Lizhong Chen

Lizhong Chen Research Officer Bioinformatics & Computationl Biology Division Walter and Eliza Hall Institute of Medical Research (WEHI) Contact: Email, Google Scholar, Linkedin, Github, CV

About me

I am a research officer in Prof. Gordon K Smyth lab team at Bioinformatics & Computationl Biology Division, WEHI. My major work is to develop new statistical and computational methods for the analysis of the sequencing data. Previously, I completed my PhD degree in Statistics from the University of Melbourne under the supervision of A/Prof. Guoqi Qian, Prof. Yuriy Kuleshov and Dr. Tingjin Chu in Jul. 2022, where I focused on the feature or variable selection and model averaging for generalized linear models. I received my M.S. and B.S. in Mathematics from Peking University in Jun. 2016 and Jul. 2013, respectively, where my main interest was homotopy theory of spheres and Lie groups.

Education

• Ph.D. in Statistics, School of Mathematics and Statistics, The University of Melbourne, Victoria, Australia, Feb. 2017 - Feb. 2022

• M.S. in Mathematics, School of Mathematical Science, Peking University, Beijing, China, Sep. 2013 - Jun. 2016

• B.S. in Mathematics, Yuanpei College, Peking University, Beijing, China, Sep. 2009 - Jul. 2013

Research Interests

• Generalized linear models (GLM), quasi-likelihood and deviacne statistics

• Feature selection or variable selection method and model averaging

• Empirical bayes, prior information estimation and mulitple hypothesis tests

• Differential analysis of gene expression and applications

• Spatial transcripts and image analysis

Publications

• Journal articles

  • Chen Y, Chen L, Lun AT, Baldoni PL and 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, 53(2), 2025. doi
    

  • Baldoni PL, Chen L, Smyth GK. Faster and more accurate assessment of differential transcript expression with Gibbs sampling and edgeR v4, NAR Genomics and Bioinformatics, 6(4), 2024. doi

  • Qian G, Chen L and Kuleshov Y. Improving Methodology for Tropical Cyclone Seasonal Forecasting in the Australian and the South Pacific Ocean Regions by Selecting and Averaging Models via Metropolis–Gibbs Sampling, Remote Sensing, 14(22), 5872. doi
    

• Preprint

  • Baldoni PL, Chen L, Li M, Chen Y, Smyth GK. Dividing out quantification uncertainty enables assessment of differential transcript usage with diffSplice. bioRxiv, 2025. doi
    

  • Baldoni PL, Chen L, Smyth GK. Faster and more accurate assessment of differential transcript expression with Gibbs sampling and edgeR 4.0. bioRxiv, 2024. doi
    

  • Wang J, Chen L, Thijssen R, Phipson B, Speed TP. GLMsim: a GLM-based single cell RNA-seq simulator incorporating batch and biological effects. bioRxiv, 2024. doi
    

  • Chen Y, Chen L, Lun AT, Baldoni PL and Smyth GK. edgeR 4.0: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. bioRxiv, 2024. doi
    

• Ph.D. Thesis

  • Model selection and averaging by Gibbs sampler with a tropical cyclone seasonal forecasting application, Feb. 2022. Minerva Access

Presentations

• edgeR quasi-likelihood: correcting deviance for bias, 2022, WEHI Bioinformatics Divsion seminar

• Extending edgeR for small counts and large samples, 2023, WEHI Bioinformatics Divsion seminar

• Applying edgeR for alternative splicing analysis, 2024, WEHI Bioinformatics Divsion seminar

Softwares

• R package: edgeR (Empirical Analysis of Digital Gene Expression Data in R)
  We develop the new quasi-likelihood method for differential expression analysis using the adjusted deviance statistics, which expands the functionality and improve the support for small counts and larger dataset. It is available on R-Bioconductor.

• R package: limma (Linear Models for Microarray and Omics Data)
  We update the prior information estimation in the empirical Bayes method using the weighted lowess trend and profile likelihood. It is available on R-CRAN.

• R package: statmod (statmod: Statistical Modeling)
  We make the exact calculations for the approximation of the first two moments of the unit deviances of Binomial, Poisson and Negative Binomial distributions. It is available on R-CRAN.

• R package: IBGS (Iterated Blockwise Gibbs Sampler)
  IBGS is an MCMC search algorithm designed to find the best model in the high dimensional data given a model selection criterion such as AIC, BIC and so on. Moreover, it can identify those most important covariates having the significant influence on the response. It is available on Github.

Supervision

• Lei Qin, PhD (Medical Biology), University of Melbourne, co-supervised with Yunshun Chen and Gordon Smyth

• Xueming Li, PhD (Mathematics & Statistics), University of Melbourne, co-supervised with Guoqi Qian

• Shama Deb, Master of Science (Mathematics & Statistics), University of Melbourne, co-supervised with Gordon Smyth