def update_samples_in_release_table(syn, file_mapping, release,
samples_in_release_synid):
"""
Updates the sample in release table
This tracks the samples of each release. 1 means it exists, and 0
means it doesn't
Args:
syn: synapse object
file_mapping: file mapping generated from file mapping function
release: GENIE release number (ie. 5.3-consortium)
samples_in_release_synid: Synapse Id of 'samples in release' Table
"""
clinical_ent = syn.get(file_mapping["clinical"], followLink=True)
clinicaldf = pd.read_csv(clinical_ent.path, sep="\t", comment="#")
cols = [
i["name"] for i in list(syn.getTableColumns(samples_in_release_synid))
]
if release not in cols:
schema = syn.get(samples_in_release_synid)
syn_col = synapseclient.Column(name=release,
columnType="INTEGER",
defaultValue=0)
new_column = syn.store(syn_col)
schema.addColumn(new_column)
schema = syn.store(schema)
# Columns of samples in release
samples_per_release = syn.tableQuery(
'SELECT SAMPLE_ID, "{}" FROM {}'.format(release,
samples_in_release_synid))
samples_per_releasedf = samples_per_release.asDataFrame()
new_samples = clinicaldf[[
"SAMPLE_ID"
]][~clinicaldf.SAMPLE_ID.isin(samples_per_releasedf.SAMPLE_ID)]
new_samples[release] = 1
old_samples = clinicaldf[["SAMPLE_ID"]][clinicaldf.SAMPLE_ID.isin(
samples_per_releasedf.SAMPLE_ID)]
old_samples[release] = 1
samples_in_releasedf = new_samples.append(old_samples)
process_functions.updateDatabase(
syn,
samples_per_releasedf,
samples_in_releasedf,
samples_in_release_synid,
["SAMPLE_ID"],
)
def update_data_completeness_table(syn, database_mappingdf):
"""
Updates the data completeness of the database
Args:
syn: synapse object
database_mappingdf: mapping between synapse ids and database
"""
data_completion_synid = database_mappingdf["Id"][
database_mappingdf["Database"] == "dataCompletion"].values[0]
sample = syn.tableQuery("select * from syn7517674")
sampledf = sample.asDataFrame()
patient = syn.tableQuery("select * from syn7517669")
patientdf = patient.asDataFrame()
data_completenessdf = pd.DataFrame()
center_infos = sampledf.CENTER.drop_duplicates().apply(
lambda center: get_center_data_completion(center, sampledf))
for center_info in center_infos:
data_completenessdf = pd.concat([data_completenessdf, center_info])
center_infos = patientdf.CENTER.drop_duplicates().apply(
lambda center: get_center_data_completion(center, patientdf))
for center_info in center_infos:
data_completenessdf = pd.concat([data_completenessdf, center_info])
data_completeness_db = syn.tableQuery("select * from %s" %
data_completion_synid)
data_completeness_dbdf = data_completeness_db.asDataFrame()
data_completenessdf.columns = data_completeness_dbdf.columns
process_functions.updateDatabase(
syn,
data_completeness_dbdf,
data_completenessdf,
data_completion_synid,
["FIELD", "CENTER"],
to_delete=True,
)
def validation(syn, center, process, center_mapping_df,
databaseToSynIdMappingDf, thread, testing, oncotreeLink):
centerInputSynId = center_mapping_df['inputSynId'][
center_mapping_df['center'] == center][0]
logger.info("Center: " + center)
allFiles = getCenterInputFiles(syn, centerInputSynId, center, process)
allFiles = pd.DataFrame(allFiles, columns=['synId', 'filePaths'])
#If a center has no files, then return empty list
if allFiles.empty:
logger.info("%s has not uploaded any files" % center)
return ([])
else:
#Make sure the vcf validation statuses don't get wiped away
if process != "vcf":
addToQuery = "and name not like '%.vcf'"
else:
addToQuery = ''
validationStatus = syn.tableQuery(
"SELECT * FROM %s where center = '%s' %s" %
(process_functions.getDatabaseSynId(
syn,
"validationStatus",
databaseToSynIdMappingDf=databaseToSynIdMappingDf), center,
addToQuery))
errorTracker = syn.tableQuery(
"SELECT * FROM %s where center = '%s' %s" %
(process_functions.getDatabaseSynId(
syn,
"errorTracker",
databaseToSynIdMappingDf=databaseToSynIdMappingDf), center,
addToQuery))
#VALIDATE FILES
validationStatusDf = validationStatus.asDataFrame()
errorTrackerDf = errorTracker.asDataFrame()
validated = allFiles.apply(
lambda x: validateFile(syn, validationStatusDf, errorTrackerDf,
center, thread, x, testing, oncotreeLink),
axis=1)
inputValidStatus = []
invalidErrors = []
for inputStat, invalErrors in validated:
inputValidStatus.extend(inputStat)
if invalErrors is not None:
invalidErrors.extend(invalErrors)
inputValidStatus = pd.DataFrame(inputValidStatus,
columns=[
"id", 'path', 'md5', 'status',
'name', 'modifiedOn', 'fileType'
])
logger.info("CHECK FOR DUPLICATED FILES")
##### DUPLICATED FILES ######
#check for duplicated filenames. There should be no duplication, files should be uploaded as new versions and the entire dataset should be uploaded everytime
#cbs and seg files should not be duplicated. There can only be one
duplicatedFiles = inputValidStatus[inputValidStatus['name'].duplicated(
keep=False)]
cbsSegBool = [
os.path.basename(i).endswith('.cbs')
or os.path.basename(i).endswith('.seg')
for i in inputValidStatus['name']
]
cbsSegFiles = inputValidStatus[cbsSegBool]
if len(cbsSegFiles) > 1:
duplicatedFiles = duplicatedFiles.append(cbsSegFiles)
# nodups = ["data_mutations_extended"]
# allDuplicatedFiles = []
# for nodup in nodups:
# checkDups = [name for name in inputValidStatus['name'] if name.startswith(nodup)]
# if len(checkDups) > 1:
# allDuplicatedFiles.extend(checkDups)
# duplicatedFiles = duplicatedFiles.append(inputValidStatus[inputValidStatus['name'].isin(allDuplicatedFiles)])
duplicatedFiles.drop_duplicates("id", inplace=True)
inputValidStatus['status'][inputValidStatus['id'].isin(
duplicatedFiles['id'])] = "INVALID"
duplicatedFiles[
'errors'] = "DUPLICATED FILENAME! FILES SHOULD BE UPLOADED AS NEW VERSIONS AND THE ENTIRE DATASET SHOULD BE UPLOADED EVERYTIME"
#Send an email if there are any duplicated files
if not duplicatedFiles.empty:
incorrectFiles = ", ".join([
name for synId, name in zip(duplicatedFiles['id'],
duplicatedFiles['name'])
])
incorrectEnt = syn.get(duplicatedFiles['id'].iloc[0])
sendEmail = set([incorrectEnt.modifiedBy, incorrectEnt.createdBy])
userNames = ", ".join(
[syn.getUserProfile(user).userName for user in sendEmail])
syn.sendMessage(
list(sendEmail), "GENIE Validation Error",
"Dear %s,\n\nYour files (%s) are duplicated! FILES SHOULD BE UPLOADED AS NEW VERSIONS AND THE ENTIRE DATASET SHOULD BE UPLOADED EVERYTIME"
% (userNames, incorrectFiles))
logger.info("THERE ARE %d DUPLICATED FILES" % len(duplicatedFiles))
##### DUPLICATED FILES ######
#Create invalid error synapse table
logger.info("UPDATE INVALID FILE REASON DATABASE")
invalidErrors = pd.DataFrame(invalidErrors,
columns=["id", 'errors', 'name'])
# Remove fixed duplicated files
dupIds = invalidErrors['id'][
invalidErrors['errors'] ==
"DUPLICATED FILENAME! FILES SHOULD BE UPLOADED AS NEW VERSIONS AND THE ENTIRE DATASET SHOULD BE UPLOADED EVERYTIME"]
removeIds = dupIds[~dupIds.isin(duplicatedFiles['id'])]
invalidErrors = invalidErrors[~invalidErrors['id'].isin(removeIds)]
# Append duplicated file errors
invalidErrors = invalidErrors.append(
duplicatedFiles[['id', 'errors', 'name']])
invalidErrors['center'] = center
invalidIds = inputValidStatus['id'][inputValidStatus['status'] ==
"INVALID"]
invalidErrors = invalidErrors[invalidErrors['id'].isin(invalidIds)]
process_functions.updateDatabase(
syn,
errorTracker.asDataFrame(),
invalidErrors,
process_functions.getDatabaseSynId(
syn,
"errorTracker",
databaseToSynIdMappingDf=databaseToSynIdMappingDf), ["id"],
toDelete=True)
paths = inputValidStatus['path']
filenames = [os.path.basename(name) for name in paths]
del inputValidStatus['path']
logger.info("UPDATE VALIDATION STATUS DATABASE")
inputValidStatus['center'] = center
#Remove fixed duplicated files
inputValidStatus = inputValidStatus[~inputValidStatus['id'].
isin(removeIds)]
process_functions.updateDatabase(
syn,
validationStatus.asDataFrame(),
inputValidStatus[[
"id", 'md5', 'status', 'name', 'center', 'modifiedOn'
]],
process_functions.getDatabaseSynId(
syn,
"validationStatus",
databaseToSynIdMappingDf=databaseToSynIdMappingDf), ["id"],
toDelete=True)
inputValidStatus['path'] = paths
validFiles = inputValidStatus[[
'id', 'path', 'fileType'
]][inputValidStatus['status'] == "VALIDATED"]
return (validFiles)
def print_clinical_values_difference_table(syn, database_mappingdf):
"""
Checks for a decrease in values in the clinical file
from last consortium release to most recent consortium release
Args:
syn: synapse object
database_mappingdf: mapping between synapse ids and database
"""
release_folder_fileview_synid = database_mappingdf["Id"][
database_mappingdf["Database"] == "releaseFolder"].values[0]
clinical_key_decrease_synid = database_mappingdf["Id"][
database_mappingdf["Database"] == "clinicalKeyDecrease"].values[0]
release_folder = syn.tableQuery(
f"select id,name from {release_folder_fileview_synid} "
"where name not like 'Release%' and name <> 'case_lists' "
"and name not like '%.0.%' and name not like '%-public' "
"and name <> 'potential_artifacts'")
release_folderdf = release_folder.asDataFrame()
# Set release number as a numerical value since string "10" < "9"
# Also can't set by created on date, because sometimes
# there are patch releases
release_folderdf["num_release"] = [
float(name.replace(".0", "").replace("-consortium", ""))
for name in release_folderdf["name"]
]
release_folderdf.sort_values("num_release", ascending=False, inplace=True)
current_release = release_folderdf["id"][0]
older_release = release_folderdf["id"][1]
current_release_files = syn.getChildren(current_release)
current_clinical_synids = {
file["name"]: file["id"]
for file in current_release_files if file["name"] in
["data_clinical_sample.txt", "data_clinical_patient.txt"]
}
older_release_files = syn.getChildren(older_release)
older_clinical_synids = {
file["name"]: file["id"]
for file in older_release_files if file["name"] in
["data_clinical_sample.txt", "data_clinical_patient.txt"]
}
current_sample_ent = syn.get(
current_clinical_synids["data_clinical_sample.txt"], followLink=True)
older_sample_ent = syn.get(
older_clinical_synids["data_clinical_sample.txt"], followLink=True)
current_sampledf = pd.read_csv(current_sample_ent.path,
sep="\t",
comment="#")
current_sampledf["CENTER"] = [
patient.split("-")[1] for patient in current_sampledf["PATIENT_ID"]
]
older_sampledf = pd.read_csv(older_sample_ent.path, sep="\t", comment="#")
older_sampledf["CENTER"] = [
patient.split("-")[1] for patient in older_sampledf["PATIENT_ID"]
]
# Rather than take the CENTER, must take the SAMPLE_ID to compare
current_sampledf = current_sampledf[current_sampledf["SAMPLE_ID"].isin(
older_sampledf["SAMPLE_ID"].unique())]
logger.info("SAMPLE CLINICAL VALUE DECREASES")
center_decrease_mapping = dict()
for center in older_sampledf["CENTER"].unique():
current_center_sampledf = current_sampledf[current_sampledf["CENTER"]
== center]
older_center_sampledf = older_sampledf[older_sampledf["CENTER"] ==
center]
logger.info(center)
decrease_map = check_column_decreases(current_center_sampledf,
older_center_sampledf)
center_decrease_mapping[center] = decrease_map
current_patient_ent = syn.get(
current_clinical_synids["data_clinical_patient.txt"], followLink=True)
older_patient_ent = syn.get(
older_clinical_synids["data_clinical_patient.txt"], followLink=True)
current_patientdf = pd.read_csv(current_patient_ent.path,
sep="\t",
comment="#")
older_patientdf = pd.read_csv(older_patient_ent.path,
sep="\t",
comment="#")
# Rather than take the CENTER, must take the PATIENT_ID to compare
current_patientdf = current_patientdf[current_patientdf["PATIENT_ID"].isin(
older_patientdf["PATIENT_ID"].unique())]
logger.info("PATIENT CLINICAL VALUE DECREASES")
for center in older_patientdf["CENTER"].unique():
current_center_patientdf = current_patientdf[
current_patientdf["CENTER"] == center]
older_center_patientdf = older_patientdf[older_patientdf["CENTER"] ==
center]
logger.info(center)
patient_decrease_map = check_column_decreases(current_center_patientdf,
older_center_patientdf)
center_decrease_mapping[center].update(patient_decrease_map)
center_decrease_mapping = pd.DataFrame(center_decrease_mapping)
center_decrease_mapping = center_decrease_mapping.transpose()
center_decrease_mapping["CENTER"] = center_decrease_mapping.index
clinical_key_decrease = syn.tableQuery(
"select * from {0}".format(clinical_key_decrease_synid))
clinical_key_decreasedbdf = clinical_key_decrease.asDataFrame()
process_functions.updateDatabase(
syn,
clinical_key_decreasedbdf,
center_decrease_mapping,
clinical_key_decrease_synid,
["CENTER"],
to_delete=True,
)
def update_sample_difference_table(syn, database_mappingdf):
"""
Updates sample difference table between
consortium releases
Args:
syn: synapse object
database_mappingdf: mapping between synapse ids and database
"""
cumulative_sample_count_synid = database_mappingdf["Id"][
database_mappingdf["Database"] == "cumulativeSampleCount"].values[0]
sample_diff_count_synid = database_mappingdf["Id"][
database_mappingdf["Database"] == "sampleDiffCount"].values[0]
# UPDATE DIFF TABLE
sample_count_per_round = syn.tableQuery(
"SELECT * FROM %s where Center <> 'Total' and Release <> 'Database'" %
cumulative_sample_count_synid)
sample_count_per_rounddf = sample_count_per_round.asDataFrame()
releases = list(sample_count_per_rounddf["Release"].unique())
# sort the releases and remove public releases
releases.sort()
consortium_releases = [
release for release in releases
if "public" not in release and ".0." not in release
]
diff_between_releasesdf = sample_count_per_rounddf[
sample_count_per_rounddf["Release"] == consortium_releases[0]]
for index, release_name in enumerate(consortium_releases[1:]):
prior_release = sample_count_per_rounddf[
sample_count_per_rounddf["Release"] == consortium_releases[index]]
current_release = sample_count_per_rounddf[
sample_count_per_rounddf["Release"] == release_name]
prior_release.index = prior_release["Center"]
current_release.index = current_release["Center"]
del prior_release["Center"]
del prior_release["Release"]
del current_release["Center"]
del current_release["Release"]
# Append new rows of centers that are new and
# just added to the releases
new_centers = current_release.index[~current_release.index.
isin(prior_release.index)]
if not new_centers.empty:
prior_release = pd.concat(
[prior_release, pd.DataFrame(index=new_centers)])
prior_release = prior_release.fillna(0)
difference = current_release - prior_release
difference["Center"] = difference.index
difference["Release"] = release_name
diff_between_releasesdf = pd.concat(
[diff_between_releasesdf, difference])
difftable_db = syn.tableQuery("SELECT * FROM %s" % sample_diff_count_synid)
difftable_dbdf = difftable_db.asDataFrame()
difftable_dbdf = difftable_dbdf.fillna(0)
new_values = (diff_between_releasesdf[[
"Clinical", "Mutation", "CNV", "SEG", "Fusions"
]].fillna(0).applymap(int))
diff_between_releasesdf[["Clinical", "Mutation", "CNV", "SEG",
"Fusions"]] = new_values
process_functions.updateDatabase(
syn,
difftable_dbdf,
diff_between_releasesdf,
sample_diff_count_synid,
["Center", "Release"],
to_delete=True,
)
def update_oncotree_code_tables(syn, database_mappingdf):
"""
Updates database statistics of oncotree codes
and primary onocotree codes
Args:
syn: synapse object
database_mappingdf: mapping between synapse ids and database
"""
oncotree_distribution_synid = database_mappingdf["Id"][
database_mappingdf["Database"] == "oncotree"].values[0]
clinical = syn.tableQuery("select * from syn7517674")
clinicaldf = clinical.asDataFrame()
# DISTRIBUTION OF ONCOTREE CODE TABLE UPDATE
oncotree_code_distributiondf = pd.DataFrame(
columns=set(clinicaldf["CENTER"]),
index=set(clinicaldf["ONCOTREE_CODE"]))
for center in oncotree_code_distributiondf.columns:
onc_counts = clinicaldf["ONCOTREE_CODE"][clinicaldf["CENTER"] ==
center].value_counts()
oncotree_code_distributiondf[center] = onc_counts
oncotree_code_distributiondf = oncotree_code_distributiondf.fillna(0)
oncotree_code_distributiondf = oncotree_code_distributiondf.applymap(int)
oncotree_code_distributiondf["Total"] = oncotree_code_distributiondf.apply(
sum, axis=1)
oncotree_code_distributiondf[
"Oncotree_Code"] = oncotree_code_distributiondf.index
oncotree_distribution_db = syn.tableQuery("SELECT %s FROM %s" % (
"Oncotree_Code," + ",".join(clinicaldf["CENTER"].unique()) + ",Total",
oncotree_distribution_synid,
))
oncotree_distribution_dbdf = oncotree_distribution_db.asDataFrame()
process_functions.updateDatabase(
syn,
oncotree_distribution_dbdf,
oncotree_code_distributiondf,
oncotree_distribution_synid,
["Oncotree_Code"],
to_delete=True,
)
# DISTRIBUTION OF PRIMARY CODE TABLE UPDATE
oncotree_link_synid = database_mappingdf["Id"][
database_mappingdf["Database"] == "oncotreeLink"].values[0]
primary_code_synId = database_mappingdf["Id"][
database_mappingdf["Database"] == "primaryCode"].values[0]
# Can also use most up to date oncotree code,
# because these tables are updated from the database
oncotree_link_ent = syn.get(oncotree_link_synid)
oncotree_link = oncotree_link_ent.externalURL
oncotree_mapping = process_functions.get_oncotree_code_mappings(
oncotree_link)
clinicaldf["PRIMARY_CODES"] = [
oncotree_mapping[i.upper()]["ONCOTREE_PRIMARY_NODE"]
if i.upper() in oncotree_mapping.keys() else "DEPRECATED_CODE"
for i in clinicaldf.ONCOTREE_CODE
]
# ### DISTRIBUTION OF PRIMARY ONCOTREE CODE TABLE UPDATE
primary_code_distributiondf = pd.DataFrame(
columns=set(clinicaldf["CENTER"]),
index=set(clinicaldf["PRIMARY_CODES"]))
for center in primary_code_distributiondf.columns:
onc_counts = clinicaldf["PRIMARY_CODES"][clinicaldf["CENTER"] ==
center].value_counts()
primary_code_distributiondf[center] = onc_counts
primary_code_distributiondf = primary_code_distributiondf.fillna(0)
primary_code_distributiondf = primary_code_distributiondf.applymap(int)
primary_code_distributiondf["Total"] = primary_code_distributiondf.apply(
sum, axis=1)
primary_code_distributiondf[
"Oncotree_Code"] = primary_code_distributiondf.index
primary_code_dist_db = syn.tableQuery("SELECT %s FROM %s" % (
"Oncotree_Code," + ",".join(clinicaldf["CENTER"].unique()) + ",Total",
primary_code_synId,
))
primary_code_dist_dbdf = primary_code_dist_db.asDataFrame()
process_functions.updateDatabase(
syn,
primary_code_dist_dbdf,
primary_code_distributiondf,
primary_code_synId,
["Oncotree_Code"],
to_delete=True,
)
def update_database_numbers(syn, database_mappingdf):
"""
Updates database cumulative numbers (Only called when not staging)
Args:
syn: synapse object
database_mappingdf: mapping between synapse ids and database
"""
cumulative_sample_count_synid = database_mappingdf["Id"][
database_mappingdf["Database"] == "cumulativeSampleCount"].values[0]
# Database
database_count = syn.tableQuery(
"SELECT * FROM %s where Release = 'Database'" %
cumulative_sample_count_synid)
database_countdf = database_count.asDataFrame()
clinical = syn.tableQuery("select CENTER from syn7517674")
clinicaldf = clinical.asDataFrame()
clinincal_counts = clinicaldf["CENTER"].value_counts()
clinincal_counts["Total"] = sum(clinincal_counts)
clinincal_counts.name = "Clinical"
fusion = syn.tableQuery("select * from syn7893268")
fusiondf = fusion.asDataFrame()
fusion_counts = fusiondf["CENTER"][~fusiondf["TUMOR_SAMPLE_BARCODE"].
duplicated()].value_counts()
fusion_counts["Total"] = sum(fusion_counts)
center_flat_files = syn.getChildren("syn12278118")
cna_file_paths = [
syn.get(file["id"]).path for file in center_flat_files
if file["name"].startswith("data_CNA")
]
cna_numbers = {}
for cna_file in cna_file_paths:
center = os.path.basename(cna_file).replace(".txt", "").split("_")[2]
with open(cna_file, "r") as cna:
header = cna.readline()
samples = header.split("\t")
# Minus one because of Hugo_Symbol
cna_numbers[center] = len(samples) - 1
cna_counts = pd.Series(cna_numbers)
cna_counts["Total"] = sum(cna_counts)
seg = syn.tableQuery("select * from syn7893341")
segdf = seg.asDataFrame()
seg_counts = segdf["CENTER"][~segdf["ID"].duplicated()].value_counts()
seg_counts["Total"] = sum(seg_counts)
db_counts = pd.DataFrame(clinincal_counts)
db_counts["Fusions"] = fusion_counts
db_counts["CNV"] = cna_counts
db_counts["Mutation"] = clinincal_counts
db_counts["SEG"] = seg_counts
db_counts = db_counts.fillna(0)
db_counts = db_counts.applymap(int)
db_counts["Center"] = db_counts.index
db_counts["Release"] = "Database"
process_functions.updateDatabase(
syn,
database_countdf,
db_counts,
cumulative_sample_count_synid,
["Center", "Release"],
)
today = datetime.date.today()
if today.month in [1, 4, 8, 12]:
db_count_tracker = db_counts[["Clinical", "Center", "Release"]]
db_count_tracker.rename(
columns={
"Clinical": "sample_count",
"Center": "center",
"Release": "date"
},
inplace=True,
)
db_count_tracker["date"] = today.strftime("%b-%Y")
# Hard coded syn id
syn.store(synapseclient.Table("syn18404852", db_count_tracker))
def update_cumulative_sample_table(syn, file_mapping, release,
cumulative_sample_count_synid):
"""
Consortium release sample count table update function
This gets the cumulative sample count of each file type in each release
Args:
syn: synapse object
file_mapping: file mapping generated from file mapping function
release: GENIE release number (ie. 5.3-consortium)
cumulative_sample_count_synid: Synapse Id of
'Cumulative sample count' Table
"""
sample_count_per_round = syn.tableQuery(
"SELECT * FROM {} where Release = '{}'".format(
cumulative_sample_count_synid, release))
sample_count_per_rounddf = sample_count_per_round.asDataFrame()
clinical_ent = syn.get(file_mapping["clinical"], followLink=True)
clinicaldf = pd.read_csv(clinical_ent.path, sep="\t", comment="#")
clinicaldf.columns = [i.upper() for i in clinicaldf.columns]
if clinicaldf.get("CENTER") is None:
clinicaldf["CENTER"] = [
sample.split("-")[1] for sample in clinicaldf.SAMPLE_ID
]
clinical_counts = clinicaldf["CENTER"].value_counts()
clinical_counts["Total"] = sum(clinical_counts)
clinical_counts.name = "Clinical"
fusion_ent = syn.get(file_mapping["fusion"], followLink=True)
fusiondf = pd.read_csv(fusion_ent.path, sep="\t", comment="#")
fusiondf.columns = [i.upper() for i in fusiondf.columns]
fusion_counts = fusiondf["CENTER"][~fusiondf["TUMOR_SAMPLE_BARCODE"].
duplicated()].value_counts()
fusion_counts["Total"] = sum(fusion_counts)
cna_ent = syn.get(file_mapping["cna"], followLink=True)
cnadf = pd.read_csv(cna_ent.path, sep="\t", comment="#")
cna_counts = pd.Series([i.split("-")[1]
for i in cnadf.columns[1:]]).value_counts()
cna_counts["Total"] = sum(cna_counts)
seg_ent = syn.get(file_mapping["seg"], followLink=True)
segdf = pd.read_csv(seg_ent.path, sep="\t", comment="#")
segdf.columns = [i.upper() for i in segdf.columns]
segdf["CENTER"] = [i.split("-")[1] for i in segdf["ID"]]
seg_counts = segdf["CENTER"][~segdf["ID"].duplicated()].value_counts()
seg_counts["Total"] = sum(seg_counts)
total_counts = pd.DataFrame(clinical_counts)
total_counts["Fusions"] = fusion_counts
total_counts["CNV"] = cna_counts
total_counts["Mutation"] = clinical_counts
total_counts["SEG"] = seg_counts
total_counts = total_counts.fillna(0)
total_counts = total_counts.applymap(int)
total_counts["Center"] = total_counts.index
total_counts["Release"] = release
process_functions.updateDatabase(
syn,
sample_count_per_rounddf,
total_counts,
cumulative_sample_count_synid,
["Center", "Release"],
to_delete=True,
)
