PhD Candidate, Department of Surgery & Cancer
Imperial College London
This repository contains the complete set of R Markdown files, Python scripts, Jupyter Notebooks, Bash utilities, and HTML reports used in the computational analysis for my PhD thesis.
My research focuses on discovering non-invasive circulating cell-free DNA (cfDNA) biomarkers to improve the detection, monitoring, and prognosis of hepatocellular carcinoma (HCC).
The overarching aim of this thesis is to help patients with HCC live better and longer lives by harnessing the power of liquid biopsy and real-time tumour genomics.
- Analyse cfDNA using fragmentomics, copy-number alterations, and mutation profiling.
- Estimate tumour burden using ichorCNA and DELFI-inspired fragmentation metrics.
- Develop prognostic markers in TACE-, TKI-, and ICI-treated cohorts.
- Build diagnostic models to classify Healthy, CLD, and HCC individuals.
- Investigate mitochondrial cfDNA, end-motif patterns, and chromosomal instability.
- Produce reproducible and transparent workflows for translational biomarker discovery.
This repository includes:
- R Markdown scripts for analysis, figure generation, and reporting
- Python pipelines for machine learning, modelling, and QC
- Jupyter notebooks for exploratory and validation work
- HTML reports published via GitHub Pages
- Bash scripts for mitochondrial read processing and cfDNA alignment
- Processed data summaries, statistical outputs, and exported visualisations
Each analysis is structured for clarity and reproducibility.