The people in our lab consult and collaborate with medical researchers in the institute and elsewhere in Melbourne and Australia on the analysis of their molecular data. We also develop new methods for analysing such data. We have a particular focus on molecular data collected by cancer researchers, but we also work with scientists who study immune and infectious diseases, and those who do research in basic biomedical science.
The lab’s vision is to contribute through our collaborations towards eliminating cancer and certain immune and infectious diseases. Our mission is to work towards achieving the best possible analyses of the data collected by our collaborators.
Our main contributions have been to collaborations with other researchers, for example, to the role of tissue-resident memory T cells in breast and lung cancer. We continue to develop a body of techniques concerning removing unwanted variation from bulk and single cell omic datasets.
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