Donnelly group

Davis McCarthy

Davis McCarthy

DPhil student


Wellcome Trust Centre for Human Genetics, Roosevelt Dr.
Oxford, OX3 7BN


Work summary

I am a DPhil Student with Peter Donnelly. My current work involves the development of methods for analysing large sequencing datasets and the analysis of data from studies investigating human disease. I have recently been involved in analysis for the WGS500 and GoT2D projects.

Previously I worked on the development of methods for differential expression analysis of RNA-Seq and microarray data, contributing to the edgeR and limma software packages.

Selected publications

Anders, S., McCarthy, D.J., Chen Y., Okoniewski, M., Smyth, G.K., Huber, W. and Robinson, M.D. Count-based differential expression analysis of RNA sequencing data using R and Bioconductor. Nature Protocols. 2013; 8: 1765-1786, doi:10.1038/nprot.2013.099.

Lund, S.P., Nettleton, D., McCarthy, D.J., Smyth, G.K. Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates. Statistical Applications in Genetics and Molecular Biology2012: 11(5), Article 8. 

McCarthy, D.J., Chen, Y. and Smyth, G.K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research. 2012; 40(10): 4288-4297. First published online January 28, 2012, doi:10.1093/nar/gks042. 

Campbell, D.J., Zhang, Y., Kelly, D.J., Gilbert, R.E., McCarthy, D.J., Shi, W. and Smyth, G.K. Aliskiren increases bradykinin and tissue kallikrein mRNA levels in the heart. Clinical and Experimental Pharmacology and Physiology. 2011; 38(9): 623-631. 

Robinson, M.D., McCarthy, D.J. and Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics2010; 26(1): 139-140. 

McCarthy, D.J. and Smyth, G.K. Testing significance relative to a fold-change threshold is a TREAT. Bioinformatics. 2009: 25(6): 765-771.


edgeR: Empirical analysis of digital gene expression data in R. Designed for the analysis of RNA-Seq data.
limma: Linear models for microarray data.

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