Investigating Gene Mutations in Jupyter Notebook
Using the UCSC Xena datasets, we analyzed breast and endometrial cancer by examining gene expression and mutation data. By comparing the expression levels, we observed that certain genes were more frequently expressed in specific cancer types. For example, PIK3CA in breast cancer and TTN in endometrial cancer were appeared most frequently. In Jupyter Notebook, we loaded and processed these datasets, filtered for different features like the gene types, sample types, and mutation types, and created visualizations through bar plots to highlight these expression differences helping us better understand the molecular differences that are unique to each cancer type. Mutation Charts Jupyter Notebook file!)