Postdoc at Earlham Institute, Norwich.
When I’m not making Rstudio crash, I like reading, playing D&D, vegan cooking and failing sets at the gym.
PhD project while in Sudlab
Preclinical cancer models, such as tumour-derived cell lines and animal models are essential in cancer research. Although cell lines are the most used preclinical model, there has been great difficulty in matching the appropriate cell lines with the corresponding disease in a clinically relevant manner.
I worked on developing methods for systematic cell line scoring based on patient sample subtypes and on analysis of the causative elements of the subtype differentiation in that context.
Cell line scoring was based on machine learning models trained and tested on multi-omics cancer data (TCGA). The models achieved high accuracy and were subsequently applied to the cell line data (CCLE) to produce a clinically based cancer cell line relevance score. Majority of cell line scores were in line with the literature, but there were several misclassified cells. This lead to the identification of cell lines potentially suitable for research of treatment-resistant HER2-positive breast cancer.
The core aspects of the methodology are implemented as a Shiny application (https://cell-line-scoring.shinyapps.io/clscoring/) with an R package in the pipeline.
After finishing my PhD as a proud member of Sudlab, I started a postdoc at the Earlham Institute in Norwich, as a single-cell bionformatics scientist.
My current main project is integration and visualisation of short-read and long-read single-cell data. Previously, I worked on developing a quality control pipeline for Smart-Seq2 single-cell data. I also conduct computational analysis for various research projects at the institute through tools like cellranger, Seurat, scater and Monocle.