def __init__(self, args=None, region=None, name='hg19'):
self.genome_name = name
self.chrom = None
self.sites = None
self.region_str = None
self.bp_tuple = None
self.chrs_sz = None # DataFrame of chromosomes sizes (in number of sites)
self.name = name
self.args = args
# todo: this could be prettier
if args is not None:
self.name = args.genome
self.genome = GenomeRefPaths(self.name)
if args.sites:
self.parse_sites(args.sites)
elif args.region:
self.parse_region(args.region)
elif region is not None:
self.genome = GenomeRefPaths(self.name)
self.parse_region(region)
else:
raise IllegalArgumentError('Invalid GR init {}'.format(region))
self.nr_sites = None if self.sites is None else self.sites[
1] - self.sites[0]
self.annotation = self.add_anno()
def __init__(self, args, betas):
self.betas = betas
max_cpg = min(args.max_cpg, args.max_bp // 2)
assert (max_cpg > 1)
self.genome = GenomeRefPaths(args.genome)
self.param_dict = {
'betas': betas,
'pcount': args.pcount,
'max_cpg': max_cpg,
'max_bp': args.max_bp,
'revdict': self.genome.revdict_path,
'genome': self.genome
}
self.args = args
def set_lists(self):
# black/white lists:
blacklist = self.args.blacklist
whitelist = self.args.whitelist
if blacklist == True:
blacklist = GenomeRefPaths(self.args.genome).blacklist
elif whitelist == True:
whitelist = GenomeRefPaths(self.args.genome).whitelist
if blacklist:
validate_single_file(blacklist)
elif whitelist:
validate_single_file(whitelist)
if self.verbose:
eprint(f'[wt bam2pat] blacklist: {blacklist}')
eprint(f'[wt bam2pat] whitelist: {whitelist}')
return blacklist, whitelist
def pat2beta(pat_path, out_dir, args, force=True):
validate_single_file(pat_path)
if pat_path.endswith('.pat.gz'):
cmd = 'gunzip -cd'
elif pat_path.endswith('.pat'):
cmd = 'cat'
else:
raise IllegalArgumentError(f'Invalid pat suffix: {pat_path}')
suff = '.lbeta' if args.lbeta else '.beta'
out_beta = op.join(out_dir, splitextgz(op.basename(pat_path))[0] + suff)
if not delete_or_skip(out_beta, force):
return
if args.threads > 1 and pat_path.endswith('.pat.gz') and op.isfile(
pat_path + '.csi'):
arr = mult_pat2beta(pat_path, args)
else:
nr_sites = GenomeRefPaths(args.genome).get_nr_sites()
cmd += f' {pat_path} | {pat2beta_tool} {1} {nr_sites + 1}'
x = subprocess.check_output(cmd, shell=True).decode()
arr = np.fromstring(x, dtype=int, sep=' ').reshape((-1, 2))
trim_to_uint8(arr, args.lbeta).tofile(out_beta)
return out_beta
def __init__(self, args):
self.args = args
self.out_path = args.out_path
self.debug = args.debug
if not delete_or_skip(self.out_path, self.args.force):
return
# load bed file:
self.df = load_bed(args.bed_path, 100000 if self.debug else None)
self.genome = GenomeRefPaths(args.genome)
# load chromosomes sizes (in GpGs):
self.cf = self.genome.get_chrom_cpg_size_table()
self.cf['size'] = np.cumsum(self.cf['size'])
self.proc_bed()
def add_cpgs_to_bed(bed_file, genome, drop_empty, threads, add_anno):
# load bed file:
df = load_bed(bed_file)
# load chromosomes sizes (in GpGs):
cf = GenomeRefPaths(genome).get_chrom_cpg_size_table()
cf['size'] = np.cumsum(cf['size'])
chroms = sorted(set(cf.chr) & set(df.chr))
params = [(df[df.chr == chrom].copy(), cf, genome) for chrom in chroms]
p = Pool(threads)
arr = p.starmap(chr_thread, params)
p.close()
p.join()
# concat chromosomes
r = pd.concat(arr)
# merge with original table, to keep the order and the empty regions
r = df.merge(r[COORDS_COLS5], how='left', on=COORDS_COLS3)
r = r[COORDS_COLS5 + list(r.columns)[3:-2]]
# add annotations:
if add_anno:
r = get_anno(r, genome, bed_file)
# drop regions w/o CpGs
if drop_empty:
r.dropna(inplace=True, subset=['startCpG', 'endCpG'])
return r
def load_blocks_file(self):
if self.verbose:
eprint('loading blocks...')
names = ['chr', 'start', 'end', 'startCpG', 'endCpG']
cols = range(len(names))
nrows = DEBUG_NR if self.args.debug else None
blocks_path = self.args.blocks_path
if blocks_path is None:
blocks_path = GenomeRefPaths().blocks
df = pd.read_csv(self.args.blocks_path,
sep='\t',
header=None,
names=names,
nrows=nrows,
usecols=cols)
df['lenCpG'] = df['endCpG'] - df['startCpG']
self.keepinds = df['lenCpG'] >= self.args.min_cpg
self.orig_nr_blocks = df.shape[0]
df = df[self.keepinds].reset_index(drop=True)
self.nr_blocks = df.shape[0]
if self.verbose:
eprint(f'loaded {self.orig_nr_blocks:,}')
if self.nr_blocks != self.orig_nr_blocks:
eprint(
f'droppd to {self.nr_blocks:,} with >={self.args.min_cpg} CpGs'
)
return df
def add_bed_to_cpgs(site_file, genome, out_path=None):
validate_local_exe(add_loci_tool)
g = GenomeRefPaths(genome)
cmd = f'cat {site_file} | {add_loci_tool} {g.dict_path} {g.chrom_cpg_sizes}'
if (out_path is not None) and out_path != sys.stdout:
cmd += f' > {out_path}'
subprocess.check_call(cmd, shell=True)
def __init__(self, args):
self.args = args
self.gr = GenomicRegion(args)
self.outdir = args.outdir
self.name = ''
if not op.isdir(self.outdir):
raise IllegalArgumentError('Invalid output directory: ' +
self.outdir)
self.chrom_sizes = GenomeRefPaths(args.genome).chrom_sizes
def __init__(self, args):
self.args = args
self.gr = GenomicRegion(args)
self.debug = args.debug
self.outdir = args.outdir
if not op.isdir(self.outdir):
raise IllegalArgumentError('Invalid output directory: ' +
self.outdir)
self.chrom_sizes = GenomeRefPaths(args.genome).chrom_sizes
self.ref_dict = self.load_dict()
def __init__(self, args=None, region=None, sites=None, genome_name=None):
self.genome_name = get_genome_name(genome_name)
self.chrom = None
self.sites = sites
self.region_str = region
self.bp_tuple = None
self.args = args
# todo: this could be prettier
if args is not None:
self.genome_name = get_genome_name(args.genome)
self.genome = GenomeRefPaths(self.genome_name)
if args.sites:
self.parse_sites(args.sites)
elif args.region:
self.parse_region(args.region)
elif region is not None:
self.genome = GenomeRefPaths(self.genome_name)
self.parse_region(region)
elif sites is not None:
self.genome = GenomeRefPaths(self.genome_name)
self.parse_sites(sites)
else:
raise IllegalArgumentError(f'Invalid GR init {region}')
self.nr_sites = None if self.sites is None else self.sites[
1] - self.sites[0]
self.annotation = self.add_anno()
def get_anno(df, genome, bed_file):
try:
anno_path = GenomeRefPaths(genome).annotations
if anno_path is None:
return df
cmd = f'cut -f1-3 {bed_file} | sort -k1,1 -k2,2n -u | '
cmd += f'bedtools intersect -a - -b {anno_path} -wao | '
cmd += f'bedtools merge -i - -c 7,8 -o distinct,distinct'
names = COORDS_COLS3 + ['type', 'gene']
rf = read_shell(cmd, names=names)
return df.merge(rf, how='left', on=COORDS_COLS3)
except Exception as e:
eprint(f'[wt convert] WARNING: No annotations added.')
eprint(e)
return df
def mult_pat2beta(pat_path, args):
processes = []
with Pool(args.threads) as p:
ct = GenomeRefPaths(args.genome).get_chrom_cpg_size_table()
x = np.cumsum([0] + list(ct['size'])) + 1
chroms = list(ct['chr'])
for i, chrom in enumerate(chroms):
start = x[i]
end = x[i + 1]
params = (pat_path, chrom, start, end)
processes.append(p.apply_async(chr_thread, params))
p.close()
p.join()
beta_files = [pr.get() for pr in processes]
res = np.concatenate(beta_files, axis=0)
return res
def mult_pat2beta(pat_path, out_beta, nr_sites, args):
processes = []
with Pool(args.threads) as p:
chroms = list(
GenomeRefPaths(args.genome).get_chrom_cpg_size_table()['chr'])
for chrom in sorted(chroms):
beta = '{}.{}.beta'.format(op.splitext(out_beta)[0], chrom)
params = (chrom, pat_path, beta, nr_sites)
processes.append(p.apply_async(chr_thread, params))
p.close()
p.join()
res = np.zeros((nr_sites, 2), dtype=np.uint8)
for bpath in [pr.get() for pr in processes]:
res += load_beta_data(bpath)
os.remove(bpath)
res.tofile(out_beta)
return out_beta
class AddCpGsToBed:
def __init__(self, args):
self.args = args
self.out_path = args.out_path
self.debug = args.debug
if not delete_or_skip(self.out_path, self.args.force):
return
# load bed file:
self.df = load_bed(args.bed_path, 100000 if self.debug else None)
self.genome = GenomeRefPaths(args.genome)
# load chromosomes sizes (in GpGs):
self.cf = self.genome.get_chrom_cpg_size_table()
self.cf['size'] = np.cumsum(self.cf['size'])
self.proc_bed()
def proc_bed(self):
processes = []
with Pool(self.args.threads) as p:
chroms = [x for x in self.cf.chr if x in self.df.chr.unique()]
for chrom in sorted(chroms):
params = (self.df[self.df.chr == chrom], chrom, self.cf)
processes.append(p.apply_async(chr_thread, params))
p.close()
p.join()
r = pd.concat([pr.get() for pr in processes])
if self.out_path is None:
self.out_path = sys.stdout
# r = self.reorder_to_original(r)
r.to_csv(self.out_path,
sep='\t',
header=None,
index=None,
na_rep='NaN',
mode='a')
def reorder_to_original(self, res):
return self.df[COORDS_COLS].merge(res, how='left', on=COORDS_COLS)
def pat2beta(pat_path, out_dir, args, force=True):
validate_single_file(pat_path)
if pat_path.endswith('.pat.gz'):
cmd = 'gunzip -cd'
elif pat_path.endswith('.pat'):
cmd = 'cat'
else:
raise IllegalArgumentError('Invalid pat suffix: {}'.format(pat_path))
out_beta = op.join(out_dir, splitextgz(op.basename(pat_path))[0] + '.beta')
if not delete_or_skip(out_beta, force):
return
nr_sites = GenomeRefPaths(args.genome).nr_sites
if args.threads > 1 and pat_path.endswith('.pat.gz') and op.isfile(
pat_path + '.csi'):
return mult_pat2beta(pat_path, out_beta, nr_sites, args)
cmd += ' {} | {} {} {}'.format(pat_path, PAT2BETA_TOOL, out_beta, nr_sites)
subprocess.check_call(cmd, shell=True)
return out_beta
def bedtools_conversion(bed_file, genome, drop_empty, add_anno, debug):
df = load_bed(bed_file)
tmp_name = tempfile.NamedTemporaryFile().name
df.sort_values(by=['chr', 'start', 'end']).iloc[:, :3].\
drop_duplicates().to_csv(tmp_name, sep='\t', header=None, index=None)
ref = GenomeRefPaths(genome).dict_path
cmd = f"tabix -R {tmp_name} {ref} | "
cmd += "awk -v OFS='\t' '{print $1,$2,$2+1,$3}' | "
cmd += " sort -k1,1 -k2,2n -u | " # sort is required for cases of overlapping blocks
cmd += f"bedtools intersect -sorted -b - -a {tmp_name} -loj | "
cmd += f"bedtools groupby -g 1,2,3 -c 7,7 -o first,last | "
cmd += " awk -v OFS='\t' '{print $1,$2,$3,$4,$5+1;}' "
cmd += "| sed 's/\.\t1/NA\tNA/g'" # replace missing values with (NA)s
if debug:
eprint(cmd.replace('\t', '\\t'))
rf = read_shell(cmd, names=COORDS_COLS5)
if not debug:
os.unlink(tmp_name)
# if there are missing values, the CpG columns' type
# will be float or object. Change it to Int64
if rf.empty:
raise IllegalArgumentError(
'[wt convert] Error: failed with bedtools wrapping')
if rf['startCpG'].dtype != int:
rf = rf.astype({'startCpG': 'Int64', 'endCpG': 'Int64'})
df = df.merge(rf, how='left', on=COORDS_COLS3)
df = df[COORDS_COLS5 + list(df.columns)[3:-2]]
if drop_empty:
df.dropna(inplace=True, subset=['startCpG', 'endCpG'])
# add annotations:
if add_anno:
df = get_anno(df, genome, bed_file)
return df
def test_multiple_regions(tabixed_bed_file, pat_file, num_threads, out_file,
is_strict, min_len, verbose):
peek_df = pd.read_csv(tabixed_bed_file,
sep='\t',
nrows=1,
header=None,
comment='#')
names = COORDS_COLS5
if len(peek_df.columns) < len(names):
msg = f'Invalid blocks file: {blocks_path}. less than {len(names)} columns.\n'
msg += f'Run wgbstools convert -L {blocks_path} -o OUTPUT_REGION_FILE to add the CpG columns'
raise IllegalArgumentError(msg)
chroms = GenomeRefPaths().get_chroms()
params_list = ((tabixed_bed_file, c, pat_file, is_strict, min_len, verbose)
for c in chroms)
p = Pool(num_threads)
arr = p.starmap(read_blocks_and_test, params_list)
p.close()
p.join()
region_p_val_list = [p_reg for x in arr for p_reg in x] # flatten
if not region_p_val_list:
if verbose:
eprint(f'[wt bimodal] empty list')
return
region_p_val_list = sorted(region_p_val_list, key=lambda elem: elem[1])
[block_lines, p_vals] = zip(*region_p_val_list)
accepted_blocks, corrected_p_vals = choose_blocks_by_fdr_bh(
p_vals, block_lines)
with open(out_file, "w") if out_file != "-" else sys.stdout as f_out:
for accepted_block, corrected_p_val in zip(accepted_blocks,
corrected_p_vals):
f_out.write(f"{accepted_block}\t{corrected_p_val:,.1e}\n")
class GenomicRegion:
def __init__(self, args=None, region=None, name='hg19'):
self.genome_name = name
self.chrom = None
self.sites = None
self.region_str = None
self.bp_tuple = None
self.chrs_sz = None # DataFrame of chromosomes sizes (in number of sites)
self.name = name
self.args = args
# todo: this could be prettier
if args is not None:
self.name = args.genome
self.genome = GenomeRefPaths(self.name)
if args.sites:
self.parse_sites(args.sites)
elif args.region:
self.parse_region(args.region)
elif region is not None:
self.genome = GenomeRefPaths(self.name)
self.parse_region(region)
else:
raise IllegalArgumentError('Invalid GR init {}'.format(region))
self.nr_sites = None if self.sites is None else self.sites[
1] - self.sites[0]
self.annotation = self.add_anno()
def add_anno(self):
if self.args is None or self.is_whole():
return
if self.args.no_anno:
return
anno_path = self.genome.annotations
if anno_path is None:
return
try:
cmd = 'tabix {} {} | cut -f4- | uniq'.format(
anno_path, self.region_str)
res = subprocess.check_output(cmd, shell=True).decode().strip()
return res
except subprocess.CalledProcessError:
eprint('Failed to retrieve annotation for reagion ',
self.region_str)
return
def parse_sites(self, sites_str):
""" Parse input of the type -s / --sites (e.g 15-25) """
# Parse sites string:
s1, s2 = self._sites_str_to_tuple(sites_str)
# Translate sites indexes to genomic loci:
self.chrom, region_from = self.index2locus(s1)
chrom2, region_to = self.index2locus(s2 - 1) # non-inclusive
region_to += 1 # include the hole last site (C and G)
if self.chrom != chrom2:
eprint('ERROR: sites range cross chromosomes! ({}, {})'.format(
s1, s2))
raise IllegalArgumentError('Invalid sites input')
# Update GR fields:
self.sites = (s1, s2)
self.region_str = "{}:{}-{}".format(self.chrom, region_from, region_to)
self.bp_tuple = (region_from, region_to)
def _chrome_size(self):
df = pd.read_csv(self.genome.chrom_sizes,
sep='\t',
header=None,
names=['chr', 'size'])
return int(df[df['chr'] == self.chrom]['size'])
def parse_region(self, region):
""" Parse input of the type -r / --region (e.g chr11:200-300) """
region = region.replace(',', '') # remove commas
chrome_match = re.match(r'^chr([\d]+|[XYM])$', region)
region_match = re.match(r'chr([\d]+|[XYM]):([\d]+)-([\d]+)', region)
# In case region is a whole chromosome
if chrome_match:
self.chrom = 'chr' + chrome_match.group(1)
region_from = 1
region_to = self._chrome_size()
# match region string to format chrom:from-to
elif region_match:
self.chrom = 'chr' + region_match.group(1)
region_from = int(region_match.group(2))
region_to = int(region_match.group(3))
if region_to <= region_from:
raise IllegalArgumentError(
'Invalid genomic region: {}. end before start'.format(
region))
if region_to > self._chrome_size() or region_from < 1:
raise IllegalArgumentError(
'Invalid genomic region: {}. Out of range'.format(region))
else:
raise IllegalArgumentError(
'Invalid genomic region: {}'.format(region))
# Update GR fields:
self.region_str = region
self.sites = self._region_str2sites()
self.bp_tuple = (region_from, region_to)
def _region_str2sites(self):
# find CpG indexes in range of the region:
# todo: find start and end separately (in case they are far apart)
cmd = 'tabix {} {} | '.format(self.genome.dict_path, self.region_str)
# cmd += 'awk \'{if (NR == 1) {first=substr($4,4)}}END{print first"-"substr($4,4)}\''
cmd += 'awk \'{if (NR == 1) {first=$3}}END{print first"-"$3+1}\''
# eprint(cmd)
res = subprocess.check_output(cmd, shell=True).decode()
# throw error if there are no CpGs in range
if res.strip() == '-1':
raise IllegalArgumentError(
'Invalid genomic region: {}. No CpGs in range'.format(
self.region_str))
s1, s2 = self._sites_str_to_tuple(res)
# s2 += 1 # non-inclusive
return s1, s2
def _sites_str_to_tuple(self, sites_str):
""" extract integers tuple (e.g (120, 130)) from a sites string (e.g '120-130') """
if sites_str:
sites_str = sites_str.replace(',', '')
matchObj = re.match(r'([\d]+)-([\d]+)', sites_str)
if matchObj:
site1 = int(matchObj.group(1))
site2 = int(matchObj.group(2))
if not self.genome.nr_sites + 1 >= site2 > site1 >= 1:
msg = 'sites violate the constraints: '
msg += '{} >= {} > {} >= 1'.format(
self.genome.nr_sites + 1, site2, site1)
raise IllegalArgumentError(msg)
return site1, site2
raise IllegalArgumentError(
'sites must be of format: ([\d])-([\d]).\nGot: {}'.format(
sites_str))
def index2chrom(self, site):
if self.chrs_sz is None:
self.chrs_sz = self.genome.get_chrom_cpg_size_table()
self.chrs_sz['borders'] = np.cumsum(self.chrs_sz['size'])
cind = np.searchsorted(
np.array(self.chrs_sz['borders']).flatten(), site)
return self.chrs_sz['chr'].loc[cind]
def index2locus(self, index):
"""
translate CpG index to genomic locus. e.g, CpG1 -> (chr1, 10469)
:param index: a site index in range [1, NR_SITES]
:return: chromosome, locus
"""
index = int(index)
# validate input
if not self.genome.nr_sites + 1 >= index >= 1:
print('Invalid site index:', index)
raise IllegalArgumentError('Out of range site index:', index)
# find locus:
with open(self.genome.revdict_path, 'rb') as f:
f.seek((index - 1) * 4)
loc = np.fromfile(f, dtype=np.int32, count=1)[0] - 1
return self.index2chrom(index), loc
def __str__(self):
if self.sites is None:
return 'Whole genome'
s1, s2 = self.sites
f, t = self.bp_tuple
# res = '{} ({:,} sites, {:,} bp, sites {}-{})'.format(self.region_str, s2 - s1, t - f + 1, s1, s2)
res = '{} - {:,}bp, {:,}CpGs: {}-{}'.format(self.region_str, t - f + 1,
s2 - s1, s1, s2)
if self.annotation:
res += '\n' + self.annotation
return res
def is_whole(self):
"""
:return: True iff no filters (-r, -s) were applied. i.e, this gr is the whole genome.
"""
return self.sites is None
class GenomicRegion:
def __init__(self, args=None, region=None, sites=None, genome_name=None):
self.genome_name = get_genome_name(genome_name)
self.chrom = None
self.sites = sites
self.region_str = region
self.bp_tuple = None
self.args = args
# todo: this could be prettier
if args is not None:
self.genome_name = get_genome_name(args.genome)
self.genome = GenomeRefPaths(self.genome_name)
if args.sites:
self.parse_sites(args.sites)
elif args.region:
self.parse_region(args.region)
elif region is not None:
self.genome = GenomeRefPaths(self.genome_name)
self.parse_region(region)
elif sites is not None:
self.genome = GenomeRefPaths(self.genome_name)
self.parse_sites(sites)
else:
raise IllegalArgumentError(f'Invalid GR init {region}')
self.nr_sites = None if self.sites is None else self.sites[
1] - self.sites[0]
self.annotation = self.add_anno()
def add_anno(self):
if self.args is None or self.is_whole() or 'no_anno' not in self.args:
return
elif self.args.no_anno:
return
anno_path = self.genome.annotations
if anno_path is None:
return
try:
cmd = f'tabix {anno_path} {self.region_str} | cut -f4- | uniq'
return subprocess.check_output(cmd, shell=True).decode().strip()
except subprocess.CalledProcessError:
eprint(
f'Failed to retrieve annotation for reagion {self.region_str}')
def parse_sites(self, sites_str):
""" Parse input of the type -s / --sites (e.g 15-25) """
# Parse sites string:
s1, s2 = self._sites_str_to_tuple(sites_str)
# Translate sites indexes to genomic loci:
self.chrom, region_from = self.index2locus(s1)
chrom2, region_to = self.index2locus(s2 - 1) # non-inclusive
region_to += 1 # include the whole last site (C and G)
if self.chrom != chrom2:
eprint(f'ERROR: sites range cross chromosomes! ({s1}, {s2})')
raise IllegalArgumentError('Invalid sites input')
# Update GR fields:
self.sites = (s1, s2)
self.region_str = f'{self.chrom}:{region_from}-{region_to}'
self.bp_tuple = (region_from, region_to)
def _chrome_size(self):
df = self.genome.get_chrom_size_table()
return int(df[df['chr'] == self.chrom]['size'])
def find_region_format(self, region):
region = region.replace(',', '') # remove commas
# In case region is a whole chromosome
chrome_match = re.match(r'^(chr)?([\d]+|[XYM]|(MT))$', region)
if chrome_match:
if region not in self.genome.get_chroms():
raise IllegalArgumentError(f'Unknown chromosome: {region}')
self.chrom = region
return region, 1, self._chrome_size()
# match region string to format chrom:from
uni_region_match = re.match(r'^(chr)?([\d]+|[XYM]|(MT)):([\d]+)$',
region)
if uni_region_match:
region_from = uni_region_match.group(4)
region += f'-{int(region_from) + 1}'
# match region string to format chrom:from-to
region_match = re.match(
r'^((chr)?([\d]+|[XYM]|(MT))):([\d]+)-([\d]+)$', region)
if not region_match:
raise IllegalArgumentError(f'Invalid genomic region: {region}')
self.chrom = region_match.group(1)
if self.chrom not in self.genome.get_chroms():
raise IllegalArgumentError(f'Unknown chromosome: {region}')
region_from = int(region_match.group(5))
region_to = int(region_match.group(6))
return region, region_from, region_to
def parse_region(self, region):
""" Parse input of the type -r / --region (e.g chr11:200-300) """
self.region_str, region_from, region_to = self.find_region_format(
region)
# validate region range:
if region_to <= region_from:
raise IllegalArgumentError(
f'Invalid genomic region: {region}. end before start')
if region_to > self._chrome_size() or region_from < 1:
raise IllegalArgumentError(
f'Invalid genomic region: {region}. Out of range')
# Update GR fields:
self.bp_tuple = (region_from, region_to)
self.sites = self._region_str2sites()
def _region_str2sites(self):
# find CpG indexes in range of the region:
cmd = f'tabix {self.genome.dict_path} {self.region_str} | '
# cmd += 'awk \'(NR==1){first=$3} {lbp=$2} END{print first"-"$3+1}\''
# if bp_tuple[1] equals exactly a loci of a CpG site, this site is *not* included
# e.g., in hg19, chr6:71046415-71046562 is 9718430-9718435 in sites
cmd += f"awk -v b={self.bp_tuple[1]} "
cmd += '\'(NR==1){first=$3} END{if ($2<b) {r+=1}; print first"-"$3+r}\''
res = subprocess.check_output(cmd, shell=True).decode()
# eprint(cmd)
if len(set(res.strip().split('-'))) == 1:
res = '-1'
# throw error if there are no CpGs in range
if res.strip() == '-1':
raise IllegalArgumentError(
f'Invalid genomic region: {self.region_str}. No CpGs in range')
s1, s2 = self._sites_str_to_tuple(res)
# s2 += 1 # non-inclusive
return s1, s2
def _sites_str_to_tuple(self, sites_str):
""" extract integers tuple (e.g (120, 130)) from a sites string (e.g '120-130') """
if not sites_str:
raise IllegalArgumentError(f'Empty sites string: {sites_str}')
sites_str = sites_str.replace(',', '')
# start-end syntax
matchObj = re.match(r'([\d]+)-([\d]+)', sites_str)
if matchObj:
site1 = int(matchObj.group(1))
site2 = int(matchObj.group(2))
# single site syntax:
elif '-' not in sites_str and sites_str.isdigit():
site1 = int(sites_str)
site2 = site1 + 1
else:
raise IllegalArgumentError(
f'sites must be of format: "start-end" or "site" .\nGot: {sites_str}'
)
# validate sites are in range:
if not self.genome.get_nr_sites() + 1 >= site2 >= site1 >= 1:
msg = 'sites violate the constraints: '
msg += f'{self.genome.get_nr_sites() + 1} >= {site2} > {site1} >= 1'
raise IllegalArgumentError(msg)
if site1 == site2:
site2 += 1
return site1, site2
def index2locus(self, index):
"""
translate CpG index to genomic locus. e.g, CpG1 -> (chr1, 10469)
:param index: a site index in range [1, NR_SITES]
:return: chromosome, locus
"""
index = int(index)
# validate input
if not self.genome.get_nr_sites() + 1 >= index >= 1:
eprint('Invalid site index:', index)
raise IllegalArgumentError('Out of range site index:', index)
# find chromosome:
chrom = index2chrom(index, self.genome)
# find locus:
cmd = f'tabix {self.genome.revdict_path} {chrom}:{index}-{index} | cut -f2'
try:
loc = int(
subprocess.check_output(cmd, shell=True).decode().strip())
except ValueError as e:
msg = f'Failed retrieving locus for site {index} with command:\n{cmd}\n{e}'
raise IllegalArgumentError(msg)
return chrom, loc
def __str__(self):
if self.sites is None:
return 'Whole genome'
s1, s2 = self.sites
nr_bp = np.diff(self.bp_tuple)[0] + 1
res = f'{self.region_str} - {nr_bp:,}bp, {s2 - s1:,}CpGs: {s1}-{s2}'
if self.annotation:
res += '\n' + self.annotation
return res
def is_whole(self):
""" True iff no filters (-r, -s) were applied.
i.e, this gr is the whole genome."""
return self.sites is None
class SegmentByChunks:
def __init__(self, args, betas):
self.betas = betas
max_cpg = min(args.max_cpg, args.max_bp // 2)
assert (max_cpg > 1)
self.genome = GenomeRefPaths(args.genome)
self.param_dict = {
'betas': betas,
'pcount': args.pcount,
'max_cpg': max_cpg,
'max_bp': args.max_bp,
'revdict': self.genome.revdict_path,
'genome': self.genome
}
self.args = args
def break_to_chunks(self):
""" Break range of sites to chunks of size 'step',
while keeping chromosomes separated """
# print a warning in case chunk size is too small
step = self.args.chunk_size
if step < self.args.max_cpg:
msg = '[wt segment] WARNING: chunk_size is small compared to max_cpg and/or max_bp.\n' \
' It may cause wt segment to fail. It\'s best setting\n' \
' chunk_size > min{max_cpg, max_bp/2}'
eprint(msg)
if self.args.bed_file:
df = load_blocks_file(self.args.bed_file)[['startCpG',
'endCpG']].dropna()
# make sure bed file has no overlaps or duplicated regions
is_nice, msg = is_block_file_nice(df)
if not is_nice:
msg = '[wt segment] ERROR: invalid bed file.\n' \
f' {msg}\n' \
f' Try: sort -k1,1 -k2,2n {self.args.bed_file} | ' \
'bedtools merge -i - | wgbstools convert --drop_empty -p -L -'
eprint(msg)
raise IllegalArgumentError('Invalid bed file')
if df.shape[0] > 2 * 1e4:
msg = '[wt segment] WARNING: bed file contains many regions.\n' \
' Segmentation will take a long time.\n' \
f' Consider running w/o -L flag and intersect the results\n'
eprint(msg)
else: # No bed file provided
gr = GenomicRegion(self.args)
# whole genome - make a dummy "bed file" of the full chromosomes
if gr.is_whole():
cf = self.genome.get_chrom_cpg_size_table()
cf['endCpG'] = np.cumsum(cf['size']) + 1
cf['startCpG'] = cf['endCpG'] - cf['size']
df = cf[['startCpG', 'endCpG']]
# one region
else:
df = pd.DataFrame(columns=['startCpG', 'endCpG'],
data=[gr.sites])
# build a DataFrame of chunks, with a "tag"/label field,
# so we know which chunks to merge later on.
rf = pd.DataFrame()
tags = []
starts = []
ends = []
for ind, row in df.iterrows():
start, end = row
bords = list(range(start, end, step)) + [end]
tags += [f'{start}-{end}'] * (len(bords) - 1)
starts += bords[:-1]
ends += bords[1:]
return tags, starts, ends
def run(self):
# break input region/s to small chunks
tags, starts, ends = self.break_to_chunks()
# segment each chunk separately in a single thread
p = Pool(self.args.threads)
params = [(dict(self.param_dict, **{'sites': (s, e)}), )
for s, e in zip(starts, ends)]
arr = p.starmap(segment_process, params)
p.close()
p.join()
# merge chunks from the same "tag" group
# (i.e. the same chromosome, or the same region of the provided bed file)
df = pd.DataFrame()
for tag in set(tags):
carr = [arr[i] for i in range(len(arr)) if tags[i] == tag]
merged = self.merge_df_list(carr)
df = pd.concat([
df,
pd.DataFrame({
'startCpG': merged[:-1],
'endCpG': merged[1:]
})
])
self.dump_result(df.reset_index(drop=True))
def merge_df_list(self, dflist):
# Given a set of chunks to merge, recursively pairwise stich them.
while len(dflist) > 1:
p = Pool(self.args.threads)
params = [(dflist[i - 1], dflist[i], self.param_dict)
for i in range(1, len(dflist), 2)]
arr = p.starmap(stitch_2_dfs, params)
p.close()
p.join()
last_df = [dflist[-1]] if len(dflist) % 2 else []
dflist = arr + last_df
return dflist[0]
def dump_result(self, df):
if df.empty:
eprint('Empty blocks array')
return
# sort by startCpG and filter by CpGs
nr_blocks = df.shape[0]
df.sort_values(by=['startCpG'], inplace=True)
df = df[df.endCpG - df.startCpG > self.args.min_cpg - 1].reset_index(
drop=True)
# verbose
nr_blocks_filt = df.shape[0]
nr_dropped = nr_blocks - nr_blocks_filt
eprint(f'[wt segment] found {nr_blocks_filt:,} blocks\n' \
f' (dropped {nr_dropped:,} short blocks)')
# add genomic loci and dump/print
temp_path = next(tempfile._get_candidate_names())
try:
df.to_csv(temp_path, sep='\t', header=None, index=None)
add_bed_to_cpgs(temp_path, self.genome.genome, self.args.out_path)
finally:
if op.isfile(temp_path):
os.remove(temp_path)
