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pairwise
README.md
createSchema.py
gdcCase2Cohort.py
isb_auth.py
isb_cgc_api_v3_cases.py
isb_cgc_api_v3_cohorts.py
isb_cgc_api_v3_samples.py
isb_cgc_api_v3_users.py
isb_curl.py
melt_matrix.py

README.md

examples-Python/python

The python subdirectory of this repository contains examples to help you get started working with the data and tools hosted by the ISB-CGC on the Google Cloud Platform. There are three endpoints based on the three programs in the Genomics Data Commons:

  • isb_cgc_ccle_api
  • isb_cgc_target_api
  • isb_cgc_tcga_api and one endpoint for the cohort apis that can be cross-program:
  • isb_cgc_api

Programmatic API Examples

The ISB-CGC programmatic API is implemented using Google Cloud Endpoints. These services run on App Engine and can be accessed in several different ways. You can use the APIs Explorer to try them out directly in your web browser.

  • isb_auth.py is a help script that takes care of the auth required for the cohort endpoint APIs
  • isb_cgc_api_v3_cases shows you how to build a service object and run the isb_cgc_tcga_api cases get API. This cases get API is also part of the isb_cgc_ccle_api and isb_cgc_target_api endpoints.
  • isb_cgc_api_v3_cohorts shows you how to build a service object and run the isb_cgc_tcga_api cohorts create and preview APIs. These cohorts APIs are also part of the isb_cgc_ccle_api and isb_cgc_target_api endpoints. It also shows how to build a service object and run the isb_cgc_api cohorts get, list, cloud_storage_file_paths, and delete APIs
  • isb_cgc_api_v3_samples shows you how to build a service object and run the isb_cgc_tcga_api samples get and cloud_storage_file_paths APIs. These samples APIs are also part of the isb_cgc_ccle_api and isb_cgc_target_api endpoints.
  • isb_cgc_api_v3_users shows you how to build a service object and run the isb_cgc_tcga_api users get API. This users get API is also part of the isb_cgc_ccle_api and isb_cgc_target_api endpoints. These are some helper scripts for other aspects of the Google Cloud
  • query_ccle_reads.py script illustrates the usage of the GA4GH API for open-access CCLE reads
  • createSchema.py script generates a JSON schema for an input data file. This is useful when the data file has a large number of columns, so you can avoid manual creation of its schema. This can be used with the 'bq load' command line tool to load data to BigQuery (https://cloud.google.com/bigquery/quickstart-command-line).