cbioportal-database

To query cancer genomics data, this skill retrieves somatic mutations, copy number alterations, and clinical information from cBioPortal using the REST API. It requires Python with the `requests` and `pandas` libraries to fetch and manipulate the data.

Yes, you can perform survival analysis by integrating clinical data with genomic alterations. The skill queries cBioPortal to link patient survival outcomes and clinical attributes with specific gene mutations or copy number alterations.

To find the mutation frequency of a gene like TP53, you can query specific cancer studies to determine mutation frequencies. This facilitates pan-cancer analysis and helps validate oncogenes or tumor suppressors across various cancer types.

Yes, you need a Python environment with the `requests` and `pandas` libraries installed. These dependencies are required to programmatically retrieve and manipulate the cancer genomics data from the cBioPortal REST API.

The best way to analyze copy number alterations is by querying the target cancer study through this skill. It retrieves CNA data, allowing you to investigate amplifications and deletions for genes of interest using the cBioPortal database.

The primary limitation is dependency on the cBioPortal REST API and requires Python with `requests` and `pandas`. Pan-cancer analysis is facilitated by querying multiple studies, constrained by the data available within the cBioPortal repository.