Wei Vivian Li

PhD Student in Statistics
liw@ucla.edu

Wei (Vivian) is a Ph.D. candidate at UCLA Department of Statistics. She has a B.S. in Statistics from Huazhong University of Science and Technology. She is developing and improving statistical methods to uncover the hidden information in large-scale genomic data. Her research currently focuses on statistical modeling of both tissue-level and single-cell RNA sequencing data, and various applications in practical biological and biomedical questions. She is also interested in the comparative analysis based on transcriptomic or epigenomic profiles from multiple tissues or cell types. She enjoys both the development of statistical methods and the application of these methods to real-world problems.

[ Homepage ] [ Github ]

Research interests:

  1. Cost-sensitive classification methods
  2. Statistical modeling of bulk RNA-seq data
    • Transcript assembly and quantification
    • Statistical inference
    • Biomedical applications to cancer samples
  3. Statistical modeling of single-cell RNA-seq data
    • Imputation of gene expression
    • Experimental design
  4. Comparative transcriptomics and epigenomics

 

Publications:

Li, W.V. and Li, J.J. (2019). A statistical simulator scDesign for rational scRNA-seq experimental design. Bioinformatics 35(14):i41–i50. [ ISMB/ECCB 2019 ] [ SOFTWARE ]

Ge, X.*, Zhang, H.*, Xie, L., Li, W.V., Kwon, S.B., and Li, J.J. (2019). EpiAlign: an alignment-based bioinformatic tool for comparing chromatin state sequences. Nucleic Acids Research gkz287. [ SOFTWARE ] [ WEBSITE ]

Li, W.V.*, Li, S.*, Tong, X., Deng, L., Shi, H., and Li, J.J. (2019). AIDE: annotation-assisted isoform discovery and abundance estimation with high precision. bioRxiv[ SOFTWARE ]

Li, W.V. and Li, J.J. (2018). Modeling and analysis of RNA-seq data: a review from a statistical perspective. Quantitative Biology 6(3):195-209.

Li, W.V.*, Zhao, A., Zhang, S., and Li, J.J.* (2018). MSIQ: joint modeling of multiple RNA-seq samples for accurate isoform quantification. Annals of Applied Statistics 12(1):510-539. [ SOFTWARE ] [ COLOR PDF ]

Li, W.V.Chen, Y., and Li, J.J. (2017). TROM: a testing-based method for finding transcriptomic similarity of biological samples. Statistics in Biosciences 9(1):105-136. [ SOFTWARE ]

Li, W.V.Razaee, Z.S., and Li, J.J. (2016). Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states. BMC Genomics 17(Supp 1):10. [ SOFTWARE ]