def transform(self,func=np.log2):
''' Transform the samples table VALUES in place'''
if not self.is_raw():
log.warn("Attempting to perform transormation of apparently non raw data")
self.tbl.VALUE = func(list(map(float,self.tbl.VALUE.values)))
self.name = self.name
# make sure we didnt introduce and -Inf values
self.tbl.loc[self.tbl.VALUE == float('-Inf'),'VALUE'] = np.nan
return True
def create(cls,name,description,type='Camoco'):
'''
This is a class method to create a new camoco type object.
It initializes base directory hierarchy
'''
basedir = os.path.realpath(
os.path.expanduser(cf.get('options','basedir'))
)
# Create the basedir if not exists
try:
os.makedirs(basedir,exist_ok=True)
os.makedirs(os.path.join(basedir,"logs"),exist_ok=True)
os.makedirs(os.path.join(basedir,"databases"),exist_ok=True)
os.makedirs(os.path.join(basedir,"analyses"),exist_ok=True)
os.makedirs(os.path.join(basedir,"tmp"),exist_ok=True)
except Exception as e:
log(' Could not create files in {}',basedir)
raise
try:
# Create the base camoco database
lite.Connection(
os.path.join(basedir,'databases','Camoco.Camoco.db')
).cursor().execute('''
CREATE TABLE IF NOT EXISTS datasets (
name TEXT NOT NULL,
description TEXT,
type TEXT,
added datetime DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY(name,type)
);
INSERT OR IGNORE INTO datasets (name,description,type)
VALUES ('Camoco','Camoco base','Camoco');
INSERT OR FAIL INTO datasets (name,description,type)
VALUES (?,?,?)''',(name,description,type)
)
except ConstraintError as e:
log.warn('CAUTION! {}.{} Database already exists.',name,type)
self = cls(name)
return self
def create(cls, name, description, type='Camoco'):
'''
This is a class method to create a new camoco type object.
It initializes base directory hierarchy
'''
basedir = os.path.realpath(
os.path.expanduser(cf.get('options', 'basedir')))
# Create the basedir if not exists
try:
os.makedirs(basedir, exist_ok=True)
os.makedirs(os.path.join(basedir, "logs"), exist_ok=True)
os.makedirs(os.path.join(basedir, "databases"), exist_ok=True)
os.makedirs(os.path.join(basedir, "analyses"), exist_ok=True)
os.makedirs(os.path.join(basedir, "tmp"), exist_ok=True)
except Exception as e:
log(' Could not create files in {}', basedir)
raise
try:
# Create the base camoco database
lite.Connection(
os.path.join(basedir, 'databases',
'Camoco.Camoco.db')).cursor().execute(
'''
CREATE TABLE IF NOT EXISTS datasets (
name TEXT NOT NULL,
description TEXT,
type TEXT,
added datetime DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY(name,type)
);
INSERT OR IGNORE INTO datasets (name,description,type)
VALUES ('Camoco','Camoco base','Camoco');
INSERT OR FAIL INTO datasets (name,description,type)
VALUES (?,?,?)''', (name, description, type))
except ConstraintError as e:
log.warn('CAUTION! {}.{} Database already exists.', name, type)
self = cls(name)
return self
def snp2gene(args):
'''
Perform SNP (locus) to candidate gene mapping
'''
if args.out != sys.stdout:
# Create any non-existant directories
if os.path.dirname(args.out) != '':
os.makedirs(os.path.dirname(args.out),exist_ok=True)
if os.path.exists(args.out) and not args.force:
print(
"Output for {} exists! Skipping!".format(
args.out
),file=sys.stderr
)
return None
# Set a flag saying this is from a COB refgen
from_cob = False
# Create the refgen (option to create it from a COB)
if co.Tools.available_datasets('Expr',args.refgen):
refgen = co.COB(args.refgen).refgen
from_cob = args.refgen
elif co.Tools.available_datasets('RefGen',args.refgen):
refgen = co.RefGen(args.refgen)
# Create the GWAS object
ont = co.GWAS(args.gwas)
if 'all' in args.terms:
terms = ont.iter_terms()
else:
terms = [ont[term] for term in args.terms]
data = pd.DataFrame()
results = []
for term in terms:
for window_size in args.candidate_window_size:
for flank_limit in args.candidate_flank_limit:
if 'effective' in args.snp2gene:
# Map to effective
effective_loci = term.effective_loci(
window_size=window_size
)
elif 'strongest' in args.snp2gene:
effective_loci = term.strongest_loci(
window_size=window_size,
attr=args.strongest_attr,
lowest=args.strongest_higher
)
genes = pd.DataFrame([ x.as_dict() for x in
refgen.candidate_genes(
effective_loci,
flank_limit=flank_limit,
include_parent_locus=True,
include_num_siblings=True,
include_num_intervening=True,
include_rank_intervening=True,
include_SNP_distance=True,
include_parent_attrs=args.include_parent_attrs,
attrs={'Term':term.id},
)
])
genes['FlankLimit'] = flank_limit
genes['WindowSize'] = window_size
genes['RefGen'] = refgen.name
if from_cob != False:
genes['COB'] = from_cob
data = pd.concat([data,genes])
# Add data from gene info files
original_number_genes = len(data)
for info_file in args.gene_info:
log('Adding info for {}',info_file)
# Assume the file is a table
info = pd.read_table(info_file,sep='\t')
if len(info.columns) == 1:
info = pd.read_table(info_file,sep=',')
# try to match as many columns as possible
matching_columns = set(data.columns).intersection(info.columns)
log("Joining SNP2Gene mappings with info file on: {}",','.join(matching_columns))
data = pd.merge(data,info,how='left')
if len(data) != original_number_genes:
log.warn(
'There were multiple info rows for some genes. '
'Beware of potential duplicate candidate gene entries! '
)
# Generate the output file
data.to_csv(args.out,index=None,sep='\t')
log("Summary stats")
print('-'*100)
#print('With {}kb windows and up to {} flanking genes'.format(int(args.candidate_window_size/1000),args.candidate_flank_limit))
print("Mapped {} SNPs to {} genes".format(len(data.parent_locus.unique()),len(data.ID.unique())))
print("Number of candidate genes per term:")
print(data.groupby('Term').apply(lambda df: len(df.ID)))
def __add__(self,other):
if self.is_raw() and not other.is_raw():
log.warn('WARNING! attempting to combine {} and {} which are not both normalized')
super().__add__(self,other)
def snp2gene(args):
'''
Perform SNP (locus) to candidate gene mapping
'''
if args.out != sys.stdout:
# Create any non-existant directories
if os.path.dirname(args.out) != '':
os.makedirs(os.path.dirname(args.out),exist_ok=True)
if os.path.exists(args.out) and not args.force:
print(
"Output for {} exists! Skipping!".format(
args.out
),file=sys.stderr
)
return None
# Set a flag saying this is from a COB refgen
from_cob = False
# Create the refgen (option to create it from a COB)
if co.available_datasets('Expr',args.refgen):
refgen = co.COB(args.refgen).refgen
from_cob = args.refgen
elif co.available_datasets('RefGen',args.refgen):
refgen = co.RefGen(args.refgen)
# Create the GWAS object
ont = co.GWAS(args.gwas)
if 'all' in args.terms:
terms = ont.iter_terms()
else:
terms = [ont[term] for term in args.terms]
data = pd.DataFrame()
results = []
for term in terms:
for window_size in args.candidate_window_size:
for flank_limit in args.candidate_flank_limit:
if 'effective' in args.snp2gene:
# Map to effective
effective_loci = term.effective_loci(
window_size=window_size
)
elif 'strongest' in args.snp2gene:
effective_loci = term.strongest_loci(
window_size=window_size,
attr=args.strongest_attr,
lowest=args.strongest_higher
)
genes = pd.DataFrame([ x.as_dict() for x in
refgen.candidate_genes(
effective_loci,
flank_limit=flank_limit,
include_parent_locus=True,
include_num_siblings=True,
include_num_intervening=True,
include_rank_intervening=True,
include_SNP_distance=True,
include_parent_attrs=args.include_parent_attrs,
attrs={'Term':term.id},
)
])
genes['FlankLimit'] = flank_limit
genes['WindowSize'] = window_size
genes['RefGen'] = refgen.name
if from_cob != False:
genes['COB'] = from_cob
data = pd.concat([data,genes])
# Add data from gene info files
original_number_genes = len(data)
for info_file in args.gene_info:
log('Adding info for {}',info_file)
# Assume the file is a table
info = pd.read_table(info_file,sep='\t')
if len(info.columns) == 1:
info = pd.read_table(info_file,sep=',')
# try to match as many columns as possible
matching_columns = set(data.columns).intersection(info.columns)
log("Joining SNP2Gene mappings with info file on: {}",','.join(matching_columns))
data = pd.merge(data,info,how='left')
if len(data) != original_number_genes:
log.warn(
'There were multiple info rows for some genes. '
'Beware of potential duplicate candidate gene entries! '
)
# Generate the output file
data.to_csv(args.out,index=None,sep='\t')
log("Summary stats")
print('-'*100)
#print('With {}kb windows and up to {} flanking genes'.format(int(args.candidate_window_size/1000),args.candidate_flank_limit))
print("Mapped {} SNPs to {} genes".format(len(data.parent_locus.unique()),len(data.ID.unique())))
print("Number of candidate genes per term:")
print(data.groupby('Term').apply(lambda df: len(df.ID)))
