Kieran R Campbell

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.

Previously I was a PhD student at Oxford University supervised by Chris Yau. My thesis is available online.


Projects and publications

My research involves applications of Bayesian probabilistic modelling to genomics, with a focus on latent variable modelling and scalable inference.


Probabilistic cell type assignment of single-cell transcriptomic data reveals spatiotemporal microenvironment dynamics in human cancers

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.


clonealign: statistical integration of independent single-cell RNA & DNA-seq from human cancers

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.

Bayesian Statistical Learning for Big Data Biology

Christopher Yau and Kieran R Campbell . Biophysical Reviews 2019

Uncovering pseudotemporal trajectories with covariates from single cell and bulk expression data

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.

Single-Cell Sequencing of iPSC-Dopamine Neurons Reconstructs Disease Progression and Identifies HDAC4 as a Regulator of Parkinson Cell Phenotypes

Charmaine Lang*, Kieran R Campbell*, et al. Cell Stem Cell 2018

A descriptive marker gene approach to single-cell pseudotime inference

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.

Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers

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.

switchde: inference of switch-like differential expression along single-cell trajectories

Kieran R Campbell and Christopher Yau, Bioinformatics 2017

Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

Davis J. McCarthy, Kieran R Campbell, Aaron T. L. Lun, and Quin F. Wills, Bioinformatics 2017

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference

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.