Thomas Thorne

Shrinkage estimation of gene interaction networks in single-cell RNA sequencing data

This paper develops a sparse inverse covariance matrix estimation framework for scRNA-seq data to capture direct gene interactions, showing improved performance in high-dimensional and zero-inflated data.

Identifying sub-populations of cells in single cell transcriptomic data - a Bayesian mixture modelling approach to zero-inflation of counts

This paper introduces a Bayesian mixture model to identify cell sub-populations in single-cell RNA-seq data, addressing zero-inflation and providing uncertainty estimates in cluster assignments.

Approximate inference of gene regulatory network models from RNA-Seq time series data

The paper develops a method to infer gene regulatory networks from RNA-Seq time series data using a negative binomial model and sparse regression with a horseshoe prior, benchmarked on synthetic data and applied to human neuronal stem cell differentiation data.