masters_project

Project Description

This repository contains the code and data analysis scripts for my Master's project titled "characterising the radiogenomic properties of high Ki-67 index adrenocortical carcinoma". The study involves genomic, radiomic, and statistical analyses to uncover significant biomarkers and pathways associated with aggressive adrenocortical carcinoma.

Repository Contents

1. cbioportal_transcriptomics.Rmd
   • This Rmarkdown file contains code used to perform quartiling and segmentation of the TCGA-ACC samples based on MKI67 gene expression, survival analyses, tumour microenvironment analyses (xCell, CIBERSORTx, MCP-Counter), and additional exploratory analyses.
2. differential_expression_TCGA_ACC.Rmd
   • This Rmarkdown file contains code used to perform differential gene, miRNA, and protein expression analyses on the extreme MKI67 expression cohorts as well as gene set enrichment analysis (GSEA).
3. copy_number_analysis_TCGA_ACC.Rmd
   • This Rmarkdown file contains code used to calculate total copy number alteration (CNA) scores from the amplification and deletion scores for each patient in the extreme MKI67 expression analysis, as well as additional analyses relating to total CNA scores.
4. transcriptomic_analysis_cna_quartiles.Rmd
   • This Rmarkdown file contains code used to perform the supplementary analysis of extreme CNA score quartiles. The first portion contains a survival analysis in an attempt to replicate a previous survival analysis of extreme CNA quartiles (Langan et al., (2023)). The second portion contains an exploratory differential gene expression analysis on these selected quartiles, as well as a GSEA and some additional differential expression analyses.
5. Radiomics.Rmd
   • This Rmarkdown file contains code used to perform quantiling and segmentation of the TCIA Adrenal-ACC-Ki67-Seg samples based on their Ki-67 index value.
6. Radiomics.ipynb
   • This Jupyter Notebook file contains the code (Python) used to perform all radiomic analyses on our selected "high" and "low" Ki-67 index cohorts. Radiomic feature extraction was carried out using PyRadiomics.
7. Radiomics2.ipynb
   • This Jupyter Notebook file contains the code (Python) used to perform an additional radiomic analyses using the same cohorts as the study carried out by the original authors of this dataset, Ahmed et al. The aim of this additional analysis was to replicate the original analysis as accurately as possible and to experiment with and obtain a Params.yaml file that was suited to our own radiomic analysis of extreme Ki-67 index cohorts.
8. Params.yaml
   • This .yaml file contains the parameters used to carry out batch radiomics analysis on our selected cohorts.

Contact information

1. Evan Naughton
   • MSc Biomedical Genomics, University of Galway, Ireland.
   • Email(s): E.NAUGHTON10@universityofgalway.ie{.email} | naughtonevan@gmail.com{.email}

Acknowledgements

I would like to extend a sincere thank you to my supervisors Dr. Aaron Golden and Dr. Conall Dennedy for their advice, feedback, and guidance throughout the course of this project. I would also like to extend my thanks to PhD candidate Parisa Taheri and HRB Summer Student Scholarship recipient Eva Langan for their help and support during this project also.