def create_mapping_dict(self, filename, key_col=3, value_col=4):
"""Return a mapping dictionary for the provided file.
This returns a dictionary for use in mapping nodes or edge types from
the file specified by filetype. By default it opens the file specified
by filename creates a dictionary using the first column as the key and
the second column as the value.
Args:
filename(str): The name of the file containing the information
needed to produce the maping dictionary.
Returns:
dict: A dictionary for use in mapping nodes or edge types.
"""
term_map = dict()
n_type = 'Property'
n_meta_file = filename.replace('raw_line', 'node_meta')
node_file = filename.replace('raw_line', 'node')
orig_id, kn_id, orig_name, kn_name = ['', '', '', '']
skip = True
with open(filename) as infile, \
open(n_meta_file, 'w') as n_meta, \
open(node_file, 'w') as nfile:
reader = csv.reader(infile, delimiter='\t')
n_meta_writer = csv.writer(n_meta, delimiter='\t', lineterminator='\n')
n_writer = csv.writer(nfile, delimiter='\t', lineterminator='\n')
for line in reader:
raw = line[3]
if raw.startswith('[Term]'):
skip = False
orig_id, kn_id, orig_name, kn_name = ['', '', '', '']
continue
if raw.startswith('[Typedef]'):
skip = True
continue
if skip:
continue
if raw.startswith('id: '):
orig_id = raw[4:].strip()
kn_id = cf.pretty_name(orig_id)
continue
if raw.startswith('name: '):
orig_name = raw[6:].strip()
kn_name = cf.pretty_name('go_' + orig_name)
term_map[orig_id] = kn_id + '::' + kn_name
n_writer.writerow([kn_id, kn_name, n_type])
n_meta_writer.writerow([kn_id, 'orig_desc', orig_name])
n_meta_writer.writerow([kn_id, 'orig_id', orig_id])
if raw.startswith('alt_id: '):
alt_id = raw[8:].strip()
term_map[alt_id] = kn_id + '::' + kn_name
n_meta_writer.writerow([kn_id, 'alt_alias', alt_id])
outfile = node_file.replace('node', 'unique.node')
tu.csu(node_file, outfile)
outfile = n_meta_file.replace('node_meta', 'unique.node_meta')
tu.csu(n_meta_file, outfile)
return term_map
def create_mapping_dict(self, filename, key_col=3, value_col=4):
"""Return a mapping dictionary for the provided file.
This returns a dictionary for use in mapping nodes or edge types from
the file specified by filetype. By default it opens the file specified
by filename creates a dictionary using the key_col column as the key
and the value_col column as the value.
Args:
filename (str): The name of the file containing the information
needed to produce the maping dictionary.
key_col (int): The column containing the key for creating the
dictionary. By default this is column 3.
value_col (int): The column containing the value for creating the
dictionary. By default this is column 4.
Returns:
dict: A dictionary for use in mapping nodes or edge types.
"""
src = filename.split('.')[0]
alias = filename.split('.')[1]
map_dict = dict()
n_meta_file = filename.replace('raw_line', 'node_meta')
node_file = filename.replace('raw_line', 'node')
if not self.is_map(alias):
return map_dict
with open(filename, 'rb') as map_file, \
open(n_meta_file, 'w') as n_meta, \
open(node_file, 'w') as nfile:
reader = csv.reader((line.decode('utf-8') for line in map_file),
delimiter='\t')
n_meta_writer = csv.writer(n_meta,
delimiter='\t',
lineterminator='\n')
n_writer = csv.writer(nfile, delimiter='\t', lineterminator='\n')
for line in reader:
chksm = line[2]
orig_id = line[key_col].strip()
orig_name = line[value_col].strip()
kn_id = cf.pretty_name(orig_id)
kn_name = cf.pretty_name(src + '_' + orig_name)
map_dict[orig_id] = kn_id + '::' + kn_name
n_writer.writerow([kn_id, kn_name])
n_meta_writer.writerow([kn_id, 'orig_desc', orig_name])
n_meta_writer.writerow([kn_id, 'orig_id', orig_id])
outfile = node_file.replace('node', 'unique.node')
tu.csu(node_file, outfile)
outfile = n_meta_file.replace('node_meta', 'unique.node_meta')
tu.csu(n_meta_file, outfile)
return map_dict
def table(self, raw_line, version_dict):
"""Uses the provided raw_line file to produce a 2table_edge file, an
edge_meta file, a node and/or node_meta file (only for property nodes).
This returns noting but produces the table formatted files from the
provided raw_line file:
raw_line (line_hash, line_num, file_id, raw_line)
table_file (line_hash, n1name, n1hint, n1type, n1spec,
n2name, n2hint, n2type, n2spec, et_hint, score,
table_hash)
edge_meta (line_hash, info_type, info_desc)
node_meta (node_id,
info_type (evidence, relationship, experiment, or link),
info_desc (text))
node (node_id, n_alias, n_type)
Args:
raw_line(str): The path to the raw_line file
version_dict (dict): A dictionary describing the attributes of the
alias for a source.
Returns:
"""
#outfiles
table_file = raw_line.replace('raw_line', 'table')
n_meta_file = raw_line.replace('raw_line', 'node_meta')
node_file = raw_line.replace('raw_line', 'node')
#e_meta_file = raw_line.replace('raw_line', 'edge_meta')
#static column values
n1type = 'property'
n_type = 'Property'
n2type = 'gene'
n1hint = 'Pfam/Family'
n2hint = 'Uniprot_gn'
et_hint = 'pfam_prot'
n1spec = '0'
map_dict = dict()
src = 'pf'
###Map the file name
species = (os.path.join('..', '..', 'id_map', 'species',
'species.json'))
with open(species) as infile:
species_map = json.load(infile)
n2spec = version_dict['alias']
with open(raw_line, encoding='utf-8') as infile, \
open(table_file, 'w') as edges, \
open(n_meta_file, 'w') as n_meta, \
open(node_file, 'w') as nfile:
n_meta_writer = csv.writer(n_meta,
delimiter='\t',
lineterminator='\n')
n_writer = csv.writer(nfile, delimiter='\t', lineterminator='\n')
edge_writer = csv.writer(edges,
delimiter='\t',
lineterminator='\n')
for line in infile:
line = line.replace('"', '').strip().split()
if len(line) == 1:
continue
chksm = line[0]
raw = line[3:]
# skip commented lines
comment_match = re.match('#', raw[0])
if comment_match is not None:
continue
orig_id = raw[5].strip()
orig_name = raw[6].strip()
kn_id = cf.pretty_name(src + '_' + orig_id)
kn_name = cf.pretty_name(src + '_' + orig_name)
map_dict[orig_id] = kn_id + '::' + kn_name
n_writer.writerow([kn_id, kn_name, n_type])
n_meta_writer.writerow([kn_id, 'orig_desc', orig_name])
n_meta_writer.writerow([kn_id, 'orig_id', orig_id])
n2orig = raw[0]
evalue = raw[12]
evalue = float(evalue)
score = self.sc_min
if evalue == 0.0:
score = self.sc_max
if evalue > 0.0:
score = round(-1.0 * math.log10(evalue), 4)
if score > self.sc_max:
score = self.sc_max
if score < self.sc_min:
continue
output = [
chksm, kn_id, n1hint, n1type, n1spec, n2orig, n2hint,
n2type, n2spec, et_hint,
str(score)
]
hasher = hashlib.md5()
hasher.update('\t'.join(output).encode())
t_chksum = hasher.hexdigest()
edge_writer.writerow(output + [t_chksum])
outfile = node_file.replace('node', 'unique.node')
tu.csu(node_file, outfile)
outfile = n_meta_file.replace('node_meta', 'unique.node_meta')
tu.csu(n_meta_file, outfile)
def create_mapping_dict(self, filename, key_col=3, value_col=4):
"""Return a mapping dictionary for the provided file.
This returns a dictionary for use in mapping nodes or edge types from
the file specified by filetype. By default it opens the file specified
by filename creates a dictionary using the first column as the key and
the second column as the value.
Args:
filename(str): The name of the file containing the information
needed to produce the maping dictionary.
Returns:
dict: A dictionary for use in mapping nodes or edge types.
"""
src = filename.split('.')[0]
alias = filename.split('.')[1]
map_dict = dict()
n1_type = 'Property'
n_meta_file = filename.replace('raw_line', 'node_meta')
node_file = filename.replace('raw_line', 'node')
if not self.is_map(alias):
return map_dict
if alias == 'pathway':
with open(filename, 'rb') as map_file, \
open(n_meta_file, 'w') as n_meta, \
open(node_file, 'w') as nfile:
reader = csv.reader(
(line.decode('utf-8') for line in map_file),
delimiter='\t')
n_meta_writer = csv.writer(n_meta,
delimiter='\t',
lineterminator='\n')
n_writer = csv.writer(nfile,
delimiter='\t',
lineterminator='\n')
for line in reader:
orig_id = line[3].strip()
orig_name = line[4].strip()
mod_id = src + '_' + orig_id.replace('map', '')
kn_id = cf.pretty_name(mod_id)
kn_name = cf.pretty_name(src + '_' + orig_name)
map_dict[orig_id] = kn_id + '::' + kn_name
n_writer.writerow([kn_id, kn_name, n1_type])
n_meta_writer.writerow([kn_id, 'orig_desc', orig_name])
n_meta_writer.writerow([kn_id, 'orig_id', orig_id])
outfile = node_file.replace('node', 'unique.node')
tu.csu(node_file, outfile)
outfile = n_meta_file.replace('node_meta', 'unique.node_meta')
tu.csu(n_meta_file, outfile)
else:
with open(filename, 'rb') as map_file:
reader = csv.reader(
(line.decode('utf-8') for line in map_file),
delimiter='\t')
for line in reader:
orig_id = line[3].strip()
orig_name = line[4].strip()
mod_id = src + '_' + orig_id
kn_id = orig_name.split(':')[1]
kn_name = 'EntrezGene'
map_dict[mod_id] = kn_id + '::' + kn_name
return map_dict
def table(self, raw_line, version_dict):
"""Uses the provided raw_line file to produce a 2table_edge file, an
edge_meta file, a node and/or node_meta file (only for property nodes).
This returns noting but produces the table formatted files from the
provided raw_line file:
raw_line (line_hash, line_num, file_id, raw_line)
table_file (line_hash, n1name, n1hint, n1type, n1spec,
n2name, n2hint, n2type, n2spec, et_hint, score,
table_hash)
edge_meta (line_hash, info_type, info_desc)
node_meta (node_id,
info_type (evidence, relationship, experiment, or link),
info_desc (text))
node (node_id, n_alias, n_type)
Args:
raw_line(str): The path to the raw_line file
version_dict (dict): A dictionary describing the attributes of the
alias for a source.
Returns:
"""
#outfiles
table_file = raw_line.replace('raw_line', 'table')
n_meta_file = raw_line.replace('raw_line', 'node_meta')
node_file = raw_line.replace('raw_line', 'node')
e_meta_file = raw_line.replace('raw_line', 'edge_meta')
#static column values
n1type = 'gene' #ignoring chemicals
n1hint = 'UNIPROT_GN'
n1spec = 'unknown'
n2type = n1type #ignoring chemicals
n2hint = n1hint
n2spec = n1spec
n3_type = 'property'
n3hint = 'unknown'
n3spec = 'unknown'
score = '1'
n_type = 'Property'
with open(raw_line, encoding='utf-8') as infile, \
open(table_file, 'w') as edges,\
open(e_meta_file, 'w') as e_meta, \
open(n_meta_file, 'w') as n_meta, \
open(node_file, 'w') as nfile:
edge_writer = csv.writer(edges, delimiter='\t', lineterminator='\n')
e_meta_writer = csv.writer(e_meta, delimiter='\t', lineterminator='\n')
n_meta_writer = csv.writer(n_meta, delimiter='\t', lineterminator='\n')
n_writer = csv.writer(nfile, delimiter='\t', lineterminator='\n')
for line in infile:
line = line.replace('"', '').strip().split('\t')
if line[1] == '1': #skip header
continue
chksm = line[0]
raw = line[3:]
if len(raw) != 7: #extended information
continue
(n1id, et_hint, n2id, src, publist, n3id, mediator_ids) = raw
et_hint = 'pathcom_' + et_hint.replace('-', '_')
#n1-n2 edge
hasher = hashlib.md5()
hasher.update('\t'.join([chksm, n1id, n1hint, n1type, n1spec,
n2id, n2hint, n2type, n2spec, et_hint,
score]).encode())
t_chksum = hasher.hexdigest()
edge_writer.writerow([chksm, n1id, n1hint, n1type, n1spec,
n2id, n2hint, n2type, n2spec, et_hint,
score, t_chksum])
e_meta_writer.writerow([chksm, 'original_source', src])
if publist:
e_meta_writer.writerow([chksm, 'reference', publist])
#pathway edge
if n3id:
kn_n3id = cf.pretty_name('paco_' + n3id)
n_writer.writerow([kn_n3id, kn_n3id, n_type])
n_meta_writer.writerow([kn_n3id, 'orig_id', n3id])
for node in [n1id, n2id]:
hasher = hashlib.md5()
hasher.update('\t'.join([chksm, kn_n3id, n3hint, n3_type,
n3spec, node, n1hint, n1type, n1spec,
'pathcom_pathway', score]).encode())
t_chksum = hasher.hexdigest()
edge_writer.writerow([chksm, kn_n3id, n3hint, n3_type,
n3spec, node, n1hint, n1type, n1spec,
'pathcom_pathway', score, t_chksum])
outfile = e_meta_file.replace('edge_meta', 'unique.edge_meta')
tu.csu(e_meta_file, outfile, [1, 2, 3])
outfile = node_file.replace('node', 'unique.node')
tu.csu(node_file, outfile)
outfile = n_meta_file.replace('node_meta', 'unique.node_meta')
tu.csu(n_meta_file, outfile)
def table(self, raw_line, version_dict):
"""Uses the provided raw_line file to produce a 2table_edge file, an
edge_meta file, a node and/or node_meta file (only for property nodes).
This returns noting but produces the table formatted files from the
provided raw_line file:
raw_line (line_hash, line_num, file_id, raw_line)
table_file (line_hash, n1name, n1hint, n1type, n1spec,
n2name, n2hint, n2type, n2spec, et_hint, score,
table_hash)
edge_meta (line_hash, info_type, info_desc)
node_meta (node_id,
info_type (evidence, relationship, experiment, or link),
info_desc (text))
node (node_id, n_alias, n_type)
Args:
raw_line(str): The path to the raw_line file
version_dict (dict): A dictionary describing the attributes of the
alias for a source.
Returns:
"""
#outfiles
table_file = raw_line.replace('raw_line', 'table')
n_meta_file = raw_line.replace('raw_line', 'node_meta')
node_file = raw_line.replace('raw_line', 'node')
#static column values
alias = version_dict['alias']
source = version_dict['source']
mouse_aliases = ["MGI_Mammalian_Phenotype_2013", \
"MGI_Mammalian_Phenotype_Level_3",\
"MGI_Mammalian_Phenotype_Level_4", "Mouse_Gene_Atlas"]
n1type = 'property'
n_type = 'Property'
n1spec = '0'
n1hint = source + '_' + alias
n2type = 'gene'
if alias in mouse_aliases:
n2spec = '10090'
n2hint = 'MGI'
else:
n2spec = '9606'
n2hint = 'HGNC'
(et_hint, node_prefix) = self.aliases[alias].split('::')
score = 1
if alias == 'PPI_Hub_Proteins':
n1type = 'gene'
n1spec = '9606'
n1hint = 'HGNC'
with open(raw_line, encoding='utf-8') as infile, \
open(table_file, 'w') as edges,\
open(n_meta_file, 'w') as n_meta, \
open(node_file, 'w') as nfile:
edge_writer = csv.writer(edges,
delimiter='\t',
lineterminator='\n')
n_meta_writer = csv.writer(n_meta,
delimiter='\t',
lineterminator='\n')
n_writer = csv.writer(nfile, delimiter='\t', lineterminator='\n')
for line in infile:
line = line.replace('"', '').strip().split('\t')
#line = re.split('\s{2,}', line)
if len(line) == 1:
continue
chksm = line[0]
raw = line[3:]
n1_orig_name = raw[0]
n1_kn_name = n1_orig_name
if alias != 'PPI_Hub_Proteins':
n1_kn_name = cf.pretty_name(node_prefix + '_' +
n1_orig_name)
n_meta_writer.writerow(
[n1_kn_name, 'orig_desc', n1_orig_name])
n_writer.writerow([n1_kn_name, n1_kn_name, n_type])
for n2_id in raw[1:]:
n2_id = n2_id.split(',')[0]
if n2_id == '':
continue
hasher = hashlib.md5()
hasher.update('\t'.join([chksm, n1_kn_name, n1hint, n1type, n1spec,\
n2_id, n2hint, n2type, n2spec, et_hint,\
str(score)]).encode())
t_chksum = hasher.hexdigest()
edge_writer.writerow([chksm, n1_kn_name, n1hint, n1type, n1spec, \
n2_id, n2hint, n2type, n2spec, et_hint, score, \
t_chksum])
if alias != 'PPI_Hub_Proteins':
outfile = n_meta_file.replace('node_meta', 'unique.node_meta')
tu.csu(n_meta_file, outfile)
outfile = node_file.replace('node', 'unique.node')
tu.csu(node_file, outfile)
else:
os.remove(n_meta_file)
os.remove(node_file)
def table(self, raw_line, version_dict):
"""Uses the provided raw_line file to produce a 2table_edge file, an
edge_meta file, a node and/or node_meta file (only for property nodes).
This returns noting but produces the table formatted files from the
provided raw_line file:
raw_line (line_hash, line_num, file_id, raw_line)
table_file (line_hash, n1name, n1hint, n1type, n1spec,
n2name, n2hint, n2type, n2spec, et_hint, score,
table_hash)
edge_meta (line_hash, info_type, info_desc)
node_meta (node_id,
info_type (evidence, relationship, experiment, or link),
info_desc (text))
node (node_id, n_alias, n_type)
Args:
raw_line(str): The path to the raw_line file
version_dict (dict): A dictionary describing the attributes of the
alias for a source.
Returns:
"""
#outfiles
table_file = raw_line.replace('raw_line', 'table')
n_meta_file = raw_line.replace('raw_line', 'node_meta')
node_file = raw_line.replace('raw_line', 'node')
#e_meta_file = raw_line.replace('raw_line','edge_meta')
#static column values
alias = version_dict['alias']
source = version_dict['source']
n1type = 'property'
n_type = 'Property'
n1spec = '0'
n1hint = source + '_' + alias
n2type = 'gene'
n2spec = '9606' # assumption of human genes is occasionally incorrect
n2hint = 'EntrezGene'
et_hint = source + '_' + alias.replace(".", "_")
score = 1
with open(raw_line, encoding='utf-8') as infile, \
open(table_file, 'w') as edges,\
open(n_meta_file, 'w') as n_meta, \
open(node_file, 'w') as nfile:
edge_writer = csv.writer(edges,
delimiter='\t',
lineterminator='\n')
n_meta_writer = csv.writer(n_meta,
delimiter='\t',
lineterminator='\n')
n_writer = csv.writer(nfile, delimiter='\t', lineterminator='\n')
for line in infile:
line = line.replace('"', '').strip().split('\t')
if len(line) == 1:
continue
chksm = line[0]
raw = line[3:]
n1_orig_name = raw[0]
n1_url = raw[1]
hasher = hashlib.md5()
hasher.update(n1_orig_name.encode())
n1_chksum = hasher.hexdigest()
n1_kn_id = cf.pretty_name('msig_' + n1_chksum)
n1_kn_name = cf.pretty_name('msig_' + n1_orig_name)
n1hint = n1_kn_name
n_meta_writer.writerow([n1_kn_id, 'orig_desc', n1_orig_name])
n_meta_writer.writerow([n1_kn_id, 'link', n1_url])
n_writer.writerow([n1_kn_id, n1_kn_name, n_type])
for n2_id in raw[2:]:
hasher = hashlib.md5()
hasher.update('\t'.join([chksm, n1_kn_id, n1hint, n1type, n1spec,\
n2_id, n2hint, n2type, n2spec, et_hint,\
str(score)]).encode())
t_chksum = hasher.hexdigest()
edge_writer.writerow([chksm, n1_kn_id, n1hint, n1type, n1spec, \
n2_id, n2hint, n2type, n2spec, et_hint, score, \
t_chksum])
outfile = n_meta_file.replace('node_meta', 'unique.node_meta')
tu.csu(n_meta_file, outfile)
outfile = node_file.replace('node', 'unique.node')
tu.csu(node_file, outfile)