def import_study_data(jvm_args, meta_filename, data_filename):
args = jvm_args.split(' ')
meta_file_dict, meta_file_type = cbioportal_common.parse_metadata_file(
meta_filename, logger=LOGGER)
if meta_file_type is None:
# invalid file, skip
return
if not data_filename.endswith(meta_file_dict['data_filename']):
print >> ERROR_FILE, ("'data_filename' in meta file contradicts "
"data filename in command, skipping file")
return
importer = IMPORTER_CLASSNAME_BY_META_TYPE[meta_file_type]
args.append(importer)
if IMPORTER_REQUIRES_METADATA[importer]:
args.append("--meta")
args.append(meta_filename)
args.append("--loadMode")
args.append("bulkload")
if importer in ("org.mskcc.cbio.portal.scripts.ImportMutSigData", "org.mskcc.cbio.portal.scripts.ImportGisticData"):
args.append("--data")
args.append(data_filename)
args.append("--study")
args.append(meta_file_dict['cancer_study_identifier'])
else:
args.append("--data")
args.append(data_filename)
args.append("--noprogress") # don't report memory usage and % progress
run_java(*args)
def import_study_data(jvm_args, meta_filename, data_filename):
args = jvm_args.split(' ')
meta_file_dict, meta_file_type = cbioportal_common.parse_metadata_file(
meta_filename, logger=LOGGER)
if meta_file_type is None:
# invalid file, skip
return
if not data_filename.endswith(meta_file_dict['data_filename']):
print >> ERROR_FILE, ("'data_filename' in meta file contradicts "
"data filename in command, skipping file")
return
importer = IMPORTER_CLASSNAME_BY_META_TYPE[meta_file_type]
args.append(importer)
if IMPORTER_REQUIRES_METADATA[importer]:
args.append("--meta")
args.append(meta_filename)
args.append("--loadMode")
args.append("bulkload")
if importer in ("org.mskcc.cbio.portal.scripts.ImportMutSigData",
"org.mskcc.cbio.portal.scripts.ImportGisticData"):
args.append("--data")
args.append(data_filename)
args.append("--study")
args.append(meta_file_dict['cancer_study_identifier'])
else:
args.append("--data")
args.append(data_filename)
args.append("--noprogress") # don't report memory usage and % progress
run_java(*args)
def remove_study(jvm_args, meta_filename):
args = jvm_args.split(" ")
args.append(REMOVE_STUDY_CLASS)
meta_dict, meta_type = cbioportal_common.parse_metadata_file(meta_filename, logger=LOGGER)
if meta_type != MetaFileTypes.STUDY:
# invalid file, skip
print >> ERROR_FILE, "Not a study meta file: " + meta_filename
return
args.append(meta_dict["cancer_study_identifier"])
args.append("--noprogress") # don't report memory usage and % progress
run_java(*args)
def remove_study(jvm_args, meta_filename):
args = jvm_args.split(' ')
args.append(REMOVE_STUDY_CLASS)
meta_dictionary = cbioportal_common.parse_metadata_file(meta_filename,
logger=LOGGER)
if meta_dictionary['meta_file_type'] != MetaFileTypes.STUDY:
# invalid file, skip
print >> ERROR_FILE, 'Not a study meta file: ' + meta_filename
return
args.append(meta_dictionary['cancer_study_identifier'])
args.append("--noprogress") # don't report memory usage and % progress
run_java(*args)
def import_study_data(jvm_args,
meta_filename,
data_filename,
meta_file_dictionary=None):
args = jvm_args.split(' ')
# In case the meta file is already parsed in a previous function, it is not
# necessary to parse it again
if meta_file_dictionary is None:
meta_file_dictionary = cbioportal_common.parse_metadata_file(
meta_filename, logger=LOGGER)
# Retrieve meta file type
meta_file_type = meta_file_dictionary['meta_file_type']
# invalid file, skip
if meta_file_type is None:
print >> ERROR_FILE, (
"Unrecognized meta file type '%s', skipping file" %
(meta_file_type))
return
if not data_filename.endswith(meta_file_dictionary['data_filename']):
print >> ERROR_FILE, ("'data_filename' in meta file contradicts "
"data filename in command, skipping file")
return
importer = IMPORTER_CLASSNAME_BY_META_TYPE[meta_file_type]
args.append(importer)
if IMPORTER_REQUIRES_METADATA[importer]:
args.append("--meta")
args.append(meta_filename)
args.append("--loadMode")
args.append("bulkload")
if importer in ("org.mskcc.cbio.portal.scripts.ImportMutSigData",
"org.mskcc.cbio.portal.scripts.ImportGisticData"):
args.append("--data")
args.append(data_filename)
args.append("--study")
args.append(meta_file_dictionary['cancer_study_identifier'])
elif importer == "org.mskcc.cbio.portal.scripts.ImportGenePanelProfileMap":
args.append("--meta")
args.append(meta_filename)
args.append("--data")
args.append(data_filename)
else:
args.append("--data")
args.append(data_filename)
args.append("--noprogress") # don't report memory usage and % progress
run_java(*args)
def import_study_data(jvm_args, meta_filename, data_filename, meta_file_dictionary = None):
args = jvm_args.split(' ')
# In case the meta file is already parsed in a previous function, it is not
# necessary to parse it again
if meta_file_dictionary is None:
meta_file_dictionary = cbioportal_common.parse_metadata_file(
meta_filename, logger=LOGGER)
# Retrieve meta file type
meta_file_type = meta_file_dictionary['meta_file_type']
# invalid file, skip
if meta_file_type is None:
print >> ERROR_FILE, ("Unrecognized meta file type '%s', skipping file"
% (meta_file_type))
return
if not data_filename.endswith(meta_file_dictionary['data_filename']):
print >> ERROR_FILE, ("'data_filename' in meta file contradicts "
"data filename in command, skipping file")
return
importer = IMPORTER_CLASSNAME_BY_META_TYPE[meta_file_type]
args.append(importer)
if IMPORTER_REQUIRES_METADATA[importer]:
args.append("--meta")
args.append(meta_filename)
args.append("--loadMode")
args.append("bulkload")
if importer in ("org.mskcc.cbio.portal.scripts.ImportMutSigData", "org.mskcc.cbio.portal.scripts.ImportGisticData"):
args.append("--data")
args.append(data_filename)
args.append("--study")
args.append(meta_file_dictionary['cancer_study_identifier'])
elif importer == "org.mskcc.cbio.portal.scripts.ImportGenePanelProfileMap":
args.append("--meta")
args.append(meta_filename)
args.append("--data")
args.append(data_filename)
else:
args.append("--data")
args.append(data_filename)
args.append("--noprogress") # don't report memory usage and % progress
run_java(*args)
def process_directory(jvm_args, study_directory):
"""Import an entire study directory based on meta files found."""
meta_filenames = (
os.path.join(study_directory, f) for
f in os.listdir(study_directory) if
re.search(r'(\b|_)meta(\b|[_0-9])', f,
flags=re.IGNORECASE) and
not (f.startswith('.') or f.endswith('~')))
study_id = None
study_metafile = None
study_metadata = None
cancer_type_filepairs = []
sample_attr_filepair = None
regular_filepairs = []
# read all meta files (excluding case lists) to determine what to import
for f in meta_filenames:
# parse meta file
metadata, meta_file_type = cbioportal_common.parse_metadata_file(
f, study_id=study_id, logger=LOGGER)
if meta_file_type is None:
# invalid meta file, let's die
raise RuntimeError('Invalid meta file: ' + f)
# remember study id to give an error in case any other file is referencing a different one
if study_id is None and 'cancer_study_identifier' in metadata:
study_id = metadata['cancer_study_identifier']
if meta_file_type == MetaFileTypes.CANCER_TYPE:
cancer_type_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.STUDY:
if study_metafile is not None:
raise RuntimeError(
'Multiple meta_study files found: {} and {}'.format(
study_metafile, f))
study_metafile = f
study_metadata = metadata
elif meta_file_type == MetaFileTypes.SAMPLE_ATTRIBUTES:
if sample_attr_filepair is not None:
raise RuntimeError(
'Multiple sample attribute files found: {} and {}'.format(
sample_attr_filepair[0], f))
sample_attr_filepair = (
f, os.path.join(study_directory, metadata['data_filename']))
else:
regular_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
# First, import cancer types
for meta_filename, data_filename in cancer_type_filepairs:
import_cancer_type(jvm_args, data_filename)
# Then define the study
if study_metafile is None:
raise RuntimeError('No meta_study file found')
else:
# First remove study if exists
remove_study(jvm_args, study_metafile)
import_study(jvm_args, study_metafile)
# Next, we need to import sample definitions
if sample_attr_filepair is None:
raise RuntimeError('No sample attribute file found')
else:
meta_filename, data_filename = sample_attr_filepair
import_study_data(jvm_args, meta_filename, data_filename)
# Now, import everything else
for meta_filename, data_filename in regular_filepairs:
import_study_data(jvm_args, meta_filename, data_filename)
# do the case lists
case_list_dirname = os.path.join(study_directory, 'case_lists')
if os.path.isdir(case_list_dirname):
process_case_lists(jvm_args, case_list_dirname)
if study_metadata.get('add_global_case_list', 'false').lower() == 'true':
add_global_case_list(jvm_args, study_id)
# enable study
update_study_status(jvm_args, study_id)
def process_directory(jvm_args, study_directory):
"""
Import an entire study directory based on meta files found.
1. Determine meta files in study directory.
2. Read all meta files and determine file types.
3. Import data files in specific order by file type.
"""
study_id = None
study_meta_filename = None
study_meta_dictionary = {}
cancer_type_filepairs = []
sample_attr_filepair = None
regular_filepairs = []
gene_panel_matrix_filepair = None
zscore_filepairs = []
gsva_score_filepair = None
gsva_pvalue_filepair = None
fusion_filepair = None
# Determine meta filenames in study directory
meta_filenames = (
os.path.join(study_directory, meta_filename)
for meta_filename in os.listdir(study_directory) if
re.search(r'(\b|_)meta(\b|[_0-9])', meta_filename, flags=re.IGNORECASE)
and not (meta_filename.startswith('.') or meta_filename.endswith('~')))
# Read all meta files (excluding case lists) to determine what to import
for meta_filename in meta_filenames:
# Parse meta file
meta_dictionary = cbioportal_common.parse_metadata_file(
meta_filename, study_id=study_id, logger=LOGGER)
# Save meta dictionary in study meta dictionary
study_meta_dictionary[meta_filename] = meta_dictionary
# Retrieve meta file type
meta_file_type = meta_dictionary['meta_file_type']
if meta_file_type is None:
# invalid meta file, let's die
raise RuntimeError('Invalid meta file: ' + meta_filename)
# remember study id to give an error in case any other file is referencing a different one
if study_id is None and 'cancer_study_identifier' in meta_dictionary:
study_id = meta_dictionary['cancer_study_identifier']
# Check the type of metafile. It is to know which metafile types the
# study contains because at a later stage we want to import in a
# specific order.
# Check for cancer type file
if meta_file_type == MetaFileTypes.CANCER_TYPE:
cancer_type_filepairs.append(
(meta_filename,
os.path.join(study_directory,
meta_dictionary['data_filename'])))
# Check for meta study file
elif meta_file_type == MetaFileTypes.STUDY:
if study_meta_filename is not None:
raise RuntimeError(
'Multiple meta_study files found: {} and {}'.format(
study_meta_filename, meta_filename))
# Determine the study meta filename
study_meta_filename = meta_filename
study_meta_dictionary[study_meta_filename] = meta_dictionary
# Check for sample attributes
elif meta_file_type == MetaFileTypes.SAMPLE_ATTRIBUTES:
if sample_attr_filepair is not None:
raise RuntimeError(
'Multiple sample attribute files found: {} and {}'.format(
sample_attr_filepair[0], meta_filename)) # pylint: disable=unsubscriptable-object
sample_attr_filepair = (meta_filename,
os.path.join(
study_directory,
meta_dictionary['data_filename']))
# Check for gene panel matrix
elif meta_file_type == MetaFileTypes.GENE_PANEL_MATRIX:
gene_panel_matrix_filepair = ((
meta_filename,
os.path.join(study_directory,
meta_dictionary['data_filename'])))
# Check for z-score exression files
elif meta_file_type == MetaFileTypes.EXPRESSION and meta_dictionary[
'datatype'] == "Z-SCORE":
zscore_filepairs.append(
(meta_filename,
os.path.join(study_directory,
meta_dictionary['data_filename'])))
# Check for GSVA scores
elif meta_file_type == MetaFileTypes.GSVA_SCORES:
gsva_score_filepair = ((meta_filename,
os.path.join(
study_directory,
meta_dictionary['data_filename'])))
# Check for GSVA p-values
elif meta_file_type == MetaFileTypes.GSVA_PVALUES:
gsva_pvalue_filepair = ((meta_filename,
os.path.join(
study_directory,
meta_dictionary['data_filename'])))
# Check for fusion data
elif meta_file_type == MetaFileTypes.FUSION:
fusion_filepair = ((meta_filename,
os.path.join(
study_directory,
meta_dictionary['data_filename'])))
# Add all other types of data
else:
regular_filepairs.append(
(meta_filename,
os.path.join(study_directory,
meta_dictionary['data_filename'])))
# First, import cancer types
for meta_filename, data_filename in cancer_type_filepairs:
import_cancer_type(jvm_args, data_filename)
# Then define the study
if study_meta_filename is None:
raise RuntimeError('No meta_study file found')
else:
# First remove study if exists
remove_study(jvm_args, study_meta_filename)
import_study(jvm_args, study_meta_filename)
# Next, we need to import sample definitions
if sample_attr_filepair is None:
raise RuntimeError('No sample attribute file found')
else:
meta_filename, data_filename = sample_attr_filepair
import_study_data(jvm_args, meta_filename, data_filename,
study_meta_dictionary[meta_filename])
# Next, import everything else except gene panel, fusion data, GSVA and
# z-score expression. If in the future more types refer to each other, (like
# in a tree structure) this could be programmed in a recursive fashion.
for meta_filename, data_filename in regular_filepairs:
import_study_data(jvm_args, meta_filename, data_filename,
study_meta_dictionary[meta_filename])
# Import fusion data (after mutation)
if fusion_filepair is not None:
meta_filename, data_filename = fusion_filepair
import_study_data(jvm_args, meta_filename, data_filename,
study_meta_dictionary[meta_filename])
# Import expression z-score (after expression)
for meta_filename, data_filename in zscore_filepairs:
import_study_data(jvm_args, meta_filename, data_filename,
study_meta_dictionary[meta_filename])
# Import GSVA genetic profiles (after expression and z-scores)
if gsva_score_filepair is not None:
# First import the GSVA score data
meta_filename, data_filename = gsva_score_filepair
import_study_data(jvm_args, meta_filename, data_filename,
study_meta_dictionary[meta_filename])
# Second import the GSVA p-value data
meta_filename, data_filename = gsva_pvalue_filepair
import_study_data(jvm_args, meta_filename, data_filename,
study_meta_dictionary[meta_filename])
if gene_panel_matrix_filepair is not None:
meta_filename, data_filename = gene_panel_matrix_filepair
import_study_data(jvm_args, meta_filename, data_filename,
study_meta_dictionary[meta_filename])
# Import the case lists
case_list_dirname = os.path.join(study_directory, 'case_lists')
if os.path.isdir(case_list_dirname):
process_case_lists(jvm_args, case_list_dirname)
if study_meta_dictionary[study_meta_filename].get(
'add_global_case_list', 'false').lower() == 'true':
add_global_case_list(jvm_args, study_id)
# enable study
update_study_status(jvm_args, study_id)
def process_directory(jvm_args, study_directory):
"""Import an entire study directory based on meta files found."""
meta_filenames = (
os.path.join(study_directory, f) for f in os.listdir(study_directory)
if re.search(r'(\b|_)meta(\b|[_0-9])', f, flags=re.IGNORECASE)
and not (f.startswith('.') or f.endswith('~')))
study_id = None
study_metafile = None
study_metadata = None
cancer_type_filepairs = []
sample_attr_filepair = None
regular_filepairs = []
gene_panel_matrix_filepair = None
zscore_filepairs = []
gsva_score_filepair = None
gsva_pvalue_filepair = None
# read all meta files (excluding case lists) to determine what to import
for f in meta_filenames:
# parse meta file
metadata, meta_file_type = cbioportal_common.parse_metadata_file(
f, study_id=study_id, logger=LOGGER)
if meta_file_type is None:
# invalid meta file, let's die
raise RuntimeError('Invalid meta file: ' + f)
# remember study id to give an error in case any other file is referencing a different one
if study_id is None and 'cancer_study_identifier' in metadata:
study_id = metadata['cancer_study_identifier']
if meta_file_type == MetaFileTypes.CANCER_TYPE:
cancer_type_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.STUDY:
if study_metafile is not None:
raise RuntimeError(
'Multiple meta_study files found: {} and {}'.format(
study_metafile, f))
study_metafile = f
study_metadata = metadata
elif meta_file_type == MetaFileTypes.SAMPLE_ATTRIBUTES:
if sample_attr_filepair is not None:
raise RuntimeError(
'Multiple sample attribute files found: {} and {}'.format(
sample_attr_filepair[0], f)) # pylint: disable=unsubscriptable-object
sample_attr_filepair = (f,
os.path.join(study_directory,
metadata['data_filename']))
elif meta_file_type == MetaFileTypes.GENE_PANEL_MATRIX:
gene_panel_matrix_filepair = ((f,
os.path.join(
study_directory,
metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.EXPRESSION and metadata[
'datatype'] == "Z-SCORE":
zscore_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.GSVA_SCORES:
gsva_score_filepair = ((f,
os.path.join(study_directory,
metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.GSVA_PVALUES:
gsva_pvalue_filepair = ((f,
os.path.join(study_directory,
metadata['data_filename'])))
else:
regular_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
# First, import cancer types
for meta_filename, data_filename in cancer_type_filepairs:
import_cancer_type(jvm_args, data_filename)
# Then define the study
if study_metafile is None:
raise RuntimeError('No meta_study file found')
else:
# First remove study if exists
remove_study(jvm_args, study_metafile)
import_study(jvm_args, study_metafile)
# Next, we need to import sample definitions
if sample_attr_filepair is None:
raise RuntimeError('No sample attribute file found')
else:
meta_filename, data_filename = sample_attr_filepair
import_study_data(jvm_args, meta_filename, data_filename)
# Now, import everything else except gene panel, gsva & z-score expression
# These data types have to be imported last, because they both refer to expression by
# source_stable_id. If in the future more types refer to each other, (like
# in a tree structure) this could be programmed in a recursive fashion.
for meta_filename, data_filename in regular_filepairs:
import_study_data(jvm_args, meta_filename, data_filename)
# Now import expression z-score
for meta_filename, data_filename in zscore_filepairs:
import_study_data(jvm_args, meta_filename, data_filename)
# Now import gsva genetic profiles
if gsva_score_filepair is not None:
# First import the score data
meta_filename, data_filename = gsva_score_filepair
import_study_data(jvm_args, meta_filename, data_filename)
#Second import the pvalue data
meta_filename, data_filename = gsva_pvalue_filepair
import_study_data(jvm_args, gsva_pvalue_filepair[0],
gsva_pvalue_filepair[1])
if gene_panel_matrix_filepair is not None:
import_study_data(jvm_args, gene_panel_matrix_filepair[0],
gene_panel_matrix_filepair[1])
# do the case lists
case_list_dirname = os.path.join(study_directory, 'case_lists')
if os.path.isdir(case_list_dirname):
process_case_lists(jvm_args, case_list_dirname)
if study_metadata.get('add_global_case_list', 'false').lower() == 'true':
add_global_case_list(jvm_args, study_id)
# enable study
update_study_status(jvm_args, study_id)
def process_directory(jvm_args, study_directory):
"""Import an entire study directory based on meta files found."""
meta_filenames = (
os.path.join(study_directory, f) for f in os.listdir(study_directory)
if re.search(r'(\b|_)meta(\b|[_0-9])', f, flags=re.IGNORECASE)
and not (f.startswith('.') or f.endswith('~')))
study_id = None
study_metafile = None
study_metadata = None
cancer_type_filepairs = []
sample_attr_filepair = None
regular_filepairs = []
# read all meta files (excluding case lists) to determine what to import
for f in meta_filenames:
# parse meta file
metadata, meta_file_type = cbioportal_common.parse_metadata_file(
f, study_id=study_id, logger=LOGGER)
if meta_file_type is None:
# invalid meta file, let's die
raise RuntimeError('Invalid meta file: ' + f)
# remember study id to give an error in case any other file is referencing a different one
if study_id is None and 'cancer_study_identifier' in metadata:
study_id = metadata['cancer_study_identifier']
if meta_file_type == MetaFileTypes.CANCER_TYPE:
cancer_type_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.STUDY:
if study_metafile is not None:
raise RuntimeError(
'Multiple meta_study files found: {} and {}'.format(
study_metafile, f))
study_metafile = f
study_metadata = metadata
elif meta_file_type == MetaFileTypes.SAMPLE_ATTRIBUTES:
if sample_attr_filepair is not None:
raise RuntimeError(
'Multiple sample attribute files found: {} and {}'.format(
sample_attr_filepair[0], f))
sample_attr_filepair = (f,
os.path.join(study_directory,
metadata['data_filename']))
else:
regular_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
# First, import cancer types
for meta_filename, data_filename in cancer_type_filepairs:
import_cancer_type(jvm_args, data_filename)
# Then define the study
if study_metafile is None:
raise RuntimeError('No meta_study file found')
else:
# First remove study if exists
remove_study(jvm_args, study_metafile)
import_study(jvm_args, study_metafile)
# Next, we need to import sample definitions
if sample_attr_filepair is None:
raise RuntimeError('No sample attribute file found')
else:
meta_filename, data_filename = sample_attr_filepair
import_study_data(jvm_args, meta_filename, data_filename)
# Now, import everything else
for meta_filename, data_filename in regular_filepairs:
import_study_data(jvm_args, meta_filename, data_filename)
# do the case lists
case_list_dirname = os.path.join(study_directory, 'case_lists')
if os.path.isdir(case_list_dirname):
process_case_lists(jvm_args, case_list_dirname)
if study_metadata.get('add_global_case_list', 'false').lower() == 'true':
add_global_case_list(jvm_args, study_id)
# enable study
update_study_status(jvm_args, study_id)
def process_directory(jvm_args, study_directory):
"""Import an entire study directory based on meta files found."""
meta_filenames = (
os.path.join(study_directory, f) for
f in os.listdir(study_directory) if
re.search(r'(\b|_)meta(\b|[_0-9])', f,
flags=re.IGNORECASE) and
not (f.startswith('.') or f.endswith('~')))
study_id = None
study_metafile = None
study_metadata = None
cancer_type_filepairs = []
sample_attr_filepair = None
regular_filepairs = []
gene_panel_matrix_filepair = None
zscore_filepairs = []
gsva_score_filepair = None
gsva_pvalue_filepair = None
# read all meta files (excluding case lists) to determine what to import
for f in meta_filenames:
# parse meta file
metadata, meta_file_type = cbioportal_common.parse_metadata_file(
f, study_id=study_id, logger=LOGGER)
if meta_file_type is None:
# invalid meta file, let's die
raise RuntimeError('Invalid meta file: ' + f)
# remember study id to give an error in case any other file is referencing a different one
if study_id is None and 'cancer_study_identifier' in metadata:
study_id = metadata['cancer_study_identifier']
if meta_file_type == MetaFileTypes.CANCER_TYPE:
cancer_type_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.STUDY:
if study_metafile is not None:
raise RuntimeError(
'Multiple meta_study files found: {} and {}'.format(
study_metafile, f))
study_metafile = f
study_metadata = metadata
elif meta_file_type == MetaFileTypes.SAMPLE_ATTRIBUTES:
if sample_attr_filepair is not None:
raise RuntimeError(
'Multiple sample attribute files found: {} and {}'.format(
sample_attr_filepair[0], f)) # pylint: disable=unsubscriptable-object
sample_attr_filepair = (
f, os.path.join(study_directory, metadata['data_filename']))
elif meta_file_type == MetaFileTypes.GENE_PANEL_MATRIX:
gene_panel_matrix_filepair = (
(f, os.path.join(study_directory, metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.EXPRESSION and metadata['datatype'] == "Z-SCORE":
zscore_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.GSVA_SCORES:
gsva_score_filepair = (
(f, os.path.join(study_directory, metadata['data_filename'])))
elif meta_file_type == MetaFileTypes.GSVA_PVALUES:
gsva_pvalue_filepair = (
(f, os.path.join(study_directory, metadata['data_filename'])))
else:
regular_filepairs.append(
(f, os.path.join(study_directory, metadata['data_filename'])))
# First, import cancer types
for meta_filename, data_filename in cancer_type_filepairs:
import_cancer_type(jvm_args, data_filename)
# Then define the study
if study_metafile is None:
raise RuntimeError('No meta_study file found')
else:
# First remove study if exists
remove_study(jvm_args, study_metafile)
import_study(jvm_args, study_metafile)
# Next, we need to import sample definitions
if sample_attr_filepair is None:
raise RuntimeError('No sample attribute file found')
else:
meta_filename, data_filename = sample_attr_filepair
import_study_data(jvm_args, meta_filename, data_filename)
# Now, import everything else except gene panel, gsva & z-score expression
# These data types have to be imported last, because they both refer to expression by
# source_stable_id. If in the future more types refer to each other, (like
# in a tree structure) this could be programmed in a recursive fashion.
for meta_filename, data_filename in regular_filepairs:
import_study_data(jvm_args, meta_filename, data_filename)
# Now import expression z-score
for meta_filename, data_filename in zscore_filepairs:
import_study_data(jvm_args, meta_filename, data_filename)
# Now import gsva genetic profiles
if gsva_score_filepair is not None:
# First import the score data
meta_filename, data_filename = gsva_score_filepair
import_study_data(jvm_args, meta_filename, data_filename)
#Second import the pvalue data
meta_filename, data_filename = gsva_pvalue_filepair
import_study_data(jvm_args, gsva_pvalue_filepair[0], gsva_pvalue_filepair[1])
if gene_panel_matrix_filepair is not None:
import_study_data(jvm_args, gene_panel_matrix_filepair[0], gene_panel_matrix_filepair[1])
# do the case lists
case_list_dirname = os.path.join(study_directory, 'case_lists')
if os.path.isdir(case_list_dirname):
process_case_lists(jvm_args, case_list_dirname)
if study_metadata.get('add_global_case_list', 'false').lower() == 'true':
add_global_case_list(jvm_args, study_id)
# enable study
update_study_status(jvm_args, study_id)
def process_directory(jvm_args, study_directory):
"""
Import an entire study directory based on meta files found.
1. Determine meta files in study directory.
2. Read all meta files and determine file types.
3. Import data files in specific order by file type.
"""
study_id = None
study_meta_filename = None
study_meta_dictionary = {}
cancer_type_filepairs = []
sample_attr_filepair = None
regular_filepairs = []
gene_panel_matrix_filepair = None
zscore_filepairs = []
gsva_score_filepair = None
gsva_pvalue_filepair = None
fusion_filepair = None
# Determine meta filenames in study directory
meta_filenames = (
os.path.join(study_directory, meta_filename) for
meta_filename in os.listdir(study_directory) if
re.search(r'(\b|_)meta(\b|[_0-9])', meta_filename,
flags=re.IGNORECASE) and
not (meta_filename.startswith('.') or meta_filename.endswith('~')))
# Read all meta files (excluding case lists) to determine what to import
for meta_filename in meta_filenames:
# Parse meta file
meta_dictionary = cbioportal_common.parse_metadata_file(
meta_filename, study_id=study_id, logger=LOGGER)
# Save meta dictionary in study meta dictionary
study_meta_dictionary[meta_filename] = meta_dictionary
# Retrieve meta file type
meta_file_type = meta_dictionary['meta_file_type']
if meta_file_type is None:
# invalid meta file, let's die
raise RuntimeError('Invalid meta file: ' + meta_filename)
# remember study id to give an error in case any other file is referencing a different one
if study_id is None and 'cancer_study_identifier' in meta_dictionary:
study_id = meta_dictionary['cancer_study_identifier']
# Check the type of metafile. It is to know which metafile types the
# study contains because at a later stage we want to import in a
# specific order.
# Check for cancer type file
if meta_file_type == MetaFileTypes.CANCER_TYPE:
cancer_type_filepairs.append(
(meta_filename, os.path.join(study_directory, meta_dictionary['data_filename'])))
# Check for meta study file
elif meta_file_type == MetaFileTypes.STUDY:
if study_meta_filename is not None:
raise RuntimeError(
'Multiple meta_study files found: {} and {}'.format(
study_meta_filename, meta_filename))
# Determine the study meta filename
study_meta_filename = meta_filename
study_meta_dictionary[study_meta_filename] = meta_dictionary
# Check for sample attributes
elif meta_file_type == MetaFileTypes.SAMPLE_ATTRIBUTES:
if sample_attr_filepair is not None:
raise RuntimeError(
'Multiple sample attribute files found: {} and {}'.format(
sample_attr_filepair[0], meta_filename)) # pylint: disable=unsubscriptable-object
sample_attr_filepair = (
meta_filename, os.path.join(study_directory, meta_dictionary['data_filename']))
# Check for gene panel matrix
elif meta_file_type == MetaFileTypes.GENE_PANEL_MATRIX:
gene_panel_matrix_filepair = (
(meta_filename, os.path.join(study_directory, meta_dictionary['data_filename'])))
# Check for z-score exression files
elif meta_file_type == MetaFileTypes.EXPRESSION and meta_dictionary['datatype'] == "Z-SCORE":
zscore_filepairs.append(
(meta_filename, os.path.join(study_directory, meta_dictionary['data_filename'])))
# Check for GSVA scores
elif meta_file_type == MetaFileTypes.GSVA_SCORES:
gsva_score_filepair = (
(meta_filename, os.path.join(study_directory, meta_dictionary['data_filename'])))
# Check for GSVA p-values
elif meta_file_type == MetaFileTypes.GSVA_PVALUES:
gsva_pvalue_filepair = (
(meta_filename, os.path.join(study_directory, meta_dictionary['data_filename'])))
# Check for fusion data
elif meta_file_type == MetaFileTypes.FUSION:
fusion_filepair = (
(meta_filename, os.path.join(study_directory, meta_dictionary['data_filename'])))
# Add all other types of data
else:
regular_filepairs.append(
(meta_filename, os.path.join(study_directory, meta_dictionary['data_filename'])))
# First, import cancer types
for meta_filename, data_filename in cancer_type_filepairs:
import_cancer_type(jvm_args, data_filename)
# Then define the study
if study_meta_filename is None:
raise RuntimeError('No meta_study file found')
else:
# First remove study if exists
remove_study(jvm_args, study_meta_filename)
import_study(jvm_args, study_meta_filename)
# Next, we need to import sample definitions
if sample_attr_filepair is None:
raise RuntimeError('No sample attribute file found')
else:
meta_filename, data_filename = sample_attr_filepair
import_study_data(jvm_args, meta_filename, data_filename, study_meta_dictionary[meta_filename])
# Next, import everything else except gene panel, fusion data, GSVA and
# z-score expression. If in the future more types refer to each other, (like
# in a tree structure) this could be programmed in a recursive fashion.
for meta_filename, data_filename in regular_filepairs:
import_study_data(jvm_args, meta_filename, data_filename, study_meta_dictionary[meta_filename])
# Import fusion data (after mutation)
if fusion_filepair is not None:
meta_filename, data_filename = fusion_filepair
import_study_data(jvm_args, meta_filename, data_filename, study_meta_dictionary[meta_filename])
# Import expression z-score (after expression)
for meta_filename, data_filename in zscore_filepairs:
import_study_data(jvm_args, meta_filename, data_filename, study_meta_dictionary[meta_filename])
# Import GSVA genetic profiles (after expression and z-scores)
if gsva_score_filepair is not None:
# First import the GSVA score data
meta_filename, data_filename = gsva_score_filepair
import_study_data(jvm_args, meta_filename, data_filename, study_meta_dictionary[meta_filename])
# Second import the GSVA p-value data
meta_filename, data_filename = gsva_pvalue_filepair
import_study_data(jvm_args, meta_filename, data_filename, study_meta_dictionary[meta_filename])
if gene_panel_matrix_filepair is not None:
meta_filename, data_filename = gene_panel_matrix_filepair
import_study_data(jvm_args, meta_filename, data_filename, study_meta_dictionary[meta_filename])
# Import the case lists
case_list_dirname = os.path.join(study_directory, 'case_lists')
if os.path.isdir(case_list_dirname):
process_case_lists(jvm_args, case_list_dirname)
if study_meta_dictionary[study_meta_filename].get('add_global_case_list', 'false').lower() == 'true':
add_global_case_list(jvm_args, study_id)
# enable study
update_study_status(jvm_args, study_id)
