Centre for Computational Biology

John's research focuses on gaining a deeper understanding of the molecular drivers governing cell fate transitions through omics approaches. This enables more efficient identification of genetic perturbations or drug treatments that can drive changes in cell state to reverse disease. With the emergence of large volumes of single-cell data, they are interested in deploying machine learning / deep learning algorithms to create predictive and generative models, simulating how cells respond to genetic or drug perturbations. His lab is also interested in developing new computational tools to more effectively interpret the rich single-cell data in terms of gene expression dynamics, trajectory analysis and incorporating information from different multimodal layers of gene regulation. John's personal website is at https://jfouyang.github.io/.

1. JF Ouyang et al ShinyCell: Simple and sharable visualisation of single-cell gene expression data. Bioinformatics 19, 3374 (2021)
2.  X Liu, JP Tan, J Schröder, A Aberkane, JF Ouyang et al Modelling human blastocysts by reprogramming fibroblasts into iBlastoids. Nature 591, 627–632 (2021)
3.  XY Choo, …, JF Ouyang†, OJL Rackham†. Evaluating Capture Sequence Performance for Single-cell CRISPR-activation Experiments. ACS Synthetic Biology 10, 640–645 (2021)
4.  X Liu*, JF Ouyang*, FJ Rossello et al Reprogramming roadmap reveals route to human induced trophoblast stem cells. Nature 586, 101–107 (2020)
5.  A Grubman*, G Chew*, JF Ouyang*, et al A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nature Neuroscience 22, 2087–2097 (2019)