The Ritchie laboratory develops analysis methods and open-source software (freely available as part of the Bioconductor project) that are tailored to new applications of genomic technology in biomedical research. Our time is divided evenly between methodological work and primary data analysis of in-house experiments from our collaborators and public datasets to provide new insights into gene regulation in health and disease.

Our major interests include:

  • statistical methods for modelling variation in RNA-sequencing data
  • software for interactive visualisation of gene expression data
  • software for the analysis of single-cell and long-read gene expression and methylation data
  • applying our data analysis skills to study epigenetic and genetic regulation in development and cancer together with our collaborators.

My skillset includes:

  • Bioinformatics
  • gene expression profiling
  • applied statistics
  • R programming
  • sequencing
  • genomics
  • computational biology
  • genetics
  • systems biology.


Selected publications from Prof Matthew Ritchie

Tian L, Jabbari JS, Thijssen R, Gouil Q, Amarasinghe SL, Voogd O, Kariyawasam H, Du MRM, Schuster J, Wang C, Su S, Dong X, Law CW, Lucattini A, Prawar YDJ, Collar Fernandez C, Chung JD, Naim T, Chan A, Ly CH, Lynch GS, Ryall JG, Anttila CJA, Peng H, Anderson MA, Flensburg C, Majewski I, Roberts AW, Huang DCS, Clark MB, Ritchie ME. Comprehensive characterization of single cell full-length isoforms in human and mouse with long-read sequencing. Genome Biol. 2021, 22:310. PMID: 34763716

Su S, Gouil Q, Blewitt ME, Cook D, Hickey PF, Ritchie ME. NanoMethViz: an R/Bioconductor package for visualizing long-read methylation data. PLoS Comp Biol. 2021, 17(10):e1009524. PMID: 34695109

Tian L, Dong X, Freytag S, Lê Cao KA, Su S, JalalAbadi A, Amann-Zalcenstein D, Weber TS, Seidi A, Jabbari JS, Naik SH, Ritchie ME. Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments. Nat Methods. 2019, 16(6):479-487. PMID: 31133762

Gigante S, Gouil Q, Lucattini A, Keniry A, Beck T, Tinning M, Gordon L, Woodruff C, Speed TP, Blewitt ME, Ritchie ME. Using long-read sequencing to detect imprinted DNA methylation. Nucleic Acids Res. 2019, 47(8):e46. PMID: 30793194

Tian L, Su S, Dong X, Amann-Zalcenstein D, Biben C, Seidi A, Hilton DJ, Naik SH, Ritchie ME. scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data. PLoS Comput Biol. 2018,14(8):e1006361. PMID: 30096152

Su S, Law CW, Ah-Cann C, Asselin-Labat ML, Blewitt ME, Ritchie ME. Glimma: interactive graphics for gene expression analysis. Bioinformatics. 2017, 33(13):2050-52. PMID: 28203714

Alhamdoosh M, Ng M, Wilson NJ, Sheridan JM, Huynh H, Wilson MJ, Ritchie ME. Combining multiple tools outperforms individual methods in gene set enrichment analyses. Bioinformatics. 2017, 33(3):414-24. PMID: 27694195

Law CW, Alhamdoosh M, Su S, Smyth GK, Ritchie ME. RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR, F1000Research, 2016, 5:1408. PMID: 27441086

Liu R, Holik AZ, Su S, Jansz N, Chen K, Leong HS, Blewitt ME, Asselin-Labat ML, Smyth GK, Ritchie ME. Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res. 2015;43(15):e97. PMID: 25925576

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. PMID: 25605792

Lab research projects

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