Prospective Ovarian JHU Intact Glycoproteome

Written and checked by a reviewer, not generated from metadata.

No reviewed research questions yet. The measurements and field completeness above are machine-extracted and still useful for judging fit.

Written by a reviewer against the source, not inferred from what is missing.

No reviewed limitations yet. Check the field completeness chart above before committing to an analysis. Low coverage on an endpoint you need is the most common blocker.

Articles that analyzed these data, counted separately from articles that only cited the paper. Only the first counts as reuse.

Analyses someone ran on these data end to end, each with a receipt recording when it ran, against which packages and for how long.

Nobody has shared an analysis of this dataset yet. The examples on other datasets cover the common patterns and can be adapted - and if you adapt one, it belongs here.

Starter code generated from this record’s own identifiers, then the access steps.

import requests

PDC_STUDY = "PDC000250"
API = "https://proteomic.datacommons.cancer.gov/graphql"

def gql(query, **variables):
    r = requests.post(API, json={"query": query, "variables": variables}, timeout=180)
    r.raise_for_status()
    body = r.json()
    if body.get("errors"):
        raise RuntimeError(body["errors"][0]["message"])
    return body["data"]

# filesPerStudy only fills its fields when given the study UUID: called with the
# pdc_study_id it returns the right number of rows with every column null.
study = gql("""query ($id: String!) {
  study(pdc_study_id: $id acceptDUA: true) { study_id study_name }
}""", id=PDC_STUDY)["study"][0]

files = gql("""query ($sid: String!) {
  filesPerStudy(study_id: $sid acceptDUA: true) {
    file_id file_name file_type data_category md5sum file_size signedUrl { url }
  }
}""", sid=study["study_id"])["filesPerStudy"]

print(study["study_name"], "-", len(files), "files")
for f in files[:3]:
    print(f["file_name"], f["data_category"], f["file_size"])
# Each signedUrl.url is a time-limited download link; stream it to disk with requests.get(url, stream=True).

curl -s https://cd2s.vercel.app/data/datasets/pdc-prospective-ovarian-jhu-intact-glycoproteome.json | python3 -c '
import json, sys
r = json.load(sys.stdin)
for l in r["limitations"]:
    print(l["severity"].upper(), "-", l["statement"][:120])
print("survival endpoint:", r["longitudinal"]["has_survival_endpoint"], "| treatment response:", r["longitudinal"]["has_treatment_response"])'

Access steps

1. 1

   Open the study in the PDC portalFor people5 minutes

   Proteomic data are released open; the portal's use guidelines still apply.

   https://pdc.cancer.gov/pdc/study/PDC000250

   Generated from repository policy. Check the linked policy before downloading.

2. 2

   Accept the data use guidelines and downloadFor peopleminutes to hours, by study size

   The portal asks for acknowledgement and citation, not a data access request.

   https://pdc.cancer.gov/pdc/data-use-guidelines

   Generated from repository policy. Check the linked policy before downloading.

3. 3

   Resolve the study UUID, then fetch the file manifestFor agents

   filesPerStudy keys on the study UUID, not on PDC000250: called with the pdc_study_id it returns the right number of rows with every column null. Resolve the UUID first and pass the terms acknowledgement.

   python - <<'PY'
   import requests
   API = "https://proteomic.datacommons.cancer.gov/graphql"
   
   def gql(query, **variables):
       r = requests.post(API, json={"query": query, "variables": variables}, timeout=180)
       r.raise_for_status()
       body = r.json()
       if body.get("errors"):
           raise RuntimeError(body["errors"][0]["message"])
       return body["data"]
   
   study = gql('query($id:String!){ study(pdc_study_id:$id acceptDUA:true){ study_id } }',
               id="PDC000250")["study"][0]
   files = gql('query($sid:String!){ filesPerStudy(study_id:$sid acceptDUA:true)'
               '{ file_id file_name file_size } }', sid=study["study_id"])["filesPerStudy"]
   print(len(files), "files")
   PY

   https://pdc.cancer.gov/data-dictionary/publicapi-documentation/

   Generated from repository policy. Check the linked policy before downloading.

All of it, and more, is in this record’s JSON.

Same disease area, favoring cohorts that add a measurement this one lacks.