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enable non-coding variants
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@pnrobinson
pnrobinson committed Oct 13, 2024
1 parent 169b30f commit 6172831
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Showing 4 changed files with 87 additions and 78 deletions.
2 changes: 1 addition & 1 deletion src/pyphetools/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@
from . import validation


__version__ = "0.9.109"
__version__ = "0.9.110"


__all__ = [
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66 changes: 34 additions & 32 deletions src/pyphetools/creation/import_template.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,9 +14,9 @@ class TemplateImporter:

ORCID_regex = r"^\d{4}-\d{4}-\d{4}-\d{4}$"

def __init__(self,template:str,
created_by:str,
hp_json:str=None) -> None:
def __init__(self, template: str,
created_by: str,
hp_json: str = None) -> None:
"""Constructor
:param template: path to Excel template file
Expand Down Expand Up @@ -46,7 +46,7 @@ def __init__(self,template:str,
self._moi_list = list()

@staticmethod
def _get_data_from_template(df:pd.DataFrame) -> typing.Tuple[str,str,str,str,str]:
def _get_data_from_template(df: pd.DataFrame) -> typing.Tuple[str, str, str, str, str]:
"""Check that the template (dataframe) has the columns
"HGNC_id", "gene_symbol", "transcript",
If each row has the same value for these columns, then the template is valid
Expand All @@ -62,7 +62,7 @@ def _get_data_from_template(df:pd.DataFrame) -> typing.Tuple[str,str,str,str,str
if item not in df.columns:
raise ValueError(f"Invalid template -- could not find the \"{item}\" column")
## We need to skip the first row (second row of excel file), which has the datatypes
for _, row in df.iloc[1: , :].iterrows():
for _, row in df.iloc[1:, :].iterrows():
contents_d["disease_id"].add(row["disease_id"])
contents_d["disease_label"].add(row["disease_label"])
contents_d["HGNC_id"].add(row["HGNC_id"])
Expand All @@ -84,7 +84,7 @@ def _get_data_from_template(df:pd.DataFrame) -> typing.Tuple[str,str,str,str,str
return disease_id, disease_label, HGNC_id, gene_symbol, transcript

@staticmethod
def _get_allelic_requirement(df:pd.DataFrame):
def _get_allelic_requirement(df: pd.DataFrame):
"""Determine allelic requirement
Note that we always expect the column allele_1 to have content.
If each row of allele_2 is "na", then the allelic requirement is MONO_ALLELIC
Expand All @@ -99,7 +99,7 @@ def _get_allelic_requirement(df:pd.DataFrame):
from pyphetools.creation import AllelicRequirement
total_row_count = 0
total_allele_2_na_count = 0
for _, row in df.iloc[1: , :].iterrows():
for _, row in df.iloc[1:, :].iterrows():
a1 = row["allele_1"]
a2 = row["allele_2"]
if a1 == "na":
Expand All @@ -116,15 +116,13 @@ def _get_allelic_requirement(df:pd.DataFrame):
else:
raise ValueError(f"Error: {total_allele_2_na_count} rows with two alleles but {total_row_count} total rows")



def import_phenopackets_from_template(self,
deletions:typing.Set[str]=set(),
duplications:typing.Set[str]=set(),
inversions:typing.Set[str]=set(),
translocations:typing.Set[str]=set(),
hemizygous:bool=False,
leniant_MOI:bool=False):
deletions: typing.Set[str] = set(),
duplications: typing.Set[str] = set(),
inversions: typing.Set[str] = set(),
translocations: typing.Set[str] = set(),
hemizygous: bool = False,
leniant_MOI: bool = False):
"""Import the data from an Excel template and create a collection of Phenopackets
This method writes the individuals as Phenopackets to file and also returns Individuals and the CValidator.
ToDo -- refactor to avoid side effects.
Expand All @@ -147,7 +145,7 @@ def import_phenopackets_from_template(self,
:returns: tuple with individual list and CohortValidator that optionally can be used to display in a notebook
:rtype: typing.Tuple[typing.List[pyphetools.creation.Individual], pyphetools.validation.CohortValidator]
"""
from pyphetools.creation import HpoParser
from pyphetools.creation import HpoParser
from pyphetools.creation import CaseTemplateEncoder
from pyphetools.creation import VariantManager
from pyphetools.validation import CohortValidator
Expand All @@ -161,11 +159,11 @@ def import_phenopackets_from_template(self,
disease_id, disease_label, HGNC_id, gene_symbol, transcript = TemplateImporter._get_data_from_template(df)
print(f"Importing {disease_id}, {disease_label}, {HGNC_id}, {gene_symbol}, {transcript}")
vman = VariantManager(df=df, individual_column_name="individual_id",
allele_1_column_name="allele_1",
allele_2_column_name="allele_2",
gene_id=HGNC_id,
gene_symbol=gene_symbol,
transcript=transcript)
allele_1_column_name="allele_1",
allele_2_column_name="allele_2",
gene_id=HGNC_id,
gene_symbol=gene_symbol,
transcript=transcript)
if len(deletions) > 0:
vman.code_as_chromosomal_deletion(deletions)
if len(duplications) > 0:
Expand All @@ -177,12 +175,18 @@ def import_phenopackets_from_template(self,
if vman.has_unmapped_alleles():
mapped = vman.get_mapped_allele_count()
print(f"We were able to map {mapped} alleles.")
print("The following alleles could not be mapped. Either there is an error or the variants are structural and require special treatment (see documentation)")
print("The following alleles could not be mapped.")
print("Either there is an error or the variants are structural and require special treatment.")
struct_vars = list()
for uma in vman.get_unmapped_alleles():
if uma.startswith("c."):
print(f"- {transcript}:{uma}")
else:
print(f"- {uma} (may require coding as structural variant)")
struct_vars.append(uma)
if len(struct_vars) > 0:
print("The following may require coding as a structural variant)")
for struct_var in struct_vars:
print(struct_var)
print("Fix this error and then try again!")
sys.exit(1)
vman.add_variants_to_individuals(individuals, hemizygous=hemizygous)
Expand All @@ -193,17 +197,16 @@ def import_phenopackets_from_template(self,
cvalidator = CohortValidator(cohort=individuals, ontology=hpo_ontology, min_hpo=1)
else:
all_req = TemplateImporter._get_allelic_requirement(df)
cvalidator = CohortValidator(cohort=individuals, ontology=hpo_ontology, min_hpo=1, allelic_requirement=all_req)
cvalidator = CohortValidator(cohort=individuals, ontology=hpo_ontology, min_hpo=1,
allelic_requirement=all_req)
if cvalidator.n_removed_individuals() > 0:
print(f"Removed {cvalidator.n_removed_individuals()} individuals with unfixable errors")
ef_individuals = cvalidator.get_error_free_individual_list()
encoder.output_individuals_as_phenopackets(individual_list=ef_individuals)
return individuals, cvalidator



@staticmethod
def check_disease_entries(ppkt_list:typing.List[PPKt.Phenopacket]) -> None:
def check_disease_entries(ppkt_list: typing.List[PPKt.Phenopacket]) -> None:
disease_count_d = defaultdict(int)
for ppkt in ppkt_list:
# Each phenopacket must have one disease
Expand Down Expand Up @@ -245,12 +248,11 @@ def filter_diseases(disease_id, ppkt_list):
print(f"[INFO] Extracted {(len(target_list))} from {(len(ppkt_list))} phenopackets with {disease_id}\n")
return target_list


def create_hpoa_from_phenopackets(self,
pmid:str,
mode_of_inheritance:Moi,
ppkt_dir:str="phenopackets",
target:str=None) -> pd.DataFrame:
pmid: str,
mode_of_inheritance: Moi,
ppkt_dir: str = "phenopackets",
target: str = None) -> pd.DataFrame:
"""Create an HPO annotation (HPOA) file from the current cohort
:param pmid: PubMed id for the mode of inheritance
Expand Down
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