Stephen D. Coleman

GLORB improves differential expression analysis by accounting for global expression shifts, outperforming existing methods in scenarios with widespread upregulation or downregulation.

This paper introduces a semi-supervised Bayesian method to integrate spatial proteomics data with other omic datasets to improve protein subcellular localization inference.

The paper introduces a consensus clustering method for Bayesian mixture models, showing it can uncover target clustering structures and handle large datasets efficiently.

This paper introduces a semi-supervised Bayesian mixture model for joint batch correction and classification, showing improved performance on low-dimensional data compared to existing methods.