The Campbell Lab is a group of data scientists working at the interface of statistics, machine learning, and translational biomedicine. We are based in Toronto's Discovery District at the Lunenfeld-Tanenbaum Research Institute of Sinai Health System and the Departments of Molecular Genetics and Statistical Sciences at the University of Toronto.
We have a number of research focusses including:
- Computational modelling of single-cell and spatially resolved ‘omics data
- Understanding the composition and dynamics of the tumour microenvironment and how it enables tumour progression
- Machine learning algorithms to help automate biological and biomedical data analysis
Latest news
Jun 17, 2024
New lab publication
Congratulations to Cindy and Alina for their paper Beyond benchmarking and towards predictive models of dataset-specific single-cell RNA-seq pipeline performance now published in Genome Biology.
Jun 8, 2024
Kieran presenting at Spatial Biology East Coast 2024
Kieran will be giving a talk on Machine learning tools for the analysis and interpretation of highly multiplexed imaging data at Spatial Biology East Coast 2024.
May 12, 2024
Welcome Cece and Elliot
Welcome to Cece and Elliot who join as summer undergraduate research students.
Apr 15, 2024
Lab presentations at RECOMB 2024
Michael and Jett will be presenting posters/short talks on their work on mapping loss of antigen presentation at single cell resolution and on segmentation aware clustering for multiplexed imaging at RECOMB and RECOMB-CCB 2024.
Mar 5, 2024
New preprint on cell segmentation aware clustering for spatial expression assays
Our preprint on STARLING - a new method for clustering highly multiplexed imaging data while accounting for segmentation errors - is now up on bioRxiv along with a corresponding twitter thread.
Mar 1, 2024
New paper on single-cell data integration in imbalanced settings
Congratulations to Hassaan, Michael, and Chengxin whose paper understanding the impacts of dataset imbalanced on single-cell data integration is now published in Nature Biotechnology.
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