• Tsinghua University, Biological Sciences, B.S.;
• Yale University, Biomathematics, Bioinformatics and Computational Biology, Ph.D.

Work Experience

• Present: Department of Statistics and Data Science, The Chinese University of Hong Kong, Associate Professor

Zhixiang Lin, PhD, is an Associate Professor at the Department of Statistics and Data Science, The Chinese University of Hong Kong. He received his B.S. in Biological Sciences from Tsinghua University and his Ph.D. in Biomathematics, Bioinformatics and Computational Biology from Yale University. His research focuses on Bayesian statistics, big data, computational biology and bioinformatics, and statistical machine learning. He has published in leading journals including Nature Communications, PNAS, Genome Biology, and Briefings in Bioinformatics, and serves as Principal Investigator on multiple RGC-funded projects.

Research Projects

Hosted Projects：

2023-2026: A unified framework for jointly modeling and integrative analysis of single-cell multi-omics data, RGC-GRF 14300923, Principal Investigator;

2020-2023: Statistical methods for dimension reduction in single-cell genomics leveraging bulk genomic data, RGC-GRF 14301120, Principal Investigator;

2019-2022: A unified framework for jointly modeling and clustering multiple data types in single-cell genomics, RGC-ECS 24301419, Principal Investigator

Participated Projects：

2023-2026: Uncovering the Whole Spectrum of Genetic Variations Underlying Schizophrenia and its Prognosis, RGC-CRF C4003-23Y, Co-Principal Investigator

Academic Achievements：
 

Representative Publications (past 5-10 years, in order of impact)：

1. Qi J, Ge M, Miao J, Zhou X, Lin Z†: STIFT: Spatiotemporal Transcriptomics Integration Through Spatially Informed Multi-Timepoint Bridging. Briefings in Bioinformatics (In press).
2. Miao J, Li J, Xin J, Tu J, Ge M, Qi J, Zhou X, Zhu Y, Yang C†, Lin Z†: MultiGATE: Integrative Analysis and Regulatory Inference in Spatial Multi-Omics Data via Graph Representation Learning. Nature Communications 2025, 16: 9403.
3. Wang Z, Zeng Y, Tan Z, Chen Y, Huang X, Zhao H†, Lin Z†, Yang C†: A unified framework for identification of cell-type-specific spatially variable genes in spatial transcriptomic studies. PNAS 2025, 122(46): e2503952122.
4. Wang Y, Lin Z†, Wang T†: InterVelo: A Mutually Enhancing Model for Estimating Pseudotime and RNA Velocity in Multi-Omic Single-Cell Data. Bioinformatics 2025, btaf500.
5. Ge M, Miao J, Qi J, Zhou X, Lin Z†: TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference. Nature Communications 2025, 16: 6258.
6. Tu JJ, Yan H, Zhang XF†, Lin Z†: Precise gene expression deconvolution in spatial transcriptomics with STged. Nucleic Acids Research 2025, 53: 4.
7. Zhao K, So HC†, Lin Z†: scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis. Genome Biology 2024, 25: 223.
8. Zhao K, Huang S, Lin C, Sham PC, So HC†, Lin Z†: INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis. PLoS Genetics 2024, 20(3): e1011189.
9. Li J, Wang J, and Lin Z†: SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics. Briefings in Bioinformatics 2024, 25(1), bbad490.
10. Li C, Chan TF, Yang C† and Lin Z†: stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics. Bioinformatics 2023, 39(10), btad642.
11. Wan X, Xiao J, Tam S, Cai M, Sugimura R, Wang Y, Wan X, Lin Z†, Angela Ruohao Wu AR† and Yang C†: SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models. Nature Communications 2023, 14: 7848.
12. Zhang Z, Chen S, Lin Z†: RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. Briefings in Bioinformatics 2022, bbac540.
13. Zeng P, Ma Y, Lin Z†: scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data. Bioinformatics 2022, btac739.
14. Ming J, Lin Z, Wan X, Yang C†, Wu AR†: FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms. Briefings in Bioinformatics 2022, 23(5): bbac167. 15. Hu X, Zhao J, Lin Z, Wang Y, Peng H, Zhao H†, Wan X†, Yang C†: MR-APSS: a unified approach to Mendelian Randomization accounting for pleiotropy and sample structure using genome-wide summary statistic. PNAS 2022, 119(28): e2106858119.
15. Ma Y, Sun Z, Zeng P, Zhang W, Lin Z†: JSNMF enables effective and accurate integrative analysis of single-cell multiomics data. Briefings in Bioinformatics 2022, bbac105.
16. Wangwu J, Sun Z, Lin Z†: scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation. Bioinformatics 2021, 37(21):3874–3880.
17. Chen S, Yan G, Zhang W, Li J, Jiang R†, Lin Z†: RA3 is a reference-guided approach for epigenetic characterization of single cells. Nature Communications 2021, 12:2177.
18. Zeng P, Lin Z†: coupleCoC+: an information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data. PLOS Computational Biology 2021, 17(6): e1009064.
19. Zeng P, Wangwu J, Lin Z†: Coupled Co-clustering-based Unsupervised Transfer Learning for the Integrative Analysis of Single-Cell Genomic Data. Briefings in Bioinformatics 2020, bbaa347.
20. Zhang S, Yang L, Yang J, Lin Z, Ng KM: Dimensionality reduction for single cell RNA sequencing data using constrained robust non-negative matrix factorization. NAR Genomics and Bioinformatics 2020, 2(3): lqaa064.
21. Lin Z†, Zamanighomi M, Daley T, Ma S and Wong WH†: Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression. Statistical Science 2020, 35(1):2-13.
22. Zhang W, Wangwu J and Lin Z†: Weighted K-means Clustering with Observation Weight for Single-cell Epigenomic Data. Statistical Modeling in Biomedical Research, book chapter p37-64.
23. Mingfeng Li, ..., BrainSpan Consortium*, ..., Nenad Sestan: Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 2018, 362:6420. (Zhixiang Lin is a member of the BrainSpan consortium.)
24. Daley T†, Lin Z, Bhate S, Lin X, Liu Y, Wong WH, and Qi L: CRISPhieRmix: a hierarchical mixture model for CRISPR pooled screens. Genome Biology 2018, 19:159.
25. Zamanighomi M, Lin Z, Daley T, Chen Xi, Zhana Duren, Schep A, Greenleaf WJ, and Wong WH†: Unsupervised clustering and epigenetic classification of single cells. Nature Communications 2018, 9:2410.
26. Zamanighomi M, Lin Z, Wang Y, Jiang R, and Wong WH†: Predicting transcription factor binding motifs from DNA-binding domains, chromatin accessibility, and gene expression data. Nucleic Acids Research 2017, 45(10): 5666-5677.
27. Lin Z, Wang T, Yang C, and Zhao H†: On joint estimation of Gaussian graphical models for spatial and temporal data. Biometrics 2017, 73: 769-779.
28. Lin Z, Yang C, Zhu Y, Duchi JC, Fu Y, Wang Y, Jiang B, Zamanighomi M, Xu X, Li M, Sestan N, Zhao H†, and Wong WH†: Simultaneous dimension reduction and adjustment for confounding variation. PNAS 2016, 113(51): 14662-14667.
29. Lin Z, Sanders SJ, Li M, Sestan N, State MW and Zhao H†: A Markov Random Field-based approach to characterizing human brain developments using spatial-temporal transcriptome data. Annals of Applied Statistics 2015, 9(1): 429-451.

Research Awards：

· Awarded to my PhD student Jishuai Miao, National Natural Science Foundation of China (NSFC) under the Basic Research Scheme (2025) for PhD Students [國家自然科學基金 – 青年學生基礎研究項目 (博士研究生) ] for his research project titled: “Integrated Quantitative Modeling and Evaluation of Cell-Cell Communication and Gene Regulatory Networks from Spatial Multi-omics Data”

· Predoctoral Award in Basic Science, Association of Chinese Geneticists in America, 2012