The Phipson laboratory works on both novel bioinformatics methods development as well as collaborative analysis projects with scientists within and external to the Institute. We use sophisticated statistical modelling to reveal insights into normal development and disease.

A major research focus of the Phipson laboratory is on developing new bioinformatics methods for data generated from single cell technologies. We also work on genomic data generated from other high-throughput technologies including RNA-seq and DNA methylation. Our novel bioinformatics methods are implemented as publicly available open source software through the Bioconductor project.

Current research areas of interest include:
– Developing statistical methods for designed single cell experiments with biological replication
– Advancing DNA methylation analysis for current and emerging technologies


Selected publications from Dr Belinda Phipson

1. Maksimovic J, Oshlack A, and Phipson B. Gene set enrichment analysis for genome-wide DNA methylation data. Genome Biology 2021, 22(1), 1-26. PMID: 34103055

2. Boon Sim C, Phipson B, Ziemann M, Rafehi H, Mills RJ, Watt KI, Abu-Bonsrah KD, Kalathur RKR, Voges HK, Dinh DT, Huurne MT, Vivien CJ, Kaspi A, Kaipananickal H, Hidalgo A, Delbridge LMD, Robker RL, Gregorevic P, Dos Remedios CG, Lal S, Piers AT, Konstantinov IE, Elliott DA, El-Osta A, Oshlack A, Hudson JE, and Porrello ER. Sex-Specific Control of Human Heart Maturation by the Progesterone Receptor. Circulation. 2021, 143(16), 1614-1628. PMID: 33682422

3. Combes AN*, Phipson B*, Lawlor KT, Dorison A, Patrick R, Zappia L, Harvey RP, Oshlack A, and Little MH (*Co-first author). Single cell analysis of the developing mouse kidney provides deeper insight into marker gene expression and ligand-receptor crosstalk. Development. 2019, 146(12), dev178673. PMID: 31118232

4. Phipson B, Er PX, Combes AN, Forbes TA, Howden SE, Zappia L, Yen H-J, Lawlor KT, Hale LJ, Sun J, Wolvetang E, Takasato M, Oshlack A, and Little MH. Evaluation of variability in human kidney organoids. Nature Methods. 2019, 16(1), 79. PMID: 30573816

5. Phipson B, Zappia L, and Oshlack A. Gene length and detection bias in single cell RNA sequencing protocols. F1000Research. 2017, 6, 595. PMID: 28529717

6. Phipson B, Lee S, Majewski IJ, Alexander WS, and Smyth GK. Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Annals of Applied Statistics. 2016, 10(2), 946-963. PMID: 28367255

7. Phipson B, Maksimovic J, Oshlack A. missMethyl: an R package for analyzing data from Illumina’s HumanMethylation450 platform. Bioinformatics. 2016, 32 (2): 286–288. PMID: 26424855

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

9. Phipson B, and Oshlack A. DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging. Genome Biology. 2014, 15, 465. PMID: 25245051

10. Phipson B, and Smyth GK (2010). Permutation p-values should never be zero: calculating exact p-values when permutations are randomly drawn. Statistical Applications in Genetics and Molecular Biology. 2010. Volume 9, Issue 1, Article 39. PMID: 21044043

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