Data exploration is the initial step in data analysis in which analysts use visualization and statistical techniques to describe dataset characterizations, such as size, quantity, accuracy etc, to better understand the nature of the data. It is critical to the comprehension of large biological datasets obtained by high-throughput assays such as sequencing.
In this project, the candidate will create a tool/web application for interactive visualization of single-cell RNA-Seq based on a collection of colorectal tissue.
It is required to have good programming skills (preferably in R/Python) and be motivated to learn new skills. It is also advantageous to know about the Seurat pipeline used for the analysis of single-cell data. We offer a place in a great working environment in the Jahnsen lab with a well-established bioinformatics core.
Colorectal cancer is a prevalent cancer type and many patients die from colorectal cancer every year because of limited treatment options. Frode Jahnsen's lab at the Department of Pathology is studying the pathology of colorectal cancer to identify novel drugs to treat this disease. Single-cell RNA sequencing and other high-resolution single-cell technologies have dramatically advanced the field to understand cancer biology better. This project will be an essential asset in that work.