Terry Speed completed a BSc (Hons) in mathematics and statistics at the University of Melbourne and a PhD in mathematics and Dip Ed at Monash University. He has held appointments at the University of Sheffield, the University of Western Australia, the University of California at Berkeley, and with the CSIRO in Canberra. In 1997 he took up an appointment with WEHI, where he is now an Honorary Fellow and lab head in the Bioinformatics Division.
Professor Speed has worked on the applications of statistics and bioinformatics to problems in genetics and genomics for over 30 years, and his current research has a focus on datasets from cancer and immunology.
His research interests are broad, but include the statistical and bioinformatic analysis of microarray, DNA sequence and mass spectrometry data from genetics, genomics, proteomics and metabolomics. He works with molecular data at several different levels, from the lowest level where the data come directly from the instruments that generate it, up to the tasks of data integration, and of relating molecular to clinical data. Technologies that generate molecular data are constantly evolving, so that he is always presented with novel challenges.
Australia, University of Melbourne, BSc (Hons), 1965
Australia, Monash University, PhD DipEd, 1969
Australia, University of Western Australia, Hon DSc, 2005
USA, University of Chicago, Hon DSc, 2014
School of Mathematics & Statistics, University of Melbourne
2014, Eureka Prize for Scientific Leadership
2014, Jerome Sacks Award for Cross-Disciplinary Research
2013, Prime Minister’s Prize for Science
2012, Victoria Prize for Science and Innovation in the Life Sciences
2014-2017, Program Grant, National Health and Medical Research Council
2009-2014, Australia Fellowship, National Health and Medical Research Council
Molania R, Foroutan M, Gagnon-Bartsch JA, Gandolfo LC, Jain A, Sinha A, Olshansky G, Dobrovic A, Papenfuss AT, Speed TP. Removing unwanted variation from large-scale RNA sequencing data with PRPS. Nat Biotechnol. 2023 41(1):82-95. PMID: 36109686.
Runx3 drives a CD8+ T cell tissue residency program that is absent in CD4+ T cells. Fonseca R, Burn TN, Gandolfo LC, Devi S, Park SL, Obers A, Evrard M, Christo SN, Buquicchio FA, Lareau CA, McDonald KM, Sandford SK, Zamudio NM, Zanluqui NG, Zaid A, Speed TP, Satpathy AT, Mueller SN, Carbone FR, Mackay LK. Nat Immunol. 2022 23(8):1236-1245.
RUV-III-NB: normalization of single cell RNA-seq data. Salim A, Molania R, Wang J, De Livera A, Thijssen R, Speed TP. Nucleic Acids Res. 2022 50(16):e96. PMID: 35758618.
Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets
Marie Trussart, Charis E Teh, Tania Tan, Lawrence Leong, Daniel H Gray Terence P Speed. Elife 2020 9:e59630. PMID: 32894218.
Using long-read sequencing to detect imprinted DNA methylation. Gigante S, Gouil Q, Lucattini A, Keniry A, Beck T, Tinning M, Gordon L, Woodruff C, Speed TP, Blewitt ME, Ritchie ME. Nucleic Acids Res. 2019 47(8):e46. PMID: 30793194.
Large scale datasets generated by different omics technologies present unique challenges in terms of normalization and integration. Our project focuses on expanding biostatistical and bioinformatics methods for such challenges. We are particularly interested in the RUV-III normalization methods, which have shown great promise in dealing with the challenges presented by large scale datasets from The Cancer Genome Atlas. RUV-III-PRPS is a novel strategy which uses pseudo-replicates (PR) of pseudo-samples (PS) to normalize RNA-seq data in situations when technical replicates are not available. We are developing user-friendly tools that enable researchers to create diagnostic plots before and after normalization to assess the quality and consistency of their data.
Team members: Marie Trussart, Terry Speed, together with Ramyar Molania from the Papenfuss lab at WEHI.
We analyse bulk and single cell RNA-seq data in order to elucidate the biology of an important class of immune cells: tissue resident memory T cells. The hope is that, if we can understand the fundamental biology of these cells, we might be able to harness them to cure diseases like cancer. So far we have characterised important ways in which these cells differ between organs (e.g. skin, liver, gut, and lung), and we have discovered key genes involved in their function (e.g. Runx3).
Team members: Luke Gandolfo, Terry Speed, together with members of the Mackay Lab at the Peter Doherty Institute.
Our focus here is the simulation of datasets which capture realistic library size, batch and biology (though not gene-gene) associations of gene expression within single cells. Continuing.
Team members: Jianan Wang, Terry Speed
This project is concerned with improving taxonomic resolution of contents of a microbiome – what organisms are present and the proportions of each. Long reads provide a very promising path for such improvement. Our focus is on enabling use of long-read sequencing technologies – specifically, but not solely, Nanopore – and also not restricted to long reads. Nanopore technology also facilitates investigation of possible epigenetic factors that may be operative. There is an emerging collaboration with the Coussens Lab, with an interest in the bronchial microbiome and possible spatial differences in both the microbiome and its epigenome. Nanopore technology provides a pathway into advanced genomics without requiring high capitalisation, while being relatively easily fieldable and inexpensive per data unit. Continuing.
Team Members: Chris Woodruff, Terry Speed