We are a group of bioinformaticians dedicated to developing statistical methods and software tools to analyze sequencing, single-cell and spatial data in cancer research. Our ultimate goal is to contribute to the development of more effective cancer treatments and therapies, which can help improve the lives of millions of people worldwide.
Our research focuses on analyzing large-scale datasets from single cells and tissues, which provide unprecedented insights into the molecular and cellular basis of cancer. By applying advanced statistical strategies and bioinformatics methods, we can identify key molecular pathways and cellular processes that drive cancer progression and metastasis. Our software tools enable researchers to analyze these complex datasets more efficiently and accurately, accelerating the pace of discovery in cancer research.
Our research has already had a significant impact on the cancer research community, with our tools and methods being widely adopted by researchers around the world. By improving our understanding of cancer biology and identifying new targets for therapy, our work has the potential to improve outcomes for cancer patients and ultimately save lives.
We are grateful for the support of donors who help make this important work possible.
Our contributions to the scientific community and research knowledge have been significant, achieved through method development, scientific collaboration, and education.
Spatial technology has gained popularity in recent years as the “next generation” of single-cell RNA sequencing, providing extra layers of spatial and morphological information for insights on cell type composition and formation within tissue specimens. However, due to its novelty, there is no gold standard on data analysis.
Our team has recently adopted the 10X Visium platform to study mammary gland structure in human breast tissue and plans to explore other platforms such as MERSCOPE and Xenium.
This project aims to
Single-cell transcriptomics has emerged as the gold standard method for exploring cellular heterogeneity and cell type composition in tissues and organisms. With the advent of multi-omics technologies, we can now measure multiple types of molecules from individual cells, allowing us to study the correlation between gene expression, DNA methylation, and chromatin accessibility at the single-cell level. This will deepen our understanding of key biological processes and underlying mechanisms.
This project aims to develop novel statistical strategies and software tools to:
The Rsubread-limma-edgeR pipeline has established itself as the preeminent method for conducting differential gene expression analysis on short-read RNA-seq data, from reads to genes to pathways. This pipeline has the capability to identify differential exon usage and alternative splicing by analyzing the read counts mapped to exons and exon-exon junctions, and also has the potential to be applied to long-read data analysis.
This project aims to establish new analysis pipelines, develop new statistical methods for transcript and alternative splicing analysis of short-read bulk RNA-seq, long-read bulk RNA-seq and long-read scRNA-seq.
Our lab is jointly affiliated with the ACRF Cancer Biology and Stem Cells (CBSC) division and the Bioinformatics division at WEHI.
We closely collaborate with wet labs in the CBSC division, and we aim to expand our collaborations with other labs, both within and outside our institute.
Our team is seeking to recruit students and researchers with a background in computational biology, computer science or statistics, who have a keen interest in pursuing a career in medical research.