https://github.com/conorheffron/gene-expr

Breast Cancer Dataset Analysis
https://github.com/conorheffron/gene-expr

deseq2-analysis dplyr ggplot2 health r-programming report

Last synced: 22 days ago
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Breast Cancer Dataset Analysis

• Host: GitHub
• URL: https://github.com/conorheffron/gene-expr
• Owner: conorheffron
• License: gpl-3.0
• Created: 2023-12-06T10:52:12.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-07-25T23:08:48.000Z (4 months ago)
• Last Synced: 2024-10-12T22:43:29.702Z (about 1 month ago)
• Topics: deseq2-analysis, dplyr, ggplot2, health, r-programming, report
• Language: R
• Homepage: https://conorheffron.github.io/gene-expr/
• Size: 5.35 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: ReadMe.md
  • Security: SECURITY.md

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README

        
![Proof HTML](https://github.com/conorheffron/gene-expr/actions/workflows/proof-html.yml/badge.svg)

### Summary

- In this report, I will analyse a publicly available dataset based on clinical breast cancer data. Breast cancer is the most diagnosed cancer in women. There are several subtypes of diseases characterized by different genetic drivers for cancer risk and tumour growth. The human epidermal growth factor receptor 2 amplified (HER2: ERBB2 / ERBB2IP) breast cancer is one of the most aggressive subtypes. In addition, I will investigate HER3 (ERBB3), HER4 (ERBB4), PIK3C2B, MDM4, LRRN2, NFASC, KLHDC8A, and CDK18 gene mutations. Although there are targeted therapies that have been developed to treat these cancer cases, the response rate ranges from 40% - 50%. I will download, decompress, clean and process the TCGA RNASeq data for breast cancer from cbioportal and identify the differentially expressed genes between ERBB2 / ERBB2IP, ERBB3, ERBB4, PIK3C2B, MDM4, LRRN2, NFASC, KLHDC8A, and CDK18 cancer tumours.

- The dataset can be downloaded from this [link](https://www.cbioportal.org/study/summary?id=brca_tcga_pan_can_atlas_2018)

**Run All Code Chunks in _`assignment-2.qmd`_ from RStudio project & render to PDF or HTML**

- See full report in html or pdf format at: [Full Report](https://conorheffron.github.io/gene-expr/assignment-2.html)