This repository contains my PhD thesis "Tools and techniques for single-cell RNA sequencing data". The HTML version is available at https://lazappi.github.io/phd-thesis.

RNA sequencing of individual cells allows us to take a snapshot of the dynamic processes within a cell and explore differences between cell types. As this technology has developed over the last few years it has been rapidly adopted by researchers in areas such as developmental biology, and many single-cell RNA sequencing datasets are now available. Coinciding with the development of protocols for producing single-cell RNA sequencing data there has been a simultaneous burst in the development of computational analysis methods. My thesis explores the computational tools and techniques for analysing single-cell RNA sequencing data. I present a database that charts the release of analysis software, where it has been published and what it can be used for, as well as a website that makes this information publicly available. I also present two of my own tools and techniques including Splatter, a software package for easily simulating single-cell datasets from multiple models, and clustering trees, a visualisation approach for inspecting clustering at multiple resolutions. In the final part of my thesis I perform analysis of a dataset from kidney organoids to demonstrate and compare some current analysis methods. Taken together, my thesis covers many aspects of the tools and techniques for single-cell RNA sequencing by describing the approaches that are available, presenting software that can help in developing and evaluating methods, introducing an approach for aiding one of the most common analysis tasks, and showing how tools can be used to extract meaning from a real dataset.

• scRNA-tools - A database of software tools for analysing single-cell RNA sequencing data (Website, GitHub, Publication)
• Splatter - An R package for the simple simulation of single-cell RNA sequencing data (Bioconductor, GitHub, Publication)
• clustree - An R package for producing clustering tree visualisation to show changes in clustering as resolution increases (CRAN, GitHub, Publication)
• Combes organoid analysis - Analysis of single-cell RNA sequencing data from kidney organoids to identify and profile the cell types present (Website, GitHub, Publication)
• PhD analysis - Re-analysis of the Combes organoid data to explore the effect of different tool and parameter choices during analysis (Website, GitHub)