Dr Yunshun Chen, Lab Head | WEHI Researcher Profile

Q: Developing computational methods for analyzing spatial data

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 1) compare existing software tools using in-house or public datasets, and 2) develop novel methodologies for processing and analyzing spatial data from various platforms.

Q: Developing statistical methods for analyzing single-cell omic data

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 1) analyze different single-cell omics data and 2) integrate multiple modalities to better interpret the data.

Q: Develop statistical methods for transcripts and alternative splicing analysis

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.

About

Our research focuses on designing innovative statistical strategies and bioinformatics tools to analyze biomedical data generated from cutting-edge technologies. We collaborate closely with research scientists both within and outside our institute, leveraging our bioinformatics expertise to interpret their data and address biological questions.

We have extensive experience and strong expertise in:
• Differential analyses of gene/transcript expression, pathways, and DNA methylation
• Detecting alternative splicing and differential exon/transcript usage
• Integration of expression, methylation, epigenetic marks, and chromatin accessibility data
• Single-cell omics analysis
• Spatial data analysis
• Applications of sequencing, single-cell, and spatial technologies in cancer research

Our longstanding collaboration with the breast cancer lab at WEHI has yielded numerous significant discoveries and high-impact publications. We are eager to form additional collaborations with other research groups both within and outside our institute to apply our bioinformatics expertise to their projects.

Education

Grants and funding

Publications

Selected publications from Dr Yunshun Chen

Yip RKH, Er J, Qin L, Nguyen QH, Motyer A, Rimes JS, Light A, Mishi RD, Ling L, Anttila CJA, Tsui E, Amann-Zalcenstein D, Dowling MR, Rogers KL, Bowden R, Chen Y, Harrison SJ, Hawkins ED. Profiling the spatial architecture of multiple myeloma in human bone marrow trephine biopsy specimens with spatial transcriptomics.Blood. 2025;:10.1182/blood.2025028896

Cheng J, Jin X, Smyth GK, Chen Y. Benchmarking cell type annotation methods for 10x Xenium spatial transcriptomics data. BMC Bioinformatics. 2025;26(1):10.1186/s12859-025-06044-0

Chen Y, Chen L, Lun ATL, Baldoni PL, Smyth GK. edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. Nucleic Acids Research. 2025;53(2):10.1093/nar/gkaf018

Baldoni PL, Chen Y, Hediyeh-zadeh S, Liao Y, Dong X, Ritchie ME, Shi W, Smyth GK. Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR. Nucleic Acids Research. 2024;52(3):10.1093/nar/gkad1167

Joyce R, Pascual R, Heitink L, Capaldo BD, Vaillant F, Christie M, Tsai M, Surgenor E, Anttila CJA, Rajasekhar P, Jackling FC, Trussart M, Milevskiy MJG, Song X, Li M, Teh CE, Gray DHD, Smyth GK, Chen Y, Lindeman GJ, Visvader JE. Identification of aberrant luminal progenitors and mTORC1 as a potential breast cancer prevention target in BRCA2 mutation carriers. Nature Cell Biology. 2024;26(1):10.1038/s41556-023-01315-5

Pal B, Chen Y, Vaillant F, Capaldo BD, Joyce R, Song X, Bryant VL, Penington JS, Di Stefano L, Tubau Ribera N, Wilcox S, Mann GB, Papenfuss AT, Lindeman GJ, Smyth GK, Visvader JE. A single‐cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. The EMBO Journal. 2021;40(11):10.15252/embj.2020107333

Chen Y, Pal B, Visvader JE, Smyth GK. Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR. F1000Research. 2017;6:10.12688/f1000research.13196.1

Pal B, Chen Y, Vaillant F, Jamieson P, Gordon L, Rios AC, Wilcox S, Fu N, Liu KH, Jackling FC, Davis MJ, Lindeman GJ, Smyth GK, Visvader JE. Construction of developmental lineage relationships in the mouse mammary gland by single-cell RNA profiling. Nature Communications. 2017;8(1):10.1038/s41467-017-01560-x

Chen Y, Lun ATL, Smyth GK. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research. 2016;5:10.12688/f1000research.8987.1

McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research. 2012;40(10):10.1093/nar/gks042