def get_subject_dats_material(cache, p_subject, gh_subject, var_lookup):
subj_id = p_subject['SUBJID']['mapped_value']
# retrieve id reference for the Identifier of the DATS Dimension for the "all subjects" consent group version of the variable
def get_var_id(name):
return var_lookup[name]['dim'].get("identifier").getIdRef()
# human experimental subject/patient
subject_sex = DatsObj(
"Dimension", [("name", util.get_value_annotation("Gender", cache)),
("description", "Gender of the subject"),
("identifier", get_var_id("SEX")),
("values", [p_subject['SEX']['mapped_value']])])
subject_age = DatsObj(
"Dimension", [("name", util.get_value_annotation("Age range", cache)),
("description", "Age range of the subject"),
("identifier", get_var_id("AGE")),
("values", [p_subject['AGE']['mapped_value']])])
subject_hardy_scale = DatsObj(
"Dimension",
[("name", util.get_value_annotation("Hardy scale", cache)),
("description", "Hardy scale death classification for the subject"),
("identifier", get_var_id("DTHHRDY")),
("values", [p_subject['DTHHRDY']['mapped_value']])])
subject_characteristics = [subject_sex, subject_age, subject_hardy_scale]
# use URI from GTEx id dump if present
identifier = subj_id
if gh_subject is not None:
identifier = gh_subject['Destination URL']['raw_value']
# human experimental subject/patient
subject_material = DatsObj(
"Material",
[("name", subj_id),
("identifier", DatsObj("Identifier", [("identifier", identifier)])),
("description", "GTEx subject " + subj_id),
("characteristics", subject_characteristics),
("taxonomy", [util.get_taxon_human(cache)]),
("roles", util.get_donor_roles(cache))])
# add to the cache
subj_key = ":".join(["Material", subj_id])
dats_subj = cache.get_obj_or_ref(subj_key, lambda: subject_material)
return dats_subj
def get_single_sample_json(sample, dats_obj_cache):
# print("converting sample to json: " + str(sample))
samp_id = sample['SAMPID']['mapped_value']
subj_id = sample['SUBJID']['mapped_value']
subject = sample['subject']
# Uberon id (or EFO id, contrary to the documentation)
anat_id = sample['SMUBRID']['mapped_value']
if anat_id is None:
print("No Uberon/anatomy ID specified for sample " + samp_id)
sys.exit(1)
anatomy_identifier = None
anatomy_alt_ids = None
# TODO - query anatomy term from UBERON/EFO instead?
anatomy_name = sample['SMTSD']['mapped_value']
# EFO id
if re.match(r'^EFO_\d+', anat_id):
anatomy_identifier = OrderedDict([("identifier", anat_id),
("identifierSource", "EFO")])
anatomy_alt_ids = [
OrderedDict([
("identifier",
"https://www.ebi.ac.uk/ols/ontologies/efo/terms?short_form=" +
str(anat_id)), ("identifierSource", "EFO")
])
]
# Uberon id
else:
anatomy_identifier = OrderedDict([("identifier",
"UBERON:" + str(anat_id)),
("identifierSource", "UBERON")])
anatomy_alt_ids = [
OrderedDict([
("identifier",
"http://purl.obolibrary.org/obo/UBERON_" + str(anat_id)),
("identifierSource", "UBERON")
])
]
# anatomical part
anat_part_key = ":".join(["AnatomicalPart", anatomy_name])
if anat_part_key in dats_obj_cache:
anatomical_part = dats_obj_cache[anat_part_key]
else:
anatomical_part = DatsObj("AnatomicalPart",
[("name", anatomy_name),
("identifier", anatomy_identifier),
("alternateIdentifiers", anatomy_alt_ids)])
dats_obj_cache[anat_part_key] = anatomical_part
# human experimental subject/patient
subject_sex = DatsObj(
"Dimension", [("name", DatsObj("Annotation", [("value", "Gender")])),
("description", "Gender of the subject"),
("identifier",
DatsObj("Identifier", [("identifier", "SEX"),
("identifierSource", "GTEx")])),
("values", [subject['SEX']['mapped_value']])])
subject_age = DatsObj(
"Dimension",
[("name", DatsObj("Annotation", [("value", "Age range")])),
("description", "Age range of the subject"),
("identifier",
DatsObj("Identifier", [("identifier", "AGE"),
("identifierSource", "GTEx")])),
("values", [subject['AGE']['mapped_value']])])
subject_hardy_scale = DatsObj(
"Dimension",
[("name", DatsObj("Annotation", [("value", "Hardy scale")])),
("description", "Hardy scale death classification for the subject"),
("identifier",
DatsObj("Identifier", [("identifier", "DTHHRDY"),
("identifierSource", "GTEx")])),
("values", [subject['DTHHRDY']['mapped_value']])])
subject_characteristics = [subject_sex, subject_age, subject_hardy_scale]
# human experimental subject/patient
subj_key = ":".join(["Material", subj_id])
if subj_key in dats_obj_cache:
subject_material = dats_obj_cache[subj_key]
else:
subject_material = DatsObj(
"Material", [("name", subj_id),
("identifier", {
"identifier": subj_id
}), ("description", "GTEx subject " + subj_id),
("characteristics", subject_characteristics),
("taxonomy", [util.get_taxon_human(dats_obj_cache)]),
("roles", util.get_donor_roles(dats_obj_cache))])
dats_obj_cache[subj_key] = subject_material
specimen_annot = util.get_annotation("specimen", dats_obj_cache)
rna_extract_annot = util.get_annotation("RNA extract", dats_obj_cache)
# biological/tissue sample
sample_name = samp_id
biological_sample_material = DatsObj(
"Material",
[("name", sample_name), ("identifier", {
"identifier": samp_id
}),
("description",
anatomy_name + " specimen collected from subject " + subj_id),
("taxonomy", [util.get_taxon_human(dats_obj_cache)]),
("roles", [specimen_annot]),
("derivesFrom", [subject_material, anatomical_part])])
# RNA extracted from tissue sample
rna_material = DatsObj(
"Material",
[("name", "RNA from " + sample_name),
("description", "total RNA extracted from " + anatomy_name +
" specimen collected from subject " + subj_id),
("taxonomy", [util.get_taxon_human(dats_obj_cache)]),
("roles", [rna_extract_annot]),
("derivesFrom", [biological_sample_material])])
return rna_material
def get_subject_dats_material(cache, study, study_md, subj_var_values):
# extract subject attributes
gender = None
age = None
visit_year = None
sys_bp = None
dias_bp = None
disease = {}
disease['hypertension'] = "unknown"
for name in subj_var_values:
name_upper = name.upper()
if name_upper == "GENDER" or name_upper == "SEX":
gender = subj_var_values[name]['value'].lower()
elif name_upper == "VISIT_AGE" or name_upper == "AGE" or name_upper == "AGE_ENROLL": #need to confirm that these allmean the same thing
age = subj_var_values[name]['value']
elif name_upper == "VISIT_YEAR":
visit_year = subj_var_values[name]['value']
elif name_upper == "SYSBP":
sys_bp = subj_var_values[name]['value']
elif name_upper == "DIASBP":
dias_bp = subj_var_values[name]['value']
elif name_upper == "HYPERTENSION" or name_upper == "HIGHBLOODPRES":
if subj_var_values[name]['value'].lower() == "yes" or subj_var_values[name]['value'] == '1':
disease['hypertension'] = "yes"
elif re.match(r'\S', subj_var_values[name]['value']):
disease['hypertension'] = "no"
subject_characteristics = []
subject_bearerOfDisease = []
# harmonized/standardized characteristics
if gender is not None:
subject_sex = DatsObj("Dimension", [
("name", DatsObj("Annotation", [("value", "Gender")])),
("description", "Gender of the subject"),
("values", [ gender ])
])
subject_characteristics.append(subject_sex)
if age is not None:
subject_age = DatsObj("Dimension", [
("name", DatsObj("Annotation", [("value", "Age")])),
("description", "Age of the subject"),
("values", [ age ])
])
subject_characteristics.append(subject_age)
if visit_year is not None:
subject_visitYear = DatsObj("Dimension", [
("name", DatsObj("Annotation", [("value", "Visit year")])),
("description", "Year of visit, to use for longitudinal analysis"),
("values", [ visit_year ])
])
subject_characteristics.append(subject_visitYear)
if sys_bp is not None:
subject_sysBP = DatsObj("Dimension", [
("name", DatsObj("Annotation", [("value", "Systolic blood pressure")])),
("description", "Systolic blood pressure of subject, measured in mmHg"),
("values", [ sys_bp ])
])
subject_characteristics.append(subject_sysBP)
if dias_bp is not None:
subject_diasBP = DatsObj("Dimension", [
("name", DatsObj("Annotation", [("value", "Diastolic blood pressure")])),
("description", "Diastolic blood pressure of subject, measured in mmHg"),
("values", [ dias_bp ])
])
subject_characteristics.append(subject_diasBP)
if disease['hypertension'] != "unknown":
disease_name = "hypertension"
disease_id = "10763"
disease_identifier = OrderedDict([
("identifier", "DOID:" + str(disease_id)),
("identifierSource", "Disease Ontology")])
disease_alt_ids = [OrderedDict([
("identifier", "http://purl.obolibrary.org/obo/DOID_" + str(disease_id)),
("identifierSource", "Disease Ontology")])]
subject_hypertension = DatsObj("Disease", [
("name", "Hypertension"),
("identifier", disease_identifier),
("alternateIdentifiers", disease_alt_ids),
("diseaseStatus", DatsObj("Annotation", [("value", disease['hypertension'] ), ("valueIRI", "")])),
])
subject_bearerOfDisease.append(subject_hypertension)
# create a DATS Dimension from a dbGaP variable value
def make_var_dimension(name, var_value):
value = var_value["value"]
dim = DatsObj("Dimension",
[("name", DatsObj("Annotation", [( "value", name )])),
("values", [ value ])
])
# find existing DATS identifier for the corresponding Dataset Dimension
if "var" in var_value:
dbgap_var_dim = var_value["var"]["dim"]
dim.setProperty("identifier", dbgap_var_dim.get("identifier").getIdRef())
return dim
# create DATS Dimensions for dbGaP subject metadata
subject_dimensions = [ make_var_dimension(vname, subj_var_values[vname]) for vname in sorted(subj_var_values) ]
# "raw" characteristics from dbGaP metadata
subject_characteristics.extend(subject_dimensions)
human_t = util.get_taxon_human(cache)
subj_id = subj_var_values['SUBJECT_ID']['value']
dbgap_subj_id = subj_var_values['dbGaP_Subject_ID']['value']
study_title = study.get("title")
# human experimental subject/patient
subject_material = DatsObj("Material", [
("name", subj_id),
("identifier", { "identifier": subj_id }),
("alternateIdentifiers", [ util.get_alt_id(dbgap_subj_id, "dbGaP") ]),
("description", study_title + " subject " + subj_id),
("characteristics", subject_characteristics),
("bearerOfDisease", subject_bearerOfDisease),
("taxonomy", [ human_t ]),
("roles", util.get_donor_roles(cache))
])
# add to the cache
subj_key = ":".join(["Material", subj_id])
dats_subj = cache.get_obj_or_ref(subj_key, lambda: subject_material)
return dats_subj
def get_single_dna_extract_json(cache, study, study_md, subj_var_values,
samp_var_values):
# Almost all samples in TOPMed WGS phase are blood samples, named "Blood", "Peripheral Blood"...
# Few samples are saliva samples probably due to sample collection issues
name = None
if 'BODY_SITE' in samp_var_values:
name = 'BODY_SITE'
elif 'Body_Site' in samp_var_values:
name = 'Body_Site'
elif 'Body Site' in samp_var_values:
name = 'Body Site'
anat_id = None
anatomy_name = None
if name is not None:
if "blood" in samp_var_values[name]['value'].lower():
anatomy_name = "blood"
anat_id = "0000178"
elif samp_var_values[name]['value'].lower() == "saliva":
anatomy_name = "saliva"
anat_id = "0001836"
else:
logging.fatal(
"encountered BODY_SITE other than 'Blood' and 'Saliva' in TOPMed sample metadata - "
+ samp_var_values['BODY_SITE']['value'])
sys.exit(1)
if anat_id is not None:
anatomy_identifier = OrderedDict([("identifier",
"UBERON:" + str(anat_id)),
("identifierSource", "UBERON")])
anatomy_alt_ids = [
OrderedDict([
("identifier",
"http://purl.obolibrary.org/obo/UBERON_" + str(anat_id)),
("identifierSource", "UBERON")
])
]
# extract subject attributes
gender = None
age = None
visit_year = None
sys_bp = None
dias_bp = None
disease = {}
disease['hypertension'] = "unknown"
for name in subj_var_values:
name_upper = name.upper()
if name_upper == "GENDER" or name_upper == "SEX":
gender = subj_var_values[name]['value'].lower()
elif name_upper == "VISIT_AGE" or name_upper == "AGE" or name_upper == "AGE_ENROLL": #need to confirm that these allmean the same thing
age = subj_var_values[name]['value']
elif name_upper == "VISIT_YEAR":
visit_year = subj_var_values[name]['value']
elif name_upper == "SYSBP":
sys_bp = subj_var_values[name]['value']
elif name_upper == "DIASBP":
dias_bp = subj_var_values[name]['value']
elif name_upper == "HYPERTENSION" or name_upper == "HIGHBLOODPRES":
if subj_var_values[name]['value'].lower(
) == "yes" or subj_var_values[name]['value'] == '1':
disease['hypertension'] = "yes"
elif re.match(r'\S', subj_var_values[name]['value']):
disease['hypertension'] = "no"
# anatomical part
anatomical_part = None
if anatomy_name is not None:
anatomical_part = DatsObj("AnatomicalPart",
[("name", anatomy_name),
("identifier", anatomy_identifier),
("alternateIdentifiers", anatomy_alt_ids)])
subject_characteristics = []
subject_bearerOfDisease = []
# harmonized/standardized characteristics
if gender is not None:
subject_sex = DatsObj(
"Dimension",
[("name", DatsObj("Annotation", [("value", "Gender")])),
("description", "Gender of the subject"), ("values", [gender])])
subject_characteristics.append(subject_sex)
if age is not None:
subject_age = DatsObj(
"Dimension", [("name", DatsObj("Annotation", [("value", "Age")])),
("description", "Age of the subject"),
("values", [age])])
subject_characteristics.append(subject_age)
if visit_year is not None:
subject_visitYear = DatsObj("Dimension", [
("name", DatsObj("Annotation", [("value", "Visit year")])),
("description", "Year of visit, to use for longitudinal analysis"),
("values", [visit_year])
])
subject_characteristics.append(subject_visitYear)
if sys_bp is not None:
subject_sysBP = DatsObj(
"Dimension",
[("name",
DatsObj("Annotation", [("value", "Systolic blood pressure")])),
("description",
"Systolic blood pressure of subject, measured in mmHg"),
("values", [sys_bp])])
subject_characteristics.append(subject_sysBP)
if dias_bp is not None:
subject_diasBP = DatsObj(
"Dimension",
[("name",
DatsObj("Annotation", [("value", "Diastolic blood pressure")])),
("description",
"Diastolic blood pressure of subject, measured in mmHg"),
("values", [dias_bp])])
subject_characteristics.append(subject_diasBP)
if disease['hypertension'] != "unknown":
disease_name = "hypertension"
disease_id = "10763"
disease_identifier = OrderedDict([
("identifier", "DOID:" + str(disease_id)),
("identifierSource", "Disease Ontology")
])
disease_alt_ids = [
OrderedDict([
("identifier",
"http://purl.obolibrary.org/obo/DOID_" + str(disease_id)),
("identifierSource", "Disease Ontology")
])
]
subject_hypertension = DatsObj("Disease", [
("name", "Hypertension"),
("identifier", disease_identifier),
("alternateIdentifiers", disease_alt_ids),
("diseaseStatus",
DatsObj("Annotation", [("value", disease['hypertension']),
("valueIRI", "")])),
])
subject_bearerOfDisease.append(subject_hypertension)
# create a DATS Dimension from a dbGaP variable value
def make_var_dimension(name, var_value):
value = var_value["value"]
dim = DatsObj("Dimension",
[("name", DatsObj("Annotation", [("value", name)])),
("values", [value])])
# find existing DATS identifier for the corresponding Dataset Dimension
if "var" in var_value:
id = var_value["var"]["id"]
dbgap_var_dim = study_md['dbgap_vars'][id]
dim.setProperty("identifier",
dbgap_var_dim.get("identifier").getIdRef())
return dim
# create DATS Dimensions for dbGaP subject metadata
subject_dimensions = [
make_var_dimension(vname, subj_var_values[vname])
for vname in sorted(subj_var_values)
]
# create DATS Dimensions for dbGaP sample metadata
sample_dimensions = [
make_var_dimension(vname, samp_var_values[vname])
for vname in sorted(samp_var_values)
]
# "raw" characteristics from dbGaP metadata
subject_characteristics.extend(subject_dimensions)
sample_characteristics = sample_dimensions
human_t = util.get_taxon_human(cache)
subj_id = subj_var_values['SUBJECT_ID']['value']
dbgap_subj_id = subj_var_values['dbGaP_Subject_ID']['value']
samp_id = samp_var_values['SAMPLE_ID']['value']
dbgap_samp_id = samp_var_values['dbGaP_Sample_ID']['value']
study_title = study.get("title")
# human experimental subject/patient
subject_material = DatsObj(
"Material",
[("name", subj_id), ("identifier", {
"identifier": subj_id
}),
("alternateIdentifiers", [util.get_alt_id(dbgap_subj_id, "dbGaP")]),
("description", study_title + " subject " + subj_id),
("characteristics", subject_characteristics),
("bearerOfDisease", subject_bearerOfDisease), ("taxonomy", [human_t]),
("roles", util.get_donor_roles(cache))])
# TODO - use DatsObjCache
specimen_annot = util.get_annotation("specimen")
dna_extract_annot = util.get_annotation("DNA extract")
# biological/tissue sample
sample_name = samp_id
sample_derives_from = [subject_material]
sample_descr = "specimen collected from subject " + subj_id
if anatomical_part is not None:
sample_derives_from.append(anatomical_part)
sample_descr = anatomy_name + " " + sample_descr
biological_sample_material = DatsObj(
"Material",
[("name", sample_name), ("identifier", {
"identifier": samp_id
}),
("alternateIdentifiers", [util.get_alt_id(dbgap_samp_id, "dbGaP")]),
("description", sample_descr),
("characteristics", sample_characteristics), ("taxonomy", [human_t]),
("roles", [specimen_annot]), ("derivesFrom", sample_derives_from)])
# DNA extracted from tissue sample
dna_descr = "DNA extracted from specimen collected from subject " + subj_id
if anatomical_part is not None:
dna_descr = "DNA extracted from " + anatomy_name + " specimen collected from subject " + subj_id
dna_material = DatsObj("Material",
[("name", "DNA from " + sample_name),
("description", dna_descr),
("taxonomy", [human_t]),
("roles", [dna_extract_annot]),
("derivesFrom", [biological_sample_material])])
return dna_material
def get_single_dna_extract_json(study, subj_var_values, samp_var_values):
# all samples in TOPMed WGS phase are blood samples
if samp_var_values['BODY_SITE'] != 'Blood':
logging.fatal(
"encountered BODY_SITE other than 'Blood' in TOPMed sample metadata - "
+ samp_var_values['BODY_SITE'])
sys.exit(1)
anatomy_name = "blood"
anat_id = "0000178"
anatomy_identifier = OrderedDict([("identifier", "UBERON:" + str(anat_id)),
("identifierSource", "UBERON")])
anatomy_alt_ids = [
OrderedDict([("identifier",
"http://purl.obolibrary.org/obo/UBERON_" + str(anat_id)),
("identifierSource", "UBERON")])
]
# extract subject attributes
gender = None
age = None
for name in subj_var_values:
if name == "GENDER":
gender = subj_var_values[name].lower()
elif name == "VISIT_AGE":
age = subj_var_values[name]
# TODO - determine what other subject attributes can be mapped directly to core DATS objects
# place original dbGaP subject metadata into extraProperties
# TODO - consider alternative of doing this only for un-harmonized metadata
subj_extra_props = [
DatsObj("CategoryValuesPair", [("category", xp),
("values", [subj_var_values[xp]])])
for xp in sorted(subj_var_values)
]
# extract sample attributes
for name in samp_var_values:
if name == 'SEQUENCING_CENTER':
# TODO - determine which DATS objects (e.g., biological sample, DNA prep, sequence data) this property should attach to
pass
# TODO - determine what other subject attributes can be mapped directly to core DATS objects
# e.g., IS_TUMOR -> bearerOfDisease ("the pathology affecting the material...")
# place original dbGaP sample metadata into extraProperties
samp_extra_props = [
DatsObj("CategoryValuesPair", [("category", xp),
("values", [samp_var_values[xp]])])
for xp in sorted(samp_var_values)
]
# anatomical part
anatomical_part = DatsObj("AnatomicalPart",
[("name", anatomy_name),
("identifier", anatomy_identifier),
("alternateIdentifiers", anatomy_alt_ids)])
subject_sex = DatsObj("Dimension", [("name", {
"value": "Gender"
}), ("description", "Gender of the subject"), ("values", [gender])])
subject_age = DatsObj("Dimension", [("name", {
"value": "Age"
}), ("description", "Age of the subject"), ("values", [age])])
subject_characteristics = [subject_sex, subject_age]
human_t = util.get_taxon_human()
subj_id = subj_var_values['SUBJECT_ID']
dbgap_subj_id = subj_var_values['dbGaP_Subject_ID']
samp_id = samp_var_values['SAMPLE_ID']
dbgap_samp_id = samp_var_values['dbGaP_Sample_ID']
study_title = study.get("title")
# human experimental subject/patient
subject_material = DatsObj(
"Material",
[("name", subj_id), ("identifier", {
"identifier": subj_id
}),
("alternateIdentifiers", [util.get_alt_id(dbgap_subj_id, "dbGaP")]),
("description", study_title + " subject " + subj_id),
("characteristics", subject_characteristics), ("taxonomy", human_t),
("roles", util.get_donor_roles()),
("extraProperties", subj_extra_props)])
# biological/tissue sample
sample_name = samp_id
biological_sample_material = DatsObj(
"Material",
[("name", sample_name), ("identifier", {
"identifier": samp_id
}),
("alternateIdentifiers", [util.get_alt_id(dbgap_samp_id, "dbGaP")]),
("description",
anatomy_name + " specimen collected from subject " + subj_id),
("taxonomy", human_t),
("roles", [OrderedDict([("value", "specimen"), ("valueIRI", "")])]),
("derivesFrom", [subject_material, anatomical_part]),
("extraProperties", samp_extra_props)])
# DNA extracted from tissue sample
dna_material = DatsObj(
"Material",
[("name", "DNA from " + sample_name),
("description", "DNA extracted from " +
anatomy_name + " specimen collected from subject " + subj_id),
("taxonomy", human_t),
("roles", [OrderedDict([("value", "DNA extract"),
("valueIRI", "")])]),
("derivesFrom", [biological_sample_material])])
return dna_material