def do(options):
if (options.seed is not None):
import numpy as np
np.random.seed(options.seed)
from simdna import random
random.seed(options.seed)
outputFileName_core = util.addArguments("DensityEmbedding",
[util.ArgumentToAdd(options.prefix, "prefix"),
util.BooleanArgument(options.bestHit, "bestHit"),
util.ArrArgument(options.motifNames, "motifs"),
util.ArgumentToAdd(options.min_motifs, "min"),
util.ArgumentToAdd(options.max_motifs, "max"),
util.ArgumentToAdd(options.mean_motifs, "mean"),
util.FloatArgument(options.zero_prob, "zeroProb"),
util.ArgumentToAdd(options.seqLength, "seqLength"),
util.ArgumentToAdd(options.numSeqs, "numSeqs")])
loadedMotifs = synthetic.LoadedEncodeMotifs(options.pathToMotifs, pseudocountProb=0.001)
Constructor = synthetic.BestHitPwmFromLoadedMotifs if options.bestHit else synthetic.PwmSamplerFromLoadedMotifs
embedInBackground = synthetic.EmbedInABackground(
backgroundGenerator=synthetic.ZeroOrderBackgroundGenerator(seqLength=options.seqLength)
, embedders=[
synthetic.RepeatedEmbedder(
synthetic.SubstringEmbedder(
synthetic.ReverseComplementWrapper(
substringGenerator=Constructor(
loadedMotifs=loadedMotifs,motifName=motifName),
reverseComplementProb=options.rc_prob
),
positionGenerator=synthetic.UniformPositionGenerator()),
quantityGenerator=synthetic.ZeroInflater(synthetic.MinMaxWrapper(
synthetic.PoissonQuantityGenerator(options.mean_motifs),
theMax=options.max_motifs, theMin=options.min_motifs), zeroProb=options.zero_prob)
)
for motifName in options.motifNames
]
)
sequenceSet = synthetic.GenerateSequenceNTimes(embedInBackground, options.numSeqs)
synthetic.printSequences(outputFileName_core+".simdata", sequenceSet,
includeFasta=True, includeEmbeddings=True,
prefix=options.prefix)
def motif_density(motif_name,
seq_length,
num_seqs,
min_counts,
max_counts,
GC_fraction,
central_bp=None):
"""
Returns sequences with motif density, along with embeddings array.
"""
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 = synthetic.InsideCentralBp(central_bp)
else:
position_generator = synthetic.UniformPositionGenerator()
quantity_generator = synthetic.UniformIntegerGenerator(
min_counts, max_counts)
embedders = [
synthetic.RepeatedEmbedder(
synthetic.SubstringEmbedder(
synthetic.ReverseComplementWrapper(substring_generator),
position_generator), quantity_generator)
]
embed_in_background = synthetic.EmbedInABackground(
synthetic.ZeroOrderBackgroundGenerator(
seq_length, discreteDistribution=get_distribution(GC_fraction)),
embedders)
generated_sequences = tuple(
synthetic.GenerateSequenceNTimes(embed_in_background,
num_seqs).generateSequences())
sequence_arr = np.array(
[generated_seq.seq for generated_seq in generated_sequences])
embedding_arr = [
generated_seq.embeddings for generated_seq in generated_sequences
]
return sequence_arr, embedding_arr
def test_uniform_positions(self):
pseudocount_prob = 0.001
pwm_name = "CTCF_known1"
num_sequences = 10000
sequence_length = 50
loaded_motifs = sn.LoadedEncodeMotifs(simdna.ENCODE_MOTIFS_PATH,
pseudocountProb=pseudocount_prob)
substring_generator = sn.PwmSamplerFromLoadedMotifs(
loaded_motifs, pwm_name)
position_generator = sn.UniformPositionGenerator()
embedders = [
sn.SubstringEmbedder(substring_generator, position_generator)
]
embed_in_background = sn.EmbedInABackground(
sn.ZeroOrderBackgroundGenerator(sequence_length,
discreteDistribution={
'A': 0.3,
'C': 0.2,
'G': 0.2,
'T': 0.3
}), embedders)
generated_sequences = list(
sn.GenerateSequenceNTimes(embed_in_background,
num_sequences).generateSequences())
motif_length = len(loaded_motifs.getPwm(pwm_name).getRows())
start_pos_count = np.zeros(sequence_length - motif_length + 1)
for seq in generated_sequences:
assert len(seq.seq) == sequence_length
embeddings = seq.embeddings
for embedding in embeddings:
assert (embedding.what.string ==
seq.seq[embedding.startPos:embedding.startPos +
len(embedding.what.string)])
start_pos_count[embedding.startPos] += 1
start_pos_count = start_pos_count / float(len(generated_sequences))
np.testing.assert_almost_equal(start_pos_count,
1.0 / len(start_pos_count), 2)
def test_background_generator(self):
random.seed(1234)
np.random.seed(1234)
seq_length = 100
#for testing, not biologically realistic
freqs = {'A': 0.1, 'C': 0.2, 'G': 0.3, 'T': 0.4}
embed_in_background = sn.EmbedInABackground(
sn.ZeroOrderBackgroundGenerator(
seq_length,
discreteDistribution=freqs), [])
generated_sequences = sn.GenerateSequenceNTimes(
embed_in_background, 500).generateSequences()
generated_seqs = [seq.seq for seq in generated_sequences]
char_count = defaultdict(lambda: 0)
for seq in generated_seqs:
assert len(seq) == seq_length
for char in seq:
char_count[char] += 1
total_chars = sum(char_count.values())
actual_freqs = {val: char_count[val]/float(total_chars)
for val in char_count}
for key in freqs:
np.testing.assert_almost_equal(actual_freqs[key], freqs[key], 2)
def test_density_motif_embedding(self):
random.seed(1234)
np.random.seed(1234)
min_counts = 2
max_counts = 5
pseudocount_prob = 0.001
pwm_name = "CTCF_known1"
num_sequences = 5000
loaded_motifs = sn.LoadedEncodeMotifs(simdna.ENCODE_MOTIFS_PATH,
pseudocountProb=pseudocount_prob)
substring_generator = sn.PwmSamplerFromLoadedMotifs(
loaded_motifs, pwm_name)
position_generator = sn.UniformPositionGenerator()
quantity_generator = sn.UniformIntegerGenerator(min_counts, max_counts)
embedders = [
sn.RepeatedEmbedder(
sn.SubstringEmbedder(
sn.ReverseComplementWrapper(
substring_generator), position_generator),
quantity_generator)]
embed_in_background = sn.EmbedInABackground(
sn.ZeroOrderBackgroundGenerator(
500, discreteDistribution={'A':0.3,'C':0.2,
'G':0.2,'T':0.3}),
embedders)
generated_sequences = list(sn.GenerateSequenceNTimes(
embed_in_background, num_sequences).generateSequences())
assert len(generated_sequences) == num_sequences
actual_pwm = np.array([[0.095290, 0.318729, 0.083242, 0.502738],
[0.182913, 0.158817, 0.453450, 0.204819],
[0.307777, 0.053669, 0.491785, 0.146769],
[0.061336, 0.876232, 0.023001, 0.039430],
[0.008762, 0.989047, 0.000000, 0.002191],
[0.814896, 0.014239, 0.071194, 0.099671],
[0.043812, 0.578313, 0.365827, 0.012048],
[0.117325, 0.474781, 0.052632, 0.355263],
[0.933114, 0.012061, 0.035088, 0.019737],
[0.005488, 0.000000, 0.991218, 0.003293],
[0.365532, 0.003293, 0.621295, 0.009879],
[0.059276, 0.013172, 0.553238, 0.374314],
[0.013187, 0.000000, 0.978022, 0.008791],
[0.061538, 0.008791, 0.851648, 0.078022],
[0.114411, 0.806381, 0.005501, 0.073707],
[0.409241, 0.014301, 0.557756, 0.018702],
[0.090308, 0.530837, 0.338106, 0.040749],
[0.128855, 0.354626, 0.080396, 0.436123],
[0.442731, 0.199339, 0.292952, 0.064978]])
actual_pwm = actual_pwm*(1-pseudocount_prob) + pseudocount_prob/4
np.testing.assert_almost_equal(np.sum(actual_pwm,axis=-1),1.0,6)
np.testing.assert_almost_equal(
actual_pwm,
np.array(loaded_motifs.getPwm(pwm_name).getRows()))
letter_to_index = {'A':0, 'C':1, 'G':2, 'T':3}
reconstructed_pwm_fwd = np.zeros_like(actual_pwm)
reconstructed_pwm_rev = np.zeros_like(actual_pwm)
quantity_distribution = defaultdict(lambda: 0)
total_fwd_embeddings = 0.0
total_rev_embeddings = 0.0
for seq in generated_sequences:
embeddings = seq.embeddings
quantity_distribution[len(embeddings)] += 1
for embedding in embeddings:
assert (embedding.what.string
==seq.seq[embedding.startPos:
embedding.startPos+len(embedding.what.string)])
if ('revComp' in embedding.what.getDescription()):
total_rev_embeddings += 1
else:
total_fwd_embeddings += 1
for char_idx, char in enumerate(embedding.what.string):
if ('revComp' in embedding.what.getDescription()):
arr = reconstructed_pwm_rev
else:
arr = reconstructed_pwm_fwd
arr[char_idx][letter_to_index[char]] += 1
total_embeddings = total_fwd_embeddings + total_rev_embeddings
np.testing.assert_almost_equal(
total_fwd_embeddings/total_embeddings, 0.5, 2)
#normalize each column of reconstructed_pwm
reconstructed_pwm_fwd = reconstructed_pwm_fwd/total_fwd_embeddings
reconstructed_pwm_rev = reconstructed_pwm_rev/total_rev_embeddings
np.testing.assert_almost_equal(actual_pwm, reconstructed_pwm_fwd, 2)
np.testing.assert_almost_equal(actual_pwm,
reconstructed_pwm_rev[::-1,::-1], 2)
#test the quantities of motifs were sampled uniformly
for quantity in range(min_counts, max_counts+1):
np.testing.assert_almost_equal(
quantity_distribution[quantity]/float(num_sequences),
1.0/(max_counts-min_counts+1),2)
def motifGrammarSimulation(options):
pc = 0.001
bestHit = options.bestHit
pathToMotifs = options.pathToMotifs
loadedMotifs = synthetic.LoadedEncodeMotifs(pathToMotifs,
pseudocountProb=pc)
motifName1 = options.motifName1
motifName2 = options.motifName2
seqLength = options.seqLength
numSeq = options.numSeq
generationSetting = options.generationSetting
outputFileName = "motifGrammarSimulation_" + generationSetting + (
"_bestHit" if bestHit else "")
if (generationSetting is not generationSettings.singleMotif2):
outputFileName += "_motif1-" + motifName1
if (generationSetting is not generationSettings.singleMotif1):
outputFileName += "_motif2-" + motifName2
outputFileName += "_seqLength" + str(seqLength) + "_numSeq" + str(
numSeq) + ".simdata"
kwargs = {'loadedMotifs': loadedMotifs}
if (bestHit):
theClass = synthetic.BestHitPwmFromLoadedMotifs
else:
theClass = synthetic.PwmSamplerFromLoadedMotifs
motif1Generator = theClass(motifName=motifName1, **kwargs)
motif2Generator = theClass(motifName=motifName2, **kwargs)
motif1Embedder = synthetic.SubstringEmbedder(
substringGenerator=motif1Generator)
motif2Embedder = synthetic.SubstringEmbedder(
substringGenerator=motif2Generator)
embedders = []
if (generationSetting == generationSettings.allBackground
or generationSetting == generationSettings.twoMotifs):
namePrefix = "synthNeg"
else:
namePrefix = "synthPos"
if (generationSetting == generationSettings.allBackground):
pass
elif (generationSetting in [
generationSettings.singleMotif1, generationSettings.twoMotifs,
generationSettings.singleMotif2
]):
if (generationSetting == generationSettings.singleMotif1):
embedders.append(motif1Embedder)
elif (generationSetting == generationSettings.singleMotif2):
embedders.append(motif2Embedder)
elif (generationSetting == generationSettings.twoMotifs):
embedders.append(motif1Embedder)
embedders.append(motif2Embedder)
else:
raise RuntimeError("Unsupported generation setting: " +
generationSetting)
elif (generationSetting in [
generationSettings.twoMotifsFixedSpacing,
generationSettings.twoMotifsVariableSpacing
]):
if (generationSetting == generationSettings.twoMotifsFixedSpacing):
separationGenerator = synthetic.FixedQuantityGenerator(
options.fixedSpacingOrMinSpacing)
elif (generationSetting == generationSettings.twoMotifsVariableSpacing
):
separationGenerator = synthetic.UniformIntegerGenerator(
minVal=options.fixedSpacingOrMinSpacing,
maxVal=options.maxSpacing)
else:
raise RuntimeError("unsupported generationSetting:" +
generationSetting)
embedders.append(
synthetic.EmbeddableEmbedder(
embeddableGenerator=synthetic.PairEmbeddableGenerator(
embeddableGenerator1=motif1Generator,
embeddableGenerator2=motif2Generator,
separationGenerator=separationGenerator)))
else:
raise RuntimeError("unsupported generationSetting:" +
generationSetting)
embedInBackground = synthetic.EmbedInABackground(
backgroundGenerator=synthetic.ZeroOrderBackgroundGenerator(seqLength),
embedders=embedders,
namePrefix=namePrefix)
sequenceSet = synthetic.GenerateSequenceNTimes(embedInBackground, numSeq)
synthetic.printSequences(outputFileName,
sequenceSet,
includeFasta=True,
includeEmbeddings=True)
def simulate_heterodimer_grammar(motif1, motif2, seq_length, min_spacing,
max_spacing, num_pos, num_neg, GC_fraction):
"""
Simulates two classes of sequences with motif1 and motif2:
- Positive class sequences with motif1 and motif2 positioned
min_spacing and max_spacing
- Negative class sequences with independent motif1 and motif2 positioned
anywhere in the sequence, not as a heterodimer grammar
Parameters
----------
seq_length : int, length of sequence
GC_fraction : float, GC fraction in background sequence
num_pos : int, number of positive class sequences
num_neg : int, number of negatice class sequences
motif1 : str, encode motif name
motif2 : str, encode motif name
min_spacing : int, minimum inter motif spacing
max_spacing : int, maximum inter motif spacing
Returns
-------
sequence_arr : 1darray
Array with sequence strings.
y : 1darray
Array with positive/negative class labels.
embedding_arr: list
List of embedding objects.
"""
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 = synthetic.EmbedInABackground(
synthetic.ZeroOrderBackgroundGenerator(
seq_length, discreteDistribution=get_distribution(GC_fraction)),
[embedder])
generated_sequences = tuple(
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 = [
generated_seq.embeddings for generated_seq in generated_sequences
]
nongrammar_sequence_arr, _, negative_embedding_arr = simulate_multi_motif_embedding(
[motif1, motif2], seq_length, 2, 2, num_neg, GC_fraction)
sequence_arr = np.concatenate(
(grammar_sequence_arr, nongrammar_sequence_arr))
y = np.array([[True]] * num_pos + [[False]] * num_neg)
embedding_arr = positive_embedding_arr + negative_embedding_arr
return sequence_arr, y, embedding_arr
def simulate_multi_motif_embedding(motif_names, seq_length, min_num_motifs,
max_num_motifs, num_seqs, GC_fraction):
"""
Generates data for multi motif recognition task.
Parameters
----------
motif_names : list
List of strings.
seq_length : int
min_num_motifs : int
max_num_motifs : int
num_seqs : int
GC_fraction : float
Returns
-------
sequence_arr : 1darray
Contains sequence strings.
y : ndarray
Contains labels for each motif.
embedding_arr: 1darray
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 = 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 = 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(
synthetic.GenerateSequenceNTimes(embed_in_background,
num_seqs).generateSequences())
sequence_arr = np.array(
[generated_seq.seq for generated_seq in generated_sequences])
label_generator = synthetic.IsInTraceLabelGenerator(
np.asarray(motif_names))
y = np.array([
label_generator.generateLabels(generated_seq)
for generated_seq in generated_sequences
],
dtype=bool)
embedding_arr = [
generated_seq.embeddings for generated_seq in generated_sequences
]
return sequence_arr, y, embedding_arr