def map_experiment_files(project_id, datasetPath, mapping):
files = builder_utils.listDirectoryFiles(datasetPath)
for file in files:
outputfile = os.path.join(datasetPath, file)
data = builder_utils.readDataset(outputfile)
data = map_experimental_data(data, mapping)
builder_utils.export_contents(data, datasetPath, file)
def load_dataset(uri, configuration):
''' This function gets the molecular data from a proteomics experiment.
Input: uri of the processed file resulting from MQ
Output: pandas DataFrame with the columns and filters defined in config.py '''
data = None
regexCols = None
filters = None
columns = configuration["columns"]
regexCols = [c.replace("\\\\", "\\") for c in columns if '+' in c]
columns = set(columns).difference(regexCols)
generated_columns = []
if 'generated_columns' in configuration:
generated_columns = configuration['generated_columns']
if 'filters' in configuration:
filters = configuration["filters"]
indexCol = configuration["indexCol"]
data = builder_utils.readDataset(uri)
missing_cols = check_columns(data, columns, generated_columns)
if len(missing_cols) == 0:
if filters is not None:
data = data[data[filters].isnull().all(1)]
data = data.drop(filters, axis=1)
columns = set(columns).difference(filters)
if 'numeric filter' in configuration:
for f in configuration['numeric filter']:
key = list(f.keys())[0]
if key in columns:
value = f[key]
data = data[data[key] >= value]
else:
raise Exception(
"Error when applying numeric filter on {}. The column is not in the dataset"
.format(f))
data = data.dropna(subset=[configuration["proteinCol"]], axis=0)
data = expand_groups(data, configuration)
columns.remove(indexCol)
for regex in regexCols:
r = re.compile(regex)
columns.update(set(filter(r.match, data.columns)))
data = data[list(columns)].replace('Filtered', np.nan)
value_cols = get_value_cols(data, configuration)
data[value_cols] = data[value_cols].apply(
lambda x: pd.to_numeric(x, errors='coerce'))
data = data.dropna(how='all', subset=value_cols, axis=0)
else:
raise Exception(
"Error when importing proteomics experiment.\n Missing columns: {}"
.format(",".join(missing_cols)))
return data, regexCols
def parse_dataset(projectId, configuration, dataDir, key='project'):
'''This function parses clinical data from subjects in the project
Input: uri of the clinical data file. Format: Subjects as rows, clinical variables as columns
Output: pandas DataFrame with the same input format but the clinical variables mapped to the
right ontology (defined in config), i.e. type = -40 -> SNOMED CT'''
data = None
if 'file_'+key in configuration:
data_file = configuration['file_'+key].replace('PROJECTID', projectId)
filepath = os.path.join(dataDir, data_file)
if os.path.isfile(filepath):
data = builder_utils.readDataset(filepath)
return data
def loadWESDataset(uri, configuration):
''' This function gets the molecular data from a Whole Exome Sequencing experiment.
Input: uri of the processed file resulting from the WES analysis pipeline. The resulting
Annovar annotated VCF file from Mutect (sampleID_mutect_annovar.vcf)
Output: pandas DataFrame with the columns and filters defined in config.py '''
aux = uri.split("/")[-1].split("_")
sample = aux[0]
#Get the columns from config
columns = configuration["columns"]
#Read the data from file
data = builder_utils.readDataset(uri)
if configuration['filter'] in data.columns:
data = data.loc[data[configuration['filter']], :]
data = data[columns]
data["sample"] = aux[0]
data["variant_calling_method"] = aux[1]
data["annotated_with"] = aux[2].split('.')[0]
data["alternative_names"] = data[configuration["alt_names"]]
data = data.drop(configuration["alt_names"], axis = 1)
data = data.iloc[1:]
data = data.replace('.', np.nan)
data["ID"] = data[configuration["id_fields"]].apply(lambda x: str(x[0])+":g."+str(x[1])+str(x[2])+'>'+str(x[3]), axis=1)
data.columns = configuration['new_columns']
return sample, data
def run_processing(n_clicks, project_id):
message = None
style = {'display': 'none'}
table = None
if n_clicks > 0:
session_cookie = flask.request.cookies.get('custom-auth-session')
destDir = os.path.join(experimentDir, project_id)
builder_utils.checkDirectory(destDir)
temporaryDirectory = os.path.join(tmpDirectory,
session_cookie + "upload")
datasets = builder_utils.listDirectoryFoldersNotEmpty(
temporaryDirectory)
res_n = dataUpload.check_samples_in_project(driver, project_id)
if 'experimental_design' in datasets:
dataset = 'experimental_design'
directory = os.path.join(temporaryDirectory, dataset)
experimental_files = os.listdir(directory)
if config['file_design'].replace('PROJECTID',
project_id) in experimental_files:
experimental_filename = config['file_design'].replace(
'PROJECTID', project_id)
designData = builder_utils.readDataset(
os.path.join(directory, experimental_filename))
designData = designData.astype(str)
if 'subject external_id' in designData.columns and 'biological_sample external_id' in designData.columns and 'analytical_sample external_id' in designData.columns:
if (res_n > 0).any().values.sum() > 0:
res = dataUpload.remove_samples_nodes_db(
driver, project_id)
res_n = dataUpload.check_samples_in_project(
driver, project_id)
if (res_n > 0).any().values.sum() > 0:
message = 'ERROR: There is already an experimental design loaded into the database and there was an error when trying to delete it. Contact your administrator.'.format(
experimental_filename, ','.join([
'subject external_id',
'biological_sample external_id',
'analytical_sample external_id'
]))
return message, style, table
res_n = None
result = create_new_identifiers.apply_async(
args=[
project_id,
designData.to_json(), directory,
experimental_filename
],
task_id='data_upload_' + session_cookie +
datetime.now().strftime('%Y%m-%d%H-%M%S-'))
result_output = result.wait(timeout=None,
propagate=True,
interval=0.2)
res_n = pd.DataFrame.from_dict(result_output['res_n'])
else:
message = 'ERROR: The Experimental design file provided ({}) is missing some of the required fields: {}'.format(
experimental_filename, ','.join([
'subject external_id',
'biological_sample external_id',
'analytical_sample external_id'
]))
builder_utils.remove_directory(directory)
return message, style, table
if 'clinical' in datasets:
dataset = 'clinical'
directory = os.path.join(temporaryDirectory, dataset)
clinical_files = os.listdir(directory)
if config['file_clinical'].replace('PROJECTID',
project_id) in clinical_files:
clinical_filename = config['file_clinical'].replace(
'PROJECTID', project_id)
data = builder_utils.readDataset(
os.path.join(directory, clinical_filename))
external_ids = {}
if 'subject external_id' in data and 'biological_sample external_id' in data and 'analytical_sample external_id' in data:
external_ids['subjects'] = data[
'subject external_id'].astype(str).unique().tolist()
external_ids['biological_samples'] = data[
'biological_sample external_id'].astype(
str).unique().tolist()
external_ids['analytical_samples'] = data[
'analytical_sample external_id'].astype(
str).unique().tolist()
dataUpload.create_mapping_cols_clinical(
driver,
data,
directory,
clinical_filename,
separator=separator)
if 0 in res_n.values:
samples = ', '.join([k for (k, v) in res_n if v == 0])
message = 'ERROR: No {} for project {} in the database. Please upload first the experimental design (ExperimentalDesign_{}.xlsx)'.format(
samples, project_id, project_id)
builder_utils.remove_directory(directory)
return message, style, table
else:
db_ids = dataUpload.check_external_ids_in_db(
driver, project_id).to_dict()
message = ''
intersections = {}
differences_in = {}
differences_out = {}
for col in external_ids:
intersect = list(
set(db_ids[col].values()).intersection(
external_ids[col]))
difference_in = list(
set(db_ids[col].values()).difference(
external_ids[col]))
difference_out = list(
set(external_ids[col]).difference(
set(db_ids[col].values())))
if len(difference_in) > 0 or len(
difference_out) > 0:
intersections[col] = intersect
differences_in[col] = difference_in
differences_out[col] = difference_out
for col in intersections:
message += 'WARNING: Some {} identifiers were not matched:\n Matching: {}\n No information provided: {} \n Non-existing in the database: {}\n'.format(
col, len(intersections[col]),
','.join(differences_in[col]),
','.join(differences_out[col]))
else:
message = 'ERROR: Format of the Clinical Data file is not correct. Check template in the documentation. Check columns: subject external_id, biological_sample external_id and analytical_sample external_id'
builder_utils.remove_directory(directory)
return message, style, table
try:
for dataset in datasets:
source = os.path.join(temporaryDirectory, dataset)
destination = os.path.join(destDir, dataset)
builder_utils.copytree(source, destination)
datasetPath = os.path.join(
os.path.join(experimentsImportDir, project_id), dataset)
if dataset != "experimental_design":
eh.generate_dataset_imports(project_id, dataset,
datasetPath)
loader.partialUpdate(imports=['project', 'experiment'],
specific=[project_id])
filename = os.path.join(tmpDirectory,
'Uploaded_files_' + project_id)
utils.compress_directory(filename,
temporaryDirectory,
compression_format='zip')
style = {'display': 'block'}
message = 'Files successfully uploaded.'
table = dataUpload.get_project_information(driver, project_id)
except Exception as err:
style = {'display': 'block'}
message = str(err)
return message, style, table