This library sits on top of the machine generated CDA Python Client and offers some syntactic sugar to make it more pleasant to query the CDA.
To try out the example notebook in MyBinder.org without having to install anything, just click on the logo below. This will launch a Jupyter Notebook instance with our example notebook ready to run.
If you want to install the CDA Python library locally:
pip install git+https://github.com/CancerDataAggregator/cda-python.git
(Also see example IPython notebook)
from cdapython import Q, columns, unique_terms
columns() # List column names eg:
# ['days_to_birth',
# 'race',
# 'sex',
# 'ethnicity',
# 'id',
# 'ResearchSubject',
# 'ResearchSubject.Diagnosis',
# 'ResearchSubject.Diagnosis.morphology',
# 'ResearchSubject.Diagnosis.tumor_stage',
# 'ResearchSubject.Diagnosis.tumor_grade',
# 'ResearchSubject.Diagnosis.Treatment',
# 'ResearchSubject.Diagnosis.Treatment.type',
# 'ResearchSubject.Diagnosis.Treatment.outcome',
unique_terms("ResearchSubject.primary_disease_type") # List unique terms for this column eg:
# [None,
# 'Acinar Cell Neoplasms',
# 'Adenomas and Adenocarcinomas',
# 'Adnexal and Skin Appendage Neoplasms',
# 'Basal Cell Neoplasms',
# 'Blood Vessel Tumors',
# 'Breast Invasive Carcinoma',
# 'Chromophobe Renal Cell Carcinoma',
# 'Chronic Myeloproliferative Disorders',
# 'Clear Cell Renal Cell Carcinoma',
# 'Colon Adenocarcinoma',
# ...
q1 = Q('ResearchSubject.primary_disease_type = "Adenomas and Adenocarcinomas"')
r = q1.run() # Executes this query on the public CDA server
# r = q1.run(host="https://localhost:8080") # Executes on local instance of CDA server
# r = q1.run(limit=2) # Limit to two results per page
r.sql # Return SQL string used to generate the query e.g.
# "SELECT * FROM gdc-bq-sample.cda_mvp.v1, UNNEST(ResearchSubject) AS _ResearchSubject WHERE (_ResearchSubject.primary_disease_type = 'Adenomas and Adenocarcinomas')"
print(r) # Prints some brief information about the result page eg:
#
# Query: SELECT * FROM gdc-bq-sample.cda_mvp.v1, UNNEST(ResearchSubject) AS _ResearchSubject WHERE (_ResearchSubject.# primary_disease_type = 'Adenomas and Adenocarcinomas')
# Offset: 0
# Limit: 2
# Count: 2
# More pages: Yes
r[0] # Returns nth result of this page as a Python dict e.g.
#
# {'days_to_birth': None,
# 'race': None,
# 'sex': None,
# 'ethnicity': None,
# 'id': '4d54f72c-e8ac-44a7-8ab9-9f20001750b3',
# 'ResearchSubject': [{'Diagnosis': [],
# 'Specimen': [],
# 'associated_project': 'CGCI-HTMCP-CC',
# 'id': '4d54f72c-e8ac-44a7-8ab9-9f20001750b3',
# 'primary_disease_type': 'Adenomas and Adenocarcinomas',
# 'identifier': [{'system': 'GDC',
# 'value': '4d54f72c-e8ac-44a7-8ab9-9f20001750b3'}],
# 'primary_disease_site': 'Cervix uteri'}],
# 'Diagnosis': [],
# 'Specimen': [],
# 'associated_project': 'CGCI-HTMCP-CC',
# 'primary_disease_type': 'Adenomas and Adenocarcinomas',
# 'identifier': [{'system': 'GDC',
# 'value': '4d54f72c-e8ac-44a7-8ab9-9f20001750b3'}],
# 'primary_disease_site': 'Cervix uteri'}
r.pretty_print(0) # Prints the nth result nicely
#
# { 'Diagnosis': [],
# 'ResearchSubject': [ { 'Diagnosis': [],
# 'Specimen': [],
# 'associated_project': 'CGCI-HTMCP-CC',
# 'id': '4d54f72c-e8ac-44a7-8ab9-9f20001750b3',
# 'identifier': [ { 'system': 'GDC',
# 'value': '4d54f72c-e8ac-44a7-8ab9-9f20001750b3'}],
# 'primary_disease_site': 'Cervix uteri',
# 'primary_disease_type': 'Adenomas and '
# 'Adenocarcinomas'}],
# 'Specimen': [],
# 'associated_project': 'CGCI-HTMCP-CC',
# 'days_to_birth': None,
# 'ethnicity': None,
# 'id': '4d54f72c-e8ac-44a7-8ab9-9f20001750b3',
# 'identifier': [ { 'system': 'GDC',
# 'value': '4d54f72c-e8ac-44a7-8ab9-9f20001750b3'}],
# 'primary_disease_site': 'Cervix uteri',
# 'primary_disease_type': 'Adenomas and Adenocarcinomas',
# 'race': None,
# 'sex': None}
r2 = r.next_page() # Fetches the next page of results
print(r2)
# Query: SELECT * FROM gdc-bq-sample.cda_mvp.v1, UNNEST(ResearchSubject) AS _ResearchSubject WHERE (_ResearchSubject.# primary_disease_type = 'Adenomas and Adenocarcinomas')
# Offset: 2
# Limit: 2
# Count: 2
# More pages: Yes
Select data from TCGA-OV project, with donors over age 50
from cdapython import Q
q1 = Q('ResearchSubject.Diagnosis.age_at_diagnosis > 50*365')
q2 = Q('ResearchSubject.associated_project = "TCGA-OV"')
q = q1.And(q2)
r = q.run()
print(r)
# Query: SELECT * FROM gdc-bq-sample.cda_mvp.v1, UNNEST(ResearchSubject) AS _ResearchSubject, UNNEST(_ResearchSubject.Diagnosis) AS # _Diagnosis WHERE ((_Diagnosis.age_at_diagnosis > 50*365) AND (_ResearchSubject.associated_project = 'TCGA-OV'))
# Offset: 0
# Limit: 1000
# Count: 461
# More pages: No
r.pretty_print(2)
# { 'Diagnosis': [ { 'Treatment': [ { 'outcome': None,
# 'type': 'Radiation Therapy, NOS'},
# { 'outcome': None,
# 'type': 'Pharmaceutical Therapy, NOS'}],
# 'age_at_diagnosis': 28779,
# 'id': 'dc8af98b-03cb-5817-84fa-d86a7f2df8c6',
# 'morphology': '8441/3',
# 'primary_diagnosis': 'Serous cystadenocarcinoma, NOS',
# 'tumor_grade': 'not reported',
# 'tumor_stage': 'not reported'}],
# 'ResearchSubject': [ { 'Diagnosis': [ { 'Treatment': [ { 'outcome': None,
# 'type': 'Radiation '
# 'Therapy, '
# 'NOS'},
# { 'outcome': None,
# 'type': 'Pharmaceutical '
# 'Therapy, '
# 'NOS'}],
# 'age_at_diagnosis': 28779,
# 'id': 'dc8af98b-03cb-5817-84fa-d86a7f2df8c6',
# 'morphology': '8441/3',
# 'primary_diagnosis': 'Serous '
# 'cystadenocarcinoma, '
# 'NOS',
# 'tumor_grade': 'not reported',
# 'tumor_stage': 'not reported'}],
# ...
Any given part of a query is expressed as a string of three parts separated by spaces:
Q('esearchSubject.associated_project = "TCGA-OV"')
The first part is interpreted as a column name, the second as a comparator and the third part as a value. If the value is a string, it needs to be put in quotes.
For cases where there may be ambiguity in the quoting, or the right side of the comparison is another column, the detailed form should be used. Here the three parts of a query are explicity split apart.
from cdapython import Q, Col, Quoted
q1 = Q(Col('ResearchSubject.Diagnosis.age_at_diagnosis'), '>=', 50 * 365)
q2 = Q(Col('ResearchSubject.associated_project), '=' Quoted('TCGA-OV'))
.run() will execute the query on the public CDA API (https://cda.cda-dev.broadinstitute.org/api/cda/v1/).
.run("https://localhost:8080") will execute the query on a CDA server running at
https://localhost:8080.
This is the spiritual successor of the Query Translator Prototype