I am a postdoctoral fellow at the Department of Statistics, University of British Columbia, and the Department of Molecular Oncology, BC Cancer Agency, where I'm supervised by Sohrab Shah and Alexandre Bouchard-Côté. I am funded by a Banting Fellowship from the Canadian Institutes of Health Research and a postdoctoral fellowship from the Canadian Statistical Sciences Institute. I am also a fellow of the UBC Data Science Institute.
My research focuses on Bayesian models and scalable inference for single-cell and cancer genomics.
My research involves applications of Bayesian probabilistic modelling to genomics, with a focus on latent variable modelling and scalable inference.
Allen W Zhang, Ciara O'Flanagan, Elizabeth Chavez, Jamie LP Lim, Andrew McPherson, Matt Wiens, Pascale Walters, Tim Chan, Brittany Hewitson, Daniel Lai, Anja Mottok, Clementine Sarkozy, Lauren Chong, Tomohiro Aoki, Xuehai Wang, Andrew P Weng, Jessica N McAlpine, Samuel Aparicio, Christian Steidl, Kieran R Campbell*, Sohrab P Shah*
Automated, probabilistic assignment of cells to known cell types in single-cell RNA-seq data accounting for patient and batch specific effects.
Kieran R Campbell et al. Genome Biology 2019
A probabilistic model to assign gene expression states to cancer clones by integrating scRNA-seq and scDNA-seq data, implemented in Tensorflow.
Christopher Yau and Kieran R Campbell . Biophysical Reviews 2019
Kieran R Campbell and Christopher Yau Nature Communications 2018
The pseudotime estimation problem where samples may have different genetic or environmental backgrounds for which we derive a novel probabilistic latent variable model and implement fast variational Bayes inference.
Charmaine Lang*, Kieran R Campbell*, et al. Cell Stem Cell 2018
Kieran R Campbell and Christopher Yau Bioinformatics 2018
Deriving single-cell pseudotimes using only small panels of marker genes and a Bayesian nonlinear factor analysis model, with inference performed using Stan.
Kieran R Campbell and Christopher Yau, Wellcome Open Research 2017
Modelling bifurcations in single-cell RNA-seq data using a Bayesian mixture of factor analysers with a hierarchical prior structure, with fast inference implemented via Gibbs sampling.
Kieran R Campbell and Christopher Yau, PLOS Computational Biology 2016
We use Gaussian Process Latent Variable Models (GPLVM) to estimate single-cell pseudotimes and use Bayesian inference to characterise the inherent uncertainty in such analyses.