import itertools
from singlecellmultiomics.molecule import Molecule
from singlecellmultiomics.molecule.nlaIII import NlaIIIMolecule
from singlecellmultiomics.molecule.nlaIII import AnnotatedNLAIIIMolecule
from singlecellmultiomics.molecule.chic import CHICMolecule
from singlecellmultiomics.molecule.chic import AnnotatedCHICMolecule
from collections import Counter
from singlecellmultiomics.utils.sequtils import complement
from itertools import product
from matplotlib.pyplot import get_cmap
from copy import copy
import numpy as np
complement_trans = str.maketrans('ATGC', 'TACG')
class TAPS:
# Methylated Cs get converted into T readout
def __init__(self, reference=None, reference_variants=None, taps_strand='F', **kwargs):
"""
Intialise the TAPS class
Args:
reference (pysam.FastaFile) : reference fasta file
reference_variants(pysam.VariantFile) : variants in comparison to supplied reference. @todo
"""
assert reference is None, 'reference is now supplied by the molecule'
if reference_variants is not None:
raise NotImplementedError()
self.overlap_tag = 'XM'
"""
z unmethylated C in CpG context (CG)
Z methylated C in CpG context (CG)
x unmethylated C in CHG context ( C[ACT]G )
X methylated C in CHG context ( C[ACT]G )
h unmethylated C in CHH context ( C[ACT][ACT] )
H methylated C in CHH context ( C[ACT][ACT] )
"""
self.context_mapping = dict()
self.context_mapping[False] = {
'CGA': 'z',
'CGT': 'z',
'CGC': 'z',
'CGG': 'z',
'CAG': 'x',
'CCG': 'x',
'CTG': 'x',
}
self.context_mapping[True] = {
context: letter.upper() for context,
letter in self.context_mapping[False].items()}
self.taps_strand = taps_strand
for x in list(itertools.product('ACT', repeat=2)):
self.context_mapping[True][''.join(['C'] + list(x))] = 'H'
self.context_mapping[False][''.join(['C'] + list(x))] = 'h'
self.colormap = copy(get_cmap('RdYlBu_r')) # Make a copy
self.colormap.set_bad((0,0,0)) # For reads without C's
def position_to_context(
self,
chromosome,
position,
ref_base,
observed_base='N',
strand=None,
reference=None
):
"""Extract bismark call letter from a chromosomal location given the observed base
Args:
chromosome (str) : chromosome / contig of location to test
position (int) : genomic location of location to test
observed_base(str) : base observed in the read
strand(int) : mapping strand, False: forward, True: Reverse
Returns:
context(str) : 3 basepair context
bismark_letter(str) : bismark call
"""
assert reference is not None
assert strand is not None
qbase = observed_base.upper()
#ref_base = reference.fetch( chromosome, position, position + 1).upper()
# if a vcf file is supplied we can extract the possible reference bases
# @todo
context = None
methylated = None
try:
if ref_base == 'C':
context = reference.fetch(chromosome, position, position + 3).upper()
if qbase == 'T':
methylated = True
elif qbase=='C':
methylated = False
elif ref_base == 'G':
origin = reference.fetch( chromosome, position - 2, position + 1).upper()
context = origin.translate(complement_trans)[::-1]
if qbase == 'A':
methylated = True
elif qbase == 'G':
methylated = False
else:
raise ValueError('Only supply reference C or G')
except ValueError: # Happens when the coordinates are outstide the reference:
context = None
methylated = None
except FileNotFoundError:
raise ValueError('Got a file not found error. This is probably caused by the reference handle being shared between processes. Stop doing that (quick solution: disable multithreading/multiprocess).')
#print(ref_base, qbase, strand, position, chromosome,context, '?' if methylated is None else ('methylated' if methylated else 'not methylated'))
if methylated is None:
symbol='.'
else:
symbol = self.context_mapping[methylated].get(context, '.')
return context, symbol
def molecule_to_context_call_dict(self, molecule):
"""Extract bismark call_string dictionary from a molecule
Args:
molecule(singlecellmultiomics.molecule.Molecule) : molecule to extract calls from
Returns:
context_call_dict(dict) : {(chrom,pos):bismark call letter}
"""
call_dict = {} # (chrom,pos)-> "bismark" call_string
for (chrom, pos), base in molecule.get_consensus().items():
context, letter = self.position_to_context(chromosome=molecule.chromosome,
position=pos,
reference=molecule.reference,
observed_base=base,
strand=molecule.strand)
if letter is not None:
call_dict[(chrom, pos)] = letter
return call_dict
class TAPSMolecule(Molecule):
def __init__(self, fragments=None, taps=None, classifier=None, taps_strand='F', allow_unsafe_base_calls=False, methylation_consensus_kwargs=None, **kwargs):
""" TAPSMolecule
Args:
fragments(list) : list of fragments to associate with the Molecule
taps(singlecellmultiomics.molecule.TAPS) : TAPS class which performs the methylation calling
classifier : fitted sklearn classifier, when supplied this classifier is used to obtain a consensus from which the methylation calls are generated.
"""
Molecule.__init__(self, fragments=fragments, **kwargs)
if taps is None:
raise ValueError("""Supply initialised TAPS class
taps = singlecellmultiomics.molecule.TAPS( )
""")
self.taps = taps # initialised TAPS class**self.
self.methylation_call_dict = None
self.classifier = classifier
self.taps_strand = taps_strand
self.allow_unsafe_base_calls = allow_unsafe_base_calls
self.get_consensus_dictionaries_kwargs = methylation_consensus_kwargs if methylation_consensus_kwargs is not None else dict()
def add_cpg_color_tag_to_read(self, read):
try:
CpG_methylation_rate = read.get_tag('sZ') / (read.get_tag('sZ') + read.get_tag('sz'))
cfloat = self.taps.colormap(CpG_methylation_rate)[:3]
except Exception as e:
CpG_methylation_rate = None
cfloat = self.taps.colormap._rgba_bad[:3]
read.set_tag('YC', '%s,%s,%s' % tuple((int(x * 255) for x in cfloat)))
def __finalise__(self):
super().__finalise__()
self.obtain_methylation_calls(**self.get_consensus_dictionaries_kwargs)
for read in self.iter_reads():
try:
CpG_methylation_rate = read.get_tag('sZ')/(read.get_tag('sZ')+read.get_tag('sz'))
cfloat = self.taps.colormap(CpG_methylation_rate)[:3]
except Exception as e:
CpG_methylation_rate = None
cfloat = self.taps.colormap._rgba_bad[:3]
read.set_tag('YC', '%s,%s,%s' % tuple( (int(x*255) for x in cfloat)))
def write_tags_to_psuedoreads(self, reads, call_super=True):
if call_super:
Molecule.write_tags_to_psuedoreads(self,reads)
for read in reads:
self.add_cpg_color_tag_to_read(read)
def is_valid(self, set_rejection_reasons=False):
if not super().is_valid(set_rejection_reasons=set_rejection_reasons):
return False
"""
try:
consensus = self.get_consensus(allow_unsafe=self.allow_unsafe_base_calls)
except ValueError:
if set_rejection_reasons:
self.set_rejection_reason('no_consensus')
return False
except TypeError:
if set_rejection_reasons:
self.set_rejection_reason('getPairGenomicLocations_failed')
return False
"""
if self.methylation_call_dict is None:
if set_rejection_reasons:
self.set_rejection_reason('methylation_calls_failed')
return False
return True
#def obtain_methylation_calls_experimental(self):
def obtain_methylation_calls(self, **get_consensus_dictionaries_kwargs):
""" This methods returns a methylation call dictionary
returns:
mcalls(dict) : (chrom,pos) : {'consensus': consensusBase, 'reference': referenceBase, 'call': call}
"""
expected_base_to_be_converted = ('G' if self.strand else 'C') \
if self.taps_strand=='F' else ('C' if self.strand else 'G')
try:
if True:
c_pos_consensus, phreds, coverage = self.get_consensus(dove_safe = not self.allow_unsafe_base_calls,
only_include_refbase=expected_base_to_be_converted,
with_probs_and_obs=True, # Include phred scores and coverage
**get_consensus_dictionaries_kwargs)
else:
c_pos_consensus = self.get_consensus(dove_safe = not self.allow_unsafe_base_calls,
only_include_refbase=expected_base_to_be_converted,
with_probs_and_obs=False, # Include phred scores and coverage
**get_consensus_dictionaries_kwargs)
except ValueError as e:
if 'MD tag not present' in str(e):
self.set_rejection_reason("MD_TAG_MISSING")
return None
raise
# obtain the context of the conversions:
conversion_contexts = {
(contig, position):
{'consensus': base_call,
'reference_base': expected_base_to_be_converted,
'cov': len(phreds[(contig, position)][base_call]),
'qual': np.mean( phreds[(contig, position)][base_call] ),
'context': self.taps.position_to_context(
chromosome=contig,
position=position,
reference=self.reference,
observed_base=base_call,
ref_base=expected_base_to_be_converted,
strand=self.strand)[1]}
for (contig, position), base_call in c_pos_consensus.items()}
# Write bismark tags:
self.set_methylation_call_tags(conversion_contexts)
return conversion_contexts
class TAPSNlaIIIMolecule(NlaIIIMolecule, TAPSMolecule):
"""Molecule class for combined TAPS and NLAIII """
def __init__(self, fragments=None, taps=None, taps_strand='R', **kwargs):
NlaIIIMolecule.__init__(self, fragments, **kwargs)
TAPSMolecule.__init__(self, fragments=fragments, taps_strand=taps_strand, taps=taps, **kwargs)
def write_tags(self):
NlaIIIMolecule.write_tags(self)
TAPSMolecule.write_tags(self)
def __finalise__(self):
NlaIIIMolecule.__finalise__(self)
TAPSMolecule.__finalise__(self)
def is_valid(self, set_rejection_reasons=False):
return NlaIIIMolecule.is_valid(
self, set_rejection_reasons=set_rejection_reasons) and TAPSMolecule.is_valid(
self, set_rejection_reasons=set_rejection_reasons)
def write_tags_to_psuedoreads(self, reads):
NlaIIIMolecule.write_tags_to_psuedoreads(self, reads)
TAPSMolecule.write_tags_to_psuedoreads(self, reads, call_super=False)
class AnnotatedTAPSNlaIIIMolecule(AnnotatedNLAIIIMolecule, TAPSMolecule):
"""Molecule class for combined TAPS, NLAIII and transcriptome """
def __init__(self, fragments=None, features=None, taps=None, **kwargs):
assert features is not None, "Supply features!"
AnnotatedNLAIIIMolecule.__init__(
self, fragments, features=features, **kwargs)
TAPSMolecule.__init__(self, fragments=fragments, taps=taps, **kwargs)
def write_tags(self):
AnnotatedNLAIIIMolecule.write_tags(self)
TAPSMolecule.write_tags(self)
def is_valid(self, set_rejection_reasons=False):
return AnnotatedNLAIIIMolecule.is_valid(
self, set_rejection_reasons=set_rejection_reasons) and TAPSMolecule.is_valid(
self, set_rejection_reasons=set_rejection_reasons)
def write_tags_to_psuedoreads(self, reads):
AnnotatedNLAIIIMolecule.write_tags_to_psuedoreads(self, reads)
TAPSMolecule.write_tags_to_psuedoreads(self, reads, call_super=False)
class TAPSCHICMolecule(CHICMolecule, TAPSMolecule):
"""Molecule class for combined TAPS and CHIC """
def __init__(self, fragments=None, taps=None, taps_strand='R', **kwargs):
CHICMolecule.__init__(self, fragments, **kwargs)
TAPSMolecule.__init__(self, fragments=fragments, taps_strand=taps_strand,taps=taps, **kwargs)
def write_tags(self):
CHICMolecule.write_tags(self)
TAPSMolecule.write_tags(self)
def is_valid(self, set_rejection_reasons=False):
return CHICMolecule.is_valid(
self, set_rejection_reasons=set_rejection_reasons) and TAPSMolecule.is_valid(
self, set_rejection_reasons=set_rejection_reasons)
def __finalise__(self):
CHICMolecule.__finalise__(self)
TAPSMolecule.__finalise__(self)
def write_tags_to_psuedoreads(self, reads):
CHICMolecule.write_tags_to_psuedoreads(self, reads)
TAPSMolecule.write_tags_to_psuedoreads(self, reads, call_super=False)
class AnnotatedTAPSCHICMolecule(AnnotatedCHICMolecule, TAPSMolecule):
"""Molecule class for combined TAPS, CHIC and transcriptome """
def __init__(self, fragments=None, features=None, taps_strand='R', taps=None, **kwargs):
assert features is not None, "Supply features!"
AnnotatedCHICMolecule.__init__(
self, fragments, features=features, **kwargs)
TAPSMolecule.__init__(self, fragments=fragments, taps_strand=taps_strand,taps=taps, **kwargs)
def write_tags(self):
AnnotatedCHICMolecule.write_tags(self)
TAPSMolecule.write_tags(self)
def __finalise__(self):
AnnotatedCHICMolecule.__finalise__(self)
TAPSMolecule.__finalise__(self)
def is_valid(self, set_rejection_reasons=False):
return AnnotatedCHICMolecule.is_valid(
self, set_rejection_reasons=set_rejection_reasons) and TAPSMolecule.is_valid(
self, set_rejection_reasons=set_rejection_reasons)
def write_tags_to_psuedoreads(self, reads):
AnnotatedCHICMolecule.write_tags_to_psuedoreads(self, reads)
TAPSMolecule.write_tags_to_psuedoreads(self, reads, call_super=False)
def strip_array_tags(molecule):
for read in molecule.iter_reads():
read.set_tag('jM',None)
read.set_tag('jI',None)
return molecule
idmap = [''.join(p) for p in product('0123','0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ')]
context_ids = {''.join(ctx):idmap[i] for i,ctx in enumerate(product('ACTG',repeat=3))}
class TAPSPTaggedMolecule(AnnotatedTAPSNlaIIIMolecule):
def __finalise__(self):
AnnotatedTAPSNlaIIIMolecule.__finalise__(self)
# Additional finalisation steps:
conversions_count = 0
forward_counts = 0
rev_counts = 0
conversion_locations = []
mismatches = 0
ambig_count = 0
molecule_variants=set()
# Obtain consensus call for the molecule
consensus = self.get_consensus(allow_unsafe=True)
context_obs = Counter()
for read in self.iter_reads():
for qpos, refpos, reference_base in read.get_aligned_pairs(with_seq=True, matches_only=True):
location = (read.reference_name, refpos)
#if (location[0], location[1]) in variant_locations:
# skipped+=1
# continue
query = read.seq[qpos]
context = self.reference.fetch(self.chromosome, refpos-1, refpos+2).upper()
# Check if call is the same as the consensus call:
if query!=consensus.get((read.reference_name,refpos), 'X'):
continue
reference_base = reference_base.upper()
if read.is_reverse: # forward, we sequence the other side
reference_base = complement(reference_base)
query = complement(query)
context=complement(context)
if query!=reference_base:
mismatches += 1
molecule_variants.add(refpos)
if (reference_base, query) in ( ('A','G'), ('G','A'), ('T','C'), ('C','T')):
conversion_locations.append(f'{location[0]}:{location[1]+1} {reference_base}>{query}' )
conversions_count+=1
context_obs[context_ids.get(context,'99')]+=1
if (reference_base, query) in ( ('A','G'), ('G','A') ):
forward_counts+=1
elif (reference_base, query) in (('T','C'), ('C','T')):
rev_counts+=1
self.set_meta('mM',mismatches)
self.set_meta('pP',conversions_count)
self.set_meta('pR',rev_counts)
self.set_meta('pF',forward_counts)
self.set_meta('pE',','.join(conversion_locations))
for context,obs in context_obs.most_common():
self.set_meta(context,obs)
self.mismatches = mismatches
self.conversions_count = conversions_count
self.context_obs = context_obs
self.rev_counts = rev_counts
self.forward_counts = forward_counts
self.conversion_locations=conversion_locations
def write_tags_to_psuedoreads(self, reads):
AnnotatedTAPSNlaIIIMolecule.write_tags_to_psuedoreads(self,reads)
Datasets
Models
