def sample_sheet(sspath):
Sheet = SampleSheet(sspath)
data = {}
for sample in Sheet.samples:
sample_name = sample['Sample_Name']
sample_description = sample['Description']
if len(sample_description.split(
"_")) != 15 or sample_description.split("_")[-1] == 'null':
sample_criteria = 7 #'Information saknas'
else:
sample_criteria = int(sample_description.split("_")[-1])
#Skip controls and eurofins samples
if sample_name.startswith(('PosCtrl', 'PosKon', 'NegCtrl', 'NegKon')):
continue
#Skip samples set to runType = 01 (desc. field 3)
#These are samples which have been re-sequenced, so they have already been uploaded to GENSAM.
runtype = sample_description.split("_")[2]
if runtype == '01':
continue
else:
#populate a dictionary with samples and their selection criteria value
sample_id = sample_name.split('_')[0]
data[sample_id] = sample_criteria
return data
def sample_sheet_criteria(path, inhouse_dict, week, run_name):
Sheet = SampleSheet(path)
data = {}
data['general'] = 0
data['selection'] = 0
data['unknown'] = 0
for sample in Sheet.samples:
sample_name = sample['Sample_ID']
description = sample['Description']
# Skip controls or if not in dict
if sample_name.lower().startswith(('neg', 'pos')):
continue
elif sample_name not in inhouse_dict[week][run_name].keys():
continue
else:
#Check if the last element is the selection criterion. This is a pretty weak check...
criterion = description.split("_")[-1]
if criterion.isdigit() and int(criterion) <= 10:
# print(criterion)
if int(criterion) == 1 or int(criterion) == 8 or int(criterion) == 9:
data['general'] += 1
elif int(criterion) == 7:
data['unknown'] += 1
else:
data['selection'] += 1
else:
data['unknown'] += 1
return data
def writeSammpleSheets(sample_list, sheet_path, template_sheet):
samplesheet_name = os.path.basename(sheet_path)
samplesheet_dir = os.path.dirname(os.path.realpath(sheet_path))
count = 0
exit_status = "success"
for key in sample_list:
count += 1
logger.debug(
f"{len(sample_list[key])} samples with idx lengths {key[1]}/{key[2]} for {key[0]} dataset"
)
new_sample_sheet = SampleSheet()
new_sample_sheet.Header = template_sheet.Header
new_sample_sheet.Reads = template_sheet.Reads
new_sample_sheet.Settings = template_sheet.Settings
for sample in sample_list[key]:
new_sample_sheet.add_sample(sample)
new_sample_sheet_file = os.path.join(
samplesheet_dir,
samplesheet_name + ".custom." + str(count) + "." + key[0])
logger.info(f"Creating custom sample sheet: {new_sample_sheet_file}")
try:
with open(new_sample_sheet_file, "w") as ss_writer:
new_sample_sheet.write(ss_writer)
except Exception as error:
logger.error(f"Exception writing new sample sheet: {error}")
exit_status = "failure"
logger.debug(f"Created custom sample sheet: {new_sample_sheet_file}")
return exit_status
def main(samplesheet_file_path, check_only):
logger.info(f"Checking SampleSheet {samplesheet_file_path}")
original_sample_sheet = SampleSheet(samplesheet_file_path)
# Run some consistency checks
#import_library_sheet_from_google('2019')
# TODO: replace has_error return with enum and expand to error, warning, info?
#has_header_error = checkSampleSheetMetadata(original_sample_sheet)
#has_id_error = checkSampleAndLibraryIdFormat(original_sample_sheet)
#has_index_error = checkSampleSheetForIndexClashes(original_sample_sheet)
#has_metadata_error = checkMetadataCorrespondence(original_sample_sheet)
# Only fail on metadata or id errors
#if has_header_error or has_id_error or has_index_error or has_metadata_error:
# raise ValueError(f"Validation detected errors. Please review the error logs!")
# Split and write individual SampleSheets, based on indexes and technology (10X)
if not check_only:
# Sort samples based on technology (truseq/10X and/or index length)
# Also replace N indexes with ""
sorted_samples = getSortedSamples(original_sample_sheet)
# Now that the samples have been sorted, we can write one or more custom sample sheets
# (which may be the same as the original if no processing was necessary)
logger.info(f"Writing {len(sorted_samples)} sample sheets.")
writeSammpleSheets(sample_list=sorted_samples,
sheet_path=samplesheet_file_path,
template_sheet=original_sample_sheet)
logger.info("All done.")
def sample_sheet(path,run):
Sheet = SampleSheet(path)
data = {}
for sample in Sheet.samples:
sample_name = sample['Sample_ID']
description = sample['Description']
# Skip controls
if sample_name.startswith(('NegCtrl', 'PosCtrl', 'PosKon', 'NegKon')):
continue
else:
data[sample['Sample_ID']] = []
data[sample['Sample_ID']].append({
'referensnummer': description.split("_")[0],
'date': description.split("_")[1],
'runtype': description.split("_")[2],
'age': description.split("_")[3],
'gender': description.split("_")[4],
'lab_reference': description.split("_")[5],
'postalcode': description.split("_")[6],
'ct_value': description.split("_")[7]
})
with open(f"/medstore/results/clinical/SARS-CoV-2-typing/nextseq_data/{run}/metadata/{run}_metadata.json", 'w') as outfile:
json.dump(data, outfile,indent=4)
def parse_samplesheet(args):
ss = SampleSheet(args.input)
df = pd.DataFrame([s.to_json() for s in ss.samples])
df["is_umi"] = args.is_umi
df["fwd_adapter"] = args.fwd_adapter
df["rev_adapter"] = args.rev_adapter
if args.merge_lanes:
log.info("Merging samples across all lanes!")
df["Lane"] = "all"
else:
if "Lane" not in df:
log.error("No lanes specified in SampleSheet.csv; use --merge-lanes or update sample sheet.")
sys.exit(1)
df["Sample_Project"] = args.project_name
df["library_type"] = args.library_type
if "Sample_ID" in df:
df["Sample_Name"] = df.Sample_ID
elif "Sample_Name" in df:
df["Sample_ID"] = df.Sample_Name
else:
log.error("Samplesheet must specify Sample_ID or Sample_Name!")
sys.exit(1)
df = df.drop_duplicates() # needed in case merge_lanes is true, which can result in duplicates
return df
def create_investigators(samplesheet_path):
samplesheet_data = SampleSheet(samplesheet_path)
header_info = samplesheet_data.Header
investigator_initials_list = header_info['Investigator Name'].split(";")
investigator_dict = {}
for investigator_initials in investigator_initials_list:
investigator_dict[investigator_initials] = Investigator(
investigator_initials)
return investigator_dict
def __parse_sample_sheet(self, path_to_sample_sheet):
parsed_data = {
"header": {
"experiment_name": None,
"instrument_type": None,
"investigator_name": None,
"workflow": None,
"chemistry": None,
},
"reads": [],
"settings": {},
"data": [],
}
try:
sample_sheet = json.loads(
SampleSheet(path_to_sample_sheet).to_json())
except Exception as e:
print(e)
sample_sheet_keys_to_message_keys_sample_sheet_header = {
'Experiment Name': 'experiment_name',
'Instrument Type': 'instrument_type',
'Investigator Name': 'investigator_name',
'Workflow': 'workflow',
'Chemistry': 'chemistry',
}
for sample_sheet_key, message_key in sample_sheet_keys_to_message_keys_sample_sheet_header.items(
):
try:
parsed_data['header'][message_key] = sample_sheet['Header'][
sample_sheet_key]
except Exception as e:
print(e)
for read in sample_sheet['Reads']:
parsed_data['reads'].append(read)
for key, val in sample_sheet['Settings'].items():
if key == 'ReverseComplement':
key = 'reverse_complement'
else:
key = key.lower()
parsed_data['settings'][key] = val
for sample in sample_sheet['Data']:
sample_to_append = {}
for key, val in sample.items():
sample_to_append[key.lower()] = val
parsed_data['data'].append(sample_to_append)
return parsed_data
def setUp(self):
qc_config = {
'name': 'UnidentifiedIndexHandler',
'significance_threshold': 1,
'white_listed_indexes': ['.*N.*', 'G{8,}']
}
self.unidentifiedIndexHandler = UnidentifiedIndexHandler(qc_config)
conversion_results_key = "ConversionResults"
conversion_results = get_stats_json()["ConversionResults"]
samplesheet_key = "samplesheet"
self.samplesheet = SampleSheet()
sample_1 = Sample(
dict(Lane=1,
Sample_ID='1823A',
Sample_Name='1823A-tissue',
index='AAAA'))
sample_2 = Sample(
dict(Lane=2,
Sample_ID='1823B',
Sample_Name='1823B-tissue',
index='TTTT'))
sample_3 = Sample(
dict(Lane=3,
Sample_ID='1823C',
Sample_Name='1823C-tissue',
index='AAAA',
index2='TTTT'))
sample_4 = Sample(
dict(Lane=4,
Sample_ID='1823D',
Sample_Name='1823D-tissue',
index='GGGG',
index2='CCCC'))
sample_5 = Sample(
dict(Lane=6,
Sample_ID='1823E',
Sample_Name='1823D-tissue',
index='ATCG'))
self.samplesheet.add_sample(sample_1)
self.samplesheet.add_sample(sample_2)
self.samplesheet.add_sample(sample_3)
self.samplesheet.add_sample(sample_4)
self.samplesheet.add_sample(sample_5)
self.unidentifiedIndexHandler.collect(
(conversion_results_key, conversion_results))
self.unidentifiedIndexHandler.collect(
(samplesheet_key, self.samplesheet))
self.samplesheet_searcher = _SamplesheetSearcher(self.samplesheet)
def build_samplesheet(df, args):
samplesheet = SampleSheet()
for ix, row in df.iterrows():
s = {
'Sample_ID': row.Sample_ID,
'Sample_Name': row.Sample_Name,
'Sample_Project': row.Sample_Project,
'index': row["index"],
'index2': row.index2
}
if not args.merge_lanes:
s["Lane"] = row.Lane
samplesheet.add_sample(Sample(s))
return samplesheet
def make_sample_sheet(body: Mapping[str, Any],
adapter_result_type=None) -> SampleSheet:
wfa = body[WORKFLOW_ACTIVITY]
activity_id = wfa[ID]
wf = wfa[WORKFLOW]
samples = wf[SAMPLES]
sample_sheet = SampleSheet()
for sample in samples:
sample_sheet.add_samples(
sample_records(activity_id,
sample,
adapter_result_type=adapter_result_type))
return sample_sheet
def get_sample_sheet_info(sample_sheet_path, header_name, data_name):
sample_sheet = SampleSheet(sample_sheet_path)
## Header section
header = pd.DataFrame(list(sample_sheet.Header.values()),
index=list(sample_sheet.Header.keys())).transpose()
for i in header_name:
if i not in header.columns:
header[i] = np.nan
header = header.rename(columns={'Description': 'Header.Description'})
## Reads section
reads = pd.DataFrame(sample_sheet.Reads, index=['Read1',
'Read2']).transpose()
## Settings section
if len(sample_sheet.Settings) == 0:
setting = pd.DataFrame({'Adapter': [np.nan]})
else:
setting = pd.DataFrame(list(sample_sheet.Settings.values()),
index=list(
sample_sheet.Settings.keys())).transpose()
setting = setting.rename(columns={'adapter': 'Adapter'})
## Data section
run_name = '_'.join([
sample_sheet_path.split('/')[3],
sample_sheet_path.split('/')[4].split('_')[2]
])
data = pd.DataFrame()
for i in range(0, len(sample_sheet)):
tmp = pd.DataFrame([dict(sample_sheet.samples[i])])
data = data.append(tmp, sort=False)
for i in data_name:
if i not in data.columns:
data[i] = np.nan
data['Run_barcode'] = run_name # + '_' + data['Sample_ID'].astype(str)
data = data.rename(columns={'Description': 'Data.Description'})
## Combine all sections
data = pd.concat([data, header], axis=1)
data = pd.concat([data, setting], axis=1)
data = pd.concat([data, reads], axis=1)
# out_header = ['Date', 'Run_barcode', 'Sample_ID', 'Read1', 'Read2', "Adapter", 'Sample_Name', 'Data.Description', 'Index_Plate_Well', 'index', 'I7_Index_ID', 'index2', 'I5_Index_ID',
# 'Sample_Project', 'Sample_Plate', 'Sample_Well', 'Local Run Manager Analysis Id', 'IEMFileVersion', 'Experiment Name', 'Module', 'Workflow', 'Application', 'Instrument Type', 'Assay',
# 'Index Adapters', 'Library Prep Kit', 'Header.Description', 'Chemistry']
data = data.fillna('-')
return data
def main(samplesheet_file_path, check_only):
logger.info(f"Checking SampleSheet {samplesheet_file_path}")
original_sample_sheet = SampleSheet(samplesheet_file_path)
# Run some consistency checks
years = get_years_from_samplesheet(original_sample_sheet)
logger.info(f"Samplesheet contains IDs from {len(years)} years: {years}")
for year in years:
library_tracking_spreadsheet[year] = get_library_sheet_from_google(
year)
import_library_sheet_validation_from_google()
# TODO: replace has_error return with enum and expand to error, warning, info?
has_header_error = checkSampleSheetMetadata(original_sample_sheet)
has_id_error = checkSampleAndLibraryIdFormat(original_sample_sheet)
has_index_error = checkSampleSheetForIndexClashes(original_sample_sheet)
has_metadata_error = checkMetadataCorrespondence(original_sample_sheet)
# Only fail on metadata or id errors
if has_index_error:
print(
"Index errors detected. Note: the pipeline will ignore those, please make sure to review those errors!"
)
if has_header_error or has_id_error or has_metadata_error:
raise ValueError(
"Pipeline breaking validation detected errors. Please review the error logs!"
)
# Split and write individual SampleSheets, based on indexes and technology (10X)
if not check_only:
# Sort samples based on technology (truseq/10X and/or index length)
# Also replace N indexes with ""
sorted_samples = getSortedSamples(original_sample_sheet)
# Now that the samples have been sorted, we can write one or more custom sample sheets
# (which may be the same as the original if no processing was necessary)
logger.info(f"Writing {len(sorted_samples)} sample sheets.")
writeSammpleSheets(sample_list=sorted_samples,
sheet_path=samplesheet_file_path,
template_sheet=original_sample_sheet)
logger.info("All done.")
'SampleSheet.csv')
else:
logger.info(
"Processing successful run. Using generated sample sheet(s).")
samplesheet_path_pattern = os.path.join(runfolder_base_dir, runfolder,
'SampleSheet.csv.custom.*')
samplesheet_paths = glob(samplesheet_path_pattern)
if len(samplesheet_paths) < 1:
raise ValueError("No sample sheets found!")
logger.info(f"Using {len(samplesheet_paths)} sample sheet(s).")
for samplesheet in samplesheet_paths:
logger.info(f"Processing samplesheet {samplesheet}")
name, extension = os.path.splitext(samplesheet)
samples = SampleSheet(samplesheet).samples
logger.info(f"Found {len(samples)} samples.")
for sample in samples:
logger.debug(
f"Looking up metadata with {sample.Sample_Name} for samplesheet.Sample_ID (UMCCR SampleID); "
+
f"{sample.Sample_ID} and samplesheet.sample_Name (UMCCR LibraryID): {sample.Sample_Name}"
)
column_values = get_meta_data_by_library_id(sample.Sample_Name)
fastq_pattern = os.path.join(bcl2fastq_base_dir, runfolder,
sample.Sample_Project,
sample.Sample_ID,
sample.Sample_Name + "*.fastq.gz")
s3_fastq_pattern = os.path.join(fastq_hpc_base_dir, runfolder,
sample.Sample_Project,
def scan_data(sample_sheet_path):
sample_sheet = SampleSheet(sample_sheet_path)
data = dict(sample_sheet.samples[0]).keys()
return list(data)
def scan_header(sample_sheet_path):
sample_sheet = SampleSheet(sample_sheet_path)
header = pd.DataFrame(list(sample_sheet.Header.values()),
index=list(sample_sheet.Header.keys())).transpose()
return list(header.columns)
