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403 | class Assayinfo(FileTypeFormat):
"""Assay information file type"""
_fileType = "assayinfo"
_process_kwargs = ["newPath", "databaseSynId"]
_validation_kwargs = ["project_id"]
def _validateFilename(self, filepath_list):
"""Validate assay information filename"""
assert os.path.basename(filepath_list[0]) == "assay_information.yaml"
def process_steps(self, assay_info_df, newPath, databaseSynId):
"""
Process bed input and update bed database
Args:
assay_info_df: Assay information dataframe
newPath: Path to processed assay information
databaseSynId: assay information database synapse id
Returns:
path to assay information dataframe
"""
# Must pass in a list
process_assay_info_df = self._process(assay_info_df)
load.update_table(
syn=self.syn,
databaseSynId=databaseSynId,
newData=process_assay_info_df,
filterBy=self.center,
toDelete=True,
)
process_assay_info_df.to_csv(newPath, sep="\t", index=False)
return newPath
def _process(self, df):
"""
Process assay_information.yaml. Standardizes SEQ_ASSAY_ID,
default 10 for gene_padding, and fills in variant_classifications
Args:
df: Assay information dataframe
Returns:
dataframe: Processed dataframe
"""
seq_assay_ids = [
assay.upper().replace("_", "-") for assay in df["SEQ_ASSAY_ID"]
]
df["SEQ_ASSAY_ID"] = seq_assay_ids
df["SEQ_PIPELINE_ID"] = [
assay.upper().replace("_", "-") for assay in df["SEQ_PIPELINE_ID"]
]
if process_functions.checkColExist(df, "gene_padding"):
df["gene_padding"] = df["gene_padding"].fillna(10)
df["gene_padding"] = df["gene_padding"].astype(int)
else:
df["gene_padding"] = 10
if not process_functions.checkColExist(df, "variant_classifications"):
df["variant_classifications"] = float("nan")
df["CENTER"] = self.center
return df
def _get_dataframe(self, filepath_list):
"""Take in yaml file, returns dataframe"""
filepath = filepath_list[0]
try:
with open(filepath, "r") as yamlfile:
# https://github.com/yaml/pyyaml/wiki/PyYAML-yaml.load(input)-Deprecation
# Must add this because yaml load deprecation
assay_info_dict = yaml.safe_load(yamlfile)
except Exception:
raise ValueError(
"assay_information.yaml: Can't read in your file. "
"Please make sure the file is a correctly formatted yaml"
)
# assay_info_df = pd.DataFrame(panel_info_dict)
# assay_info_df = assay_info_df.transpose()
# assay_info_df['SEQ_ASSAY_ID'] = assay_info_df.index
# assay_info_df.reset_index(drop=True, inplace=True)
assay_infodf = pd.DataFrame(assay_info_dict)
assay_info_transposeddf = assay_infodf.transpose()
all_panel_info = pd.DataFrame()
for assay in assay_info_dict:
assay_specific_info = assay_info_dict[assay]["assay_specific_info"]
assay_specific_infodf = pd.DataFrame(assay_specific_info)
intial_seq_id_infodf = assay_info_transposeddf.loc[[assay]]
# make sure to create a skeleton for the number of seq assay ids
# in the seq pipeline
seq_assay_id_infodf = pd.concat(
[intial_seq_id_infodf] * len(assay_specific_info)
)
seq_assay_id_infodf.reset_index(drop=True, inplace=True)
assay_finaldf = pd.concat(
[assay_specific_infodf, seq_assay_id_infodf], axis=1
)
del assay_finaldf["assay_specific_info"]
# Transform values containing lists to string concatenated values
columns_containing_lists = [
"variant_classifications",
"alteration_types",
"preservation_technique",
"coverage",
]
for col in columns_containing_lists:
if assay_finaldf.get(col) is not None:
assay_finaldf[col] = [";".join(row) for row in assay_finaldf[col]]
assay_finaldf["SEQ_PIPELINE_ID"] = assay
all_panel_info = pd.concat([all_panel_info, assay_finaldf])
return all_panel_info
def _validate(self, assay_info_df, project_id):
"""
Validates the values of assay information file
Args:
assay_info_df: assay information dataframe
Returns:
tuple: error and warning
"""
total_error = ""
warning = ""
if process_functions.checkColExist(assay_info_df, "SEQ_ASSAY_ID"):
all_seq_assays = (
assay_info_df.SEQ_ASSAY_ID.replace({"_": "-"}, regex=True)
.str.upper()
.unique()
)
if not all([assay.startswith(self.center) for assay in all_seq_assays]):
total_error += (
"Assay_information.yaml: Please make sure all your "
"SEQ_ASSAY_IDs start with your center abbreviation.\n"
)
uniq_seq_df = extract.get_syntabledf(
self.syn,
f"select distinct(SEQ_ASSAY_ID) as seq from {self.genie_config['sample']} "
f"where CENTER = '{self.center}'",
)
# These are all the SEQ_ASSAY_IDs that are in the clinical database
# but not in the assay_information file
missing_seqs = uniq_seq_df["seq"][
~uniq_seq_df["seq"]
.replace({"_": "-"}, regex=True)
.str.upper()
.isin(all_seq_assays)
]
missing_seqs_str = ", ".join(missing_seqs)
if missing_seqs.to_list():
total_error += (
"Assay_information.yaml: You are missing SEQ_ASSAY_IDs: "
f"{missing_seqs_str}\n"
)
else:
total_error += "Assay_information.yaml: Must have SEQ_ASSAY_ID column.\n"
read_group_dict = process_functions.get_gdc_data_dictionary("read_group")
read_group_headers = read_group_dict["properties"]
warn, error = process_functions.check_col_and_values(
assay_info_df,
"is_paired_end",
[True, False],
filename="Assay_information.yaml",
required=True,
)
warning += warn
total_error += error
warn, error = process_functions.check_col_and_values(
assay_info_df,
"library_selection",
read_group_headers["library_selection"]["enum"],
filename="Assay_information.yaml",
required=True,
)
warning += warn
total_error += error
warn, error = process_functions.check_col_and_values(
assay_info_df,
"library_strategy",
read_group_headers["library_strategy"]["enum"],
filename="Assay_information.yaml",
required=True,
)
warning += warn
total_error += error
warn, error = process_functions.check_col_and_values(
assay_info_df,
"platform",
read_group_headers["platform"]["enum"],
filename="Assay_information.yaml",
required=True,
)
warning += warn
total_error += error
instrument_model = read_group_headers["instrument_model"]["enum"]
instrument_model.extend(["Illumina NovaSeq 6000", None])
warn, error = process_functions.check_col_and_values(
assay_info_df,
"instrument_model",
instrument_model,
filename="Assay_information.yaml",
required=True,
)
warning += warn
total_error += error
# target_capture_kit = read_group_headers['target_capture_kit']['enum']
# warn, error = process_functions.check_col_and_values(
# assay_info_df,
# 'target_capture_kit',
# target_capture_kit,
# filename="Assay_information.yaml",
# required=True)
# warning += warn
# total_error += error
if not process_functions.checkColExist(assay_info_df, "target_capture_kit"):
total_error += (
"Assay_information.yaml: " "Must have target_capture_kit column.\n"
)
variant_classes = [
"Splice_Site",
"Nonsense_Mutation",
"Frame_Shift_Del",
"Frame_Shift_Ins",
"Nonstop_Mutation",
"Translation_Start_Site",
"In_Frame_Ins",
"In_Frame_Del",
"Missense_Mutation",
"Intron",
"Splice_Region",
"Silent",
"RNA",
"5'UTR",
"3'UTR",
"IGR",
"5'Flank",
"3'Flank",
None,
]
warn, error = process_functions.check_col_and_values(
assay_info_df,
"variant_classifications",
variant_classes,
filename="Assay_information.yaml",
na_allowed=True,
sep=";",
)
warning += warn
total_error += error
if process_functions.checkColExist(assay_info_df, "read_length"):
if not all(
[
process_functions.checkInt(i)
for i in assay_info_df["read_length"]
if i is not None and not pd.isnull(i)
]
):
total_error += (
"Assay_information.yaml: "
"Please double check your read_length. "
"It must be an integer or null.\n"
)
else:
total_error += "Assay_information.yaml: " "Must have read_length column.\n"
if process_functions.checkColExist(assay_info_df, "number_of_genes"):
if not all(
[
process_functions.checkInt(i)
for i in assay_info_df["number_of_genes"]
]
):
total_error += (
"Assay_information.yaml: "
"Please double check your number_of_genes. "
"It must be an integer.\n"
)
else:
total_error += (
"Assay_information.yaml: " "Must have number_of_genes column.\n"
)
if process_functions.checkColExist(assay_info_df, "gene_padding"):
if not all(
[
process_functions.checkInt(i)
for i in assay_info_df["gene_padding"]
if i is not None and not pd.isnull(i)
]
):
total_error += (
"Assay_information.yaml: "
"Please double check your gene_padding. "
"It must be an integer or blank.\n"
)
else:
warning += (
"Assay_information.yaml: "
"gene_padding is by default 10 if not specified.\n"
)
warn, error = process_functions.check_col_and_values(
assay_info_df,
"calling_strategy",
["tumor_only", "tumor_normal", "plasma_normal"],
filename="Assay_information.yaml",
required=True,
)
warning += warn
total_error += error
if process_functions.checkColExist(assay_info_df, "specimen_tumor_cellularity"):
if not all(
[
i.startswith(">") and i.endswith("%")
for i in assay_info_df["specimen_tumor_cellularity"]
]
):
total_error += (
"Assay_information.yaml: "
"Please double check your specimen_tumor_cellularity. "
"It must in this format >(num)%. ie. >10%\n"
)
else:
total_error += (
"Assay_information.yaml: "
"Must have specimen_tumor_cellularity column.\n"
)
alteration_types = [
"snv",
"small_indels",
"gene_level_cna",
"intragenic_cna",
"structural_variants",
]
warn, error = process_functions.check_col_and_values(
assay_info_df,
"alteration_types",
alteration_types,
filename="Assay_information.yaml",
required=True,
sep=";",
)
warning += warn
total_error += error
preservation_technique = ["FFPE", "fresh_frozen", "NA"]
warn, error = process_functions.check_col_and_values(
assay_info_df,
"preservation_technique",
preservation_technique,
filename="Assay_information.yaml",
required=True,
sep=";",
)
warning += warn
total_error += error
coverage = ["hotspot_regions", "coding_exons", "introns", "promoters"]
warn, error = process_functions.check_col_and_values(
assay_info_df,
"coverage",
coverage,
filename="Assay_information.yaml",
required=True,
sep=";",
)
warning += warn
total_error += error
return total_error, warning
|