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.


Analysis pipelines and workshops:

Data resources:


Selected publications from Dr Yunshun Chen

1. 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, 1438. PMID: 27508061

2. Chen Y, Pal B, Visvader JE, Smyth GK. Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR. F1000Research 2017, 6, 2055. PMID: 29333247

3. McCarthy DJ#, Chen Y#, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 2012, 40, 4288-4297. PMID: 24485249. #Joint first.

4. Pal B#, Chen Y#, Vaillant F#, Capaldo BD, Joyce R, Song X, Bryant VL, Penington JS, Di Stefano L, Ribera NT, Wilcox S, Mann GB, kConFab, Papenfuss AT, Lindeman GJ, Smyth GK*, Visvader JE*. A single cell RNA atlas of human breast spanning normal, preneoplastic and tumorigenic states. EMBO Journal 2021, 40(11), e3107333. PMID: 33950524. #Joint first.

5. 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), 1627. PMID: 29158510. #Joint first.

6. Law CW, Chen Y, Shi W, and Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 2014, 15, R29. PMID: 24485249

7. Chen Y, Lun ATL, Smyth GK (2014). Differential expression analysis of complex RNA-seq experiments using edgeR. Statistical Analysis of Next Generation Sequence Data, Somnath Datta and Daniel S Nettleton (eds), Springer, New York, pages 51-74.

8. Pal B#, Chen Y#, Milevskiy MJG#, Vaillant F, Prokopuk L, Dawson C, Capaldo BD, Song X, Jackling F, Timpson P, Lindeman GJ, Smyth GK*, Visvader JE*. Single cell transcriptome atlas of mouse mammary epithelial cells across development. Breast Cancer Research. 2021. 23(1), 69. PMID: 34187545. #Joint first.

9. Chen Y, Pal B, Lindeman GJ, Visvader JE, Smyth GK. R code and downstream analysis objects for the scRNA-seq atlas of normal and tumorigenic human breast tissue. Scientific Data. 2022. https://doi.org/10.1038/s41597-022-01236-2.

10. Weeden CE, Chen Y, Ma SB, Hu Y, Ramm G, Sutherland KD, Smyth GK, Asselin-Labat ML (2017). Lung basal stem cells rapidly repair DNA damage using the error-prone non-homologous end-joining pathway. PLOS Biology 2017, 15(1), e2000731. PMID: 28125611.

Lab research projects

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