, max_num_motifs)

  combined_embedder = [RandomSubsetOfEmbedders(quantity_generator, embedders)]

  embed_in_background = EmbedInABackground(

      ZeroOrderBackgroundGenerator(

  quantity_generator = synthetic.UniformIntegerGenerator(

      min_num_motifs, max_num_motifs)

  combined_embedder = [

      synthetic.RandomSubsetOfEmbedders(quantity_generator, embedders)

  ]

  embed_in_background = synthetic.EmbedInABackground(

      synthetic.ZeroOrderBackgroundGenerator(

          seq_length, discreteDistribution=get_distribution(GC_fraction)),

      combined_embedder)

  generated_sequences = tuple(

      GenerateSequenceNTimes(embed_in_background, num_seqs).generateSequences())

      synthetic.GenerateSequenceNTimes(embed_in_background,

                                       num_seqs).generateSequences())

  sequence_arr = np.array(

      [generated_seq.seq for generated_seq in generated_sequences])

  label_generator = IsInTraceLabelGenerator(np.asarray(motif_names))

  label_generator = synthetic.IsInTraceLabelGenerator(np.asarray(motif_names))

  y = np.array(

      [

          label_generator.generateLabels(generated_seq)
@@ -369,21 +372,26 @@ def simulate_heterodimer_grammar(motif1, motif2, seq_length, min_spacing, 
    embedding_arr: list

        List of embedding objects.

    """



  motif1_generator = ReverseComplementWrapper(

      PwmSamplerFromLoadedMotifs(loaded_motifs, motif1))

  motif2_generator = ReverseComplementWrapper(

      PwmSamplerFromLoadedMotifs(loaded_motifs, motif2))

  separation_generator = UniformIntegerGenerator(min_spacing, max_spacing)

  embedder = EmbeddableEmbedder(

      PairEmbeddableGenerator(motif1_generator, motif2_generator,

  import simdna

  from simdna import synthetic

  loaded_motifs = synthetic.LoadedEncodeMotifs(

      simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001)

  motif1_generator = synthetic.ReverseComplementWrapper(

      synthetic.PwmSamplerFromLoadedMotifs(loaded_motifs, motif1))

  motif2_generator = synthetic.ReverseComplementWrapper(

      synthetic.PwmSamplerFromLoadedMotifs(loaded_motifs, motif2))

  separation_generator = synthetic.UniformIntegerGenerator(

      min_spacing, max_spacing)

  embedder = synthetic.EmbeddableEmbedder(

      synthetic.PairEmbeddableGenerator(motif1_generator, motif2_generator,

                                        separation_generator))

  embed_in_background = EmbedInABackground(

      ZeroOrderBackgroundGenerator(

  embed_in_background = synthetic.EmbedInABackground(

      synthetic.ZeroOrderBackgroundGenerator(

          seq_length, discreteDistribution=get_distribution(GC_fraction)),

      [embedder])

  generated_sequences = tuple(

      GenerateSequenceNTimes(embed_in_background, num_pos).generateSequences())

      synthetic.GenerateSequenceNTimes(embed_in_background,

                                       num_pos).generateSequences())

  grammar_sequence_arr = np.array(

      [generated_seq.seq for generated_seq in generated_sequences])

  positive_embedding_arr = [

deepchem/utils/genomics.py

+0 −5
Original line number Diff line number Diff line 
"""

Genomic data handling utilities.

"""

import simdna

from simdna.synthetic import LoadedEncodeMotifs

import numpy as np



loaded_motifs = LoadedEncodeMotifs(

    simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001)





def seq_one_hot_encode(sequences, letters='ATCGN'):

  """One hot encodes list of genomic sequences.

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