def gene():
FILE = get_source(meta_id, 1)
data = os.path.join(dataDir, FILE)
df = pd.read_csv(data, sep="\t")
# add column names
col_names = [
"chr",
"type",
"name",
"description",
"biomart_source",
"ensembl_id",
"start",
"end",
]
df.columns = col_names
df.drop_duplicates(inplace=True)
create_import(df=df, meta_id=meta_id)
# create constraints
constraintCommands = [
"CREATE CONSTRAINT ON (g:Gene) ASSERT g.ensembl_id IS UNIQUE",
"CREATE INDEX ON :Gene(name)",
"CREATE INDEX ON :Gene(chr)",
]
create_constraints(constraintCommands, meta_id)
def process():
# select the file
FILE = get_source(meta_id, 1)
logger.info("Reading {}", FILE)
df = pd.read_csv(os.path.join(dataDir, FILE))
# logger.info(df.columns)
logger.info(df.shape)
# drop some columns
df.drop(["access", "priority", "coverage", ""],
axis=1,
inplace=True,
errors="ignore")
logger.info(df.shape)
# create the csv and import data
create_import(df=df, meta_id=meta_id)
# create constraints
constraintCommands = [
"CREATE CONSTRAINT ON (g:Gwas) ASSERT g.id IS UNIQUE",
"CREATE index on :Gwas(trait)",
"CREATE index on :Gwas(filename)",
]
create_constraints(constraintCommands, meta_id)
def protein():
FILE = get_source(meta_id, 1)
data = os.path.join(dataDir, FILE)
df = pd.read_csv(data, sep="\t")
df.columns = ["uniprot_id"]
df["name"] = df["uniprot_id"]
create_import(df=df, meta_id=meta_id)
constraintCommands = [
"CREATE CONSTRAINT ON (p:Protein) ASSERT p.uniprot_id IS UNIQUE",
]
create_constraints(constraintCommands, meta_id)
def process():
FILE = get_source(meta_id, 1)
df = pd.read_csv(os.path.join(dataDir, FILE), low_memory=False)
df = df[["rsid"]].drop_duplicates()
# change column name to match schema
df.rename(columns={"rsid": "name"}, inplace=True)
create_import(df=df, meta_id=meta_id)
# create constraints
constraintCommands = [
"CREATE CONSTRAINT ON (v:Variant) ASSERT v.name IS UNIQUE;",
]
create_constraints(constraintCommands, meta_id)
from workflow.scripts.utils.writers import (
create_constraints,
create_import,
)
# setup
args, dataDir = setup()
meta_id = args.name
# args = the argparse arguments (name and data)
# dataDir = the path to the working directory for this node/rel
#######################################################################
FILE = get_source(meta_id,1)
def process():
# select the file
logger.info("Reading {}", FILE)
df = pd.read_csv(os.path.join(dataDir, FILE))
# logger.info(df.columns)
logger.info(df.shape)
# drop some columns
df.drop(
["access", "priority", "coverage", "doi", "group_name", "imputation_panel", "ontology", "study_design", "covariates",""], axis=1, inplace=True, errors="ignore"
)
logger.info(df.shape)
# create the csv and import data
from workflow.scripts.utils.writers import (
create_constraints,
create_import,
)
# setup
args, dataDir = setup()
meta_id = args.name
# args = the argparse arguments (name and data)
# dataDir = the path to the working directory for this node/rel
#######################################################################
BIO_DATA = get_source(meta_id, 1)
BIO_SEM = get_source(meta_id, 2)
def merge_data(lit_data, sem_data):
# load predicate data
logger.info("loading data...")
data_df = pd.read_csv(os.path.join(dataDir, lit_data),
sep=",",
compression="gzip")
logger.info("\n{}", data_df)
logger.info("loading semrep data...")
sem_df = pd.read_csv(os.path.join(dataDir, sem_data),
sep=",",
compression="gzip")
from workflow.scripts.utils.writers import (
create_constraints,
create_import,
)
# setup
args, dataDir = setup()
meta_id = args.name
# args = the argparse arguments (name and data)
# dataDir = the path to the working directory for this node/rel
#######################################################################
FILE1 = get_source(meta_id, 1)
FILE2 = get_source(meta_id, 2)
def process():
df1 = pd.read_csv(os.path.join(dataDir, FILE1), sep=" ")
# filter by score
df1 = df1[df1["combined_score"] >= 700]
logger.info(df1.shape)
logger.info("\n {}", df1.head())
df2 = pd.read_csv(os.path.join(dataDir, FILE2), sep="\t")
df2.columns = ["species", "uniprot", "protein", "x", "y"]
df2["uniprot"] = df2["uniprot"].str.split("|", expand=True)[0]
logger.info(df2.shape)
logger.info("\n {}", df2.head())
from workflow.scripts.utils.writers import (
create_constraints,
create_import,
)
# setup
args, dataDir = setup()
meta_id = args.name
# args = the argparse arguments (name and data)
# dataDir = the path to the working directory for this node/rel
#######################################################################
PREDICATION_FILE = get_source(meta_id, 1)
def process():
# load predicate data
logger.info("loading data...")
df = pd.read_csv(os.path.join(dataDir, PREDICATION_FILE),
sep=",",
compression="gzip")
logger.info(df.shape)
# need to split subject and object ids by |
df = (df.assign(subject_id=df.subject_id.str.split("|")).explode(
"subject_id").reset_index(drop=True))
logger.info(df.shape)
df = (df.assign(object_id=df.object_id.str.split("|")).explode(
from workflow.scripts.utils.writers import (
create_constraints,
create_import,
)
# setup
args, dataDir = setup()
meta_id = args.name
# args = the argparse arguements (name and data)
# dataDir = the path to the working directory for this node/rel
#######################################################################
vep_data = get_source(meta_id, 1)
def process_data():
logger.info("Processing vep data {}", vep_data)
col_names = [
"source",
"location",
"allele",
"target",
"feature",
"feature_type",
"consequence",
"cdna_position",
"cds_position",
"protein_position",
from workflow.scripts.utils.writers import (
create_constraints,
create_import,
)
# setup
args, dataDir = setup()
meta_id = args.name
# args = the argparse arguments (name and data)
# dataDir = the path to the working directory for this node/rel
#######################################################################
MED_DATA = get_source(meta_id, 1)
MED_SEM = get_source(meta_id, 2)
def merge_data(lit_data, sem_data):
# load predicate data
logger.info("loading data...")
data_df = pd.read_csv(os.path.join(dataDir, lit_data),
sep=",",
compression="gzip")
logger.info("\n{}", data_df)
logger.info("loading semrep data...")
sem_df = pd.read_csv(os.path.join(dataDir, sem_data),
sep=",",
compression="gzip")
from workflow.scripts.utils.writers import (
create_constraints,
create_import,
)
# setup
args, dataDir = setup()
meta_id = args.name
# args = the argparse arguments (name and data)
# dataDir = the path to the working directory for this node/rel
#######################################################################
SEM = get_source(meta_id, 1)
def make_id(row, sub_type):
id_val = row[sub_type + '_id']
if pd.isna(row[sub_type + '_id']):
id_val = row[sub_type + '_gene_id']
return id_val
def process():
logger.info("loading semrep data...{}", SEM)
sem_df = pd.read_csv(os.path.join(dataDir, SEM),
sep=",",
compression="gzip")