Lizhong Chen (陈力仲)

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

About me

I am a research officer in Prof. Gordon K Smyth lab team at Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research (WEHI). 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. I received my M.S. and B.S. in Mathematics from Peking University on Jun. 2016 and Jul. 2013, respectively.

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

• Bioinformatics: limma, edgeR

• Model Selection: optimal model in high dimension via Gibbs sampler

• Model Averaging: preparing the candidate model set in high dimension

Publications

• Preprint

  • 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
    

• Journal articles

  • 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
    

• 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

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 exact calculation for unit deviance of Negative Binomial distribution. It is available on R-Bioconductor.

• R package: statmod (statmod: Statistical Modeling)
  We make the exact distributional calculations for 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.