Finding Genesets

Dependencies and Set Up¶

In [ ]:

!pip install geneweaver-client

!pip install geneweaver-client

In [1]:

from geneweaver.client.auth import login

from geneweaver.client.auth import login

In [2]:

login()

login()

1. On your computer or mobile device navigate to:  https://geneweaver.auth0.com/activate?user_code=VPPC-FWSQ
2. Enter the following code:  VPPC-FWSQ
Authenticated!
- Id Token: eyJhbGciOi...

In [4]:

from geneweaver.client.auth import get_access_token

from geneweaver.client.auth import get_access_token

Finding Cancer Related Genesets¶

In [6]:

import requests

import requests

In [56]:

result = requests.get(
    "https://geneweaver.jax.org/api/genesets",
    params={
        "search_text": "cancer",
        "curation_tier": "Tier I",
        "limit": 10
    },
    headers={"Authorization": f"Bearer {get_access_token()}"}
)
result.ok

result = requests.get( "https://geneweaver.jax.org/api/genesets", params={ "search_text": "cancer", "curation_tier": "Tier I", "limit": 10 }, headers={"Authorization": f"Bearer {get_access_token()}"} ) result.ok

Out[56]:

True

In [57]:

cancer_genesets = result.json()["data"]

cancer_genesets = result.json()["data"]

In [58]:

for i in cancer_genesets:
    print(i["name"])

for i in cancer_genesets: print(i["name"])

MP:0010337 increased chronic lymphocytic leukemia incidence
MSigDB Geneset - MORF_ZNF10
MSigDB Geneset - KRAS.LUNG.BREAST_UP.V1_UP
MSigDB Geneset - MORF_HDAC2
MSigDB Geneset - GCM_PTPRD
GWAS Catalog Data for breast carcinoma in 10,052 European ancestry cases, 12,575 European ancestry controls
MSigDB Geneset - MORF_TFDP2
GWAS Catalog Data for response to platinum based chemotherapy, non-small cell lung carcinoma in 327 European ancestry individuals
MSigDB Geneset - MORF_PML
MSigDB Geneset - GCM_ERCC4

Finding GWAS Cancer Related Genesets¶

In [60]:

result = requests.get(
    "https://geneweaver.jax.org/api/genesets",
    params={
        "search_text": "gwas & cancer",
        "curation_tier": "Tier I",
        "limit": 10
    },
    headers={"Authorization": f"Bearer {get_access_token()}"}
)
result.ok

result = requests.get( "https://geneweaver.jax.org/api/genesets", params={ "search_text": "gwas & cancer", "curation_tier": "Tier I", "limit": 10 }, headers={"Authorization": f"Bearer {get_access_token()}"} ) result.ok

Out[60]:

True

In [61]:

gwas_cancer_genesets = result.json()["data"]

gwas_cancer_genesets = result.json()["data"]

In [62]:

for i in gwas_cancer_genesets:
    print(i["name"])

for i in gwas_cancer_genesets: print(i["name"])

GWAS Catalog Data for breast carcinoma in 10,052 European ancestry cases, 12,575 European ancestry controls
GWAS Catalog Data for response to platinum based chemotherapy, non-small cell lung carcinoma in 327 European ancestry individuals
GWAS Catalog Data for lung carcinoma in 2,331 Han Chinese ancestry lung cancer cases, 1,006 Han Chinese ancestry non-cardia gastric cancer cases, 2,031 Han Chinese ancestry esophageal squamous-cell carcinoma cases, 4,006 Han Chinese ancestry controls
GWAS Catalog Data for lung carcinoma, squamous cell carcinoma, gastric carcinoma in 2,331 Han Chinese ancestry lung cancer cases, 1,006 Han Chinese ancestry non-cardia gastric cancer cases, 2,031 Han Chinese ancestry esophageal squamous-cell carcinoma cases, 4,006 Han Chinese ancestry controls
GWAS Catalog Data for estrogen-receptor negative breast cancer in 4,939 European ancestry cases, 14,352 European ancestry controls
GWAS Catalog Data for breast carcinoma in 899 European ancestry cases, 804 European ancestry controls
GWAS Catalog Data for breast carcinoma in 1,426 European ancestry cases, 1,301 European ancestry controls
GWAS Catalog Data for ovarian carcinoma in 1,817 European ancestry cases, 2,353 European ancestry controls
GWAS Catalog Data for breast carcinoma in 1,367 Sardinian cases, 1,658 Sardinian controls
GWAS Catalog Data for endometrial carcinoma in 4,907 European ancestry cases, 11,945 European ancestry controls

In [66]:

for i in gwas_cancer_genesets:
    print(f"GS{i['id']} has {i['count']} genes")

for i in gwas_cancer_genesets: print(f"GS{i['id']} has {i['count']} genes")

GS267835 has 98 genes
GS270261 has 1 genes
GS268187 has 3 genes
GS268186 has 3 genes
GS271027 has 30 genes
GS269914 has 1 genes
GS270401 has 7 genes
GS270122 has 2 genes
GS267760 has 2 genes
GS268492 has 8 genes

In [ ]: