Robust and scalable computational and statistical methods for differential cell crosstalk analysis

University of Aberdeen
Institute of Medical Sciences, School of Medicine, Medical Sciences and Nutrition
United Kingdom Aberdeen


The Institute of Medical Sciences (IMS), University of Aberdeen, is recruiting a cohort of 7 PhD students across its broad themes in neuroscience, immunology, microbiology, molecular biology and computational biology. The studentships are linked to 7 new academic appointments in the Institute. Applications are invited for 4-year fully funded PhD studentships commencing 1st October 2023. The application deadline is 3 February 2023.

Project Description

Communication between cells through the secretion and reception of biomolecules is a foundation of biology. Autocrine and paracrine signalling networks are a key component of a bewildering array of biological processes, including development, cancer, and immune responses. Recent advances in single-cell omics technology have enabled the ability to measure these signalling molecules and their receptors across many cell types simultaneously (Vento-Tormo, Nature, 2018). While computational algorithms exist to identify whether pairs or groups of cells are predicted to communicate, there are very few tools available to identify differential communication between cells (Efremova et al, Nature Protocols, 2020; Jin et al, Nature Communications, 2021; Browaeys et al, Nature Methods 2020). The development of robust computational and statistical tools would facilitate the identification of biological, experimental, and environmental factors that modulate cell-to-cell communication and pinpoint the regulators.

This project will develop the computational tools to identify biological, experimental, and environmental regulators of cell-to-cell communication using single-cell data, for instance by combining graph theory with generalised linear (mixed) models and building on previous work from the lab to identify differentially abundant cell states (Dann et al, Nature Biotechnology). Using data generated in the lab, and publicly available resources (e.g., Stephenson et al, Nature Medicine, 2021), this project will demonstrate the power of such differential analysis tools by benchmarking with simulations, and applying them to cancer, development, and immunity.

We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team. Informal enquiries are encouraged. Please contact Dr Michael Morgan ( for further information.


The student should have strong problem solving and critical analysis skills, with a first degree or 2:1 from a quantitative subject such as bioinformatics, genetics, statistics, engineering, or physics, or have developed strong quantitative skills from a biological background. Programming experience is required with R or Python or static-typed language such as C or C++, and experience of linux and HPC environments is desirable, but not essential. Software development experience would be beneficial, but not necessary.

Start date

October 01, 2023

How to Apply

Formal applications should be made through


Michael Morgan