Selected Publications

(updated on July 9, 2025; see homepage for full up-to-date list; homepage requires access to Google)

In the author list, boldface indicates group members; * represents equal contribution; and † represents the corresponding author.

1. 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 (accepted in principle).
2. Ge M, Miao J, Qi J, Zhou X, Lin Z†: TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference. Nature Communications 2025, 16: 6258. [paper link]
3. Tu JJ, Yan H, Zhang XF†, Lin Z†: Precise gene expression deconvolution in spatial transcriptomics with STged. Nucleic Acids Research 2025, 53: 4. [paper link]
4. Zhao K, So HC†, Lin Z†: scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis. Genome Biology 2024, 25: 223. [paper link]
5. 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. [paper link]
   
6. 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. [paper link]
7. 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. [paper link]
8. 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. [paper link]
9. Zhang Z, Chen S, Lin Z†: RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. Briefings in Bioinformatics 2022, bbac540. [paper link]
10. 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. [paper link]
11. 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. [paper link]
12. 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. Proceedings of the National Academy of Sciences of the United States of America 2022, 119(28): e2106858119. [paper link]
13. 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. [paper link]
    
14. 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. [paper link]
15. 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. [paper link]
16. 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. [paper link]
17. 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. [paper link]
18. 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. [paper link]
19. 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. [paper link]
20. 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. [paper link]
21. Mingfeng Li, …, BrainSpan Consortium*, …, Nenad Sestan: Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science. 2018, 362:6420. BrainSpan Consortium*: Zhixiang Lin is a member of the BrainSpan consortium. In the collaboration with Nenad Sestan, the method AC-PCA is implemented in this paper for novel biological findings.
22. 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.
23. 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. [paper link] [software link]
24. 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. [paper link]
25. 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. [paper link] [software link]
26. 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. Proceedings of the National Academy of Sciences of the United States of America. 2016, 113 (51): 14662-14667. [paper link] [software link]
27. 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. [paper link]
28. Willsey AJ, Sanders SJ, Li M, Tebbenkamp AT, Muhle RA, Reilly SK, Lin Z, Fertuzinhos S, Miller JA, Murtha MT, Bichsel C, Niu W, Cotney J, Gulhan A, Gockley J, Gupta A, Han W, He X, Homan E, Klei L, Lei J, Liu W, Liu L, Lu C, Xu X, Zhu Y, Mane SM, Lein ES, Wei L, Noonan JP, Roeder K, Devlin B†, Sestan N† and State MW†: Co-expression networks implicate human mid-fetal deep cortical projection neurons in the pathogenesis of autism. Cell 2013, 155 (5): 997-1007.