Current/past group members are highlighted in boldface
* first authors with equal contribution
† corresponding authors with equal contribution
2024
[34] Kai Zhao, Hon-Cheong So†, Zhixiang Lin†: scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis. Genome Biology 2024, 25: 223. [paper link]
[33] Kai Zhao, Sen Huang, Cuichan Lin, Pak Chung Sham, Hon-Cheong So†, Zhixiang Lin†: INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis. PLoS Genetics 2024, 20(3): e1011189. [paper link]
[32] Jinzhao Li, Jiong Wang, and Zhixiang Lin†: SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics. Briefings in Bioinformatics 2024, 25(1), bbad490. [paper link]
2023
[31] Chen Li, Ting-Fung Chan, Can Yang† and Zhixiang Lin†: stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics. Bioinformatics 2023, 39(10), btad642. [paper link]
[30] Xiaomeng Wan*, Jiashun Xiao*, Sindy Sing Ting Tam, Mingxuan Cai, Ryohichi Sugimura, Yang Wang, Xiang Wan, Zhixiang Lin†, Angela Ruohao Wu† and Can Yang†: Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nature Communications 2023, 14: 7848. [paper link]
[29] Pengcheng Zeng† and Zhixiang Lin: scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2023 (in press).
[28] Wenyu Zhang and Zhixiang Lin†: iPoLNG - An unsupervised model for the integrative analysis of single-cell multiomics data. Frontiers in Genetics. 2023; 14: 998504. [paper link]
2022
[27] Zheng Zhang, Shengquan Chen, Zhixiang Lin†: RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. Briefings in Bioinformatics 2022, bbac540. [paper link]
[26] Pengcheng Zeng, Yuanyuan Ma, Zhixiang Lin†: 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]
[25] Zhixiang Lin: Integrative analyses of single-cell multi-omics data: a review from a statistical perspective. Handbook of Statistical Bioinformatics (2nd Edition) 2022 (in press).
[24] Jingsi Ming*, Zhixiang Lin*, Xiang Wan, Can Yang†, Angela Ruohao Wu†: FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms. Briefings in Bioinformatics 2022, bbac167 [paper link].
[23] Jia Zhao*, Gefei Wang*, Jingsi Ming, Zhixiang Lin, Yang Wang, Tabula Microcebus Consortium, Angela Ruohao Wu†, Can Yang†. Adversarial domain translation networks enable fast and accurate large-scale atlas-level single-cell data integration. Nature Computational Science 2022, 2:317-330. [paper link]
[22] Yuanyuan Ma, Zexuan Sun, Pengcheng Zeng, Wenyu Zhang, Zhixiang Lin†: JSNMF enables effective and accurate integrative analysis of single-cell multiomics data. Briefings in Bioinformatics 2022, bbac105. [paper link]
[21] Xianghong Hu*, Jia Zhao*, Zhixiang Lin, Yang Wang, Heng Peng, Hongyu Zhao†, Xiang Wan†, Can Yang†: 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 (in press). [paper link]
2021
[20] Jiaxuan Wangwu, Zexuan Sun, Zhixiang Lin†: scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation. Bioinformatics (in press) [paper link]
[19] Shengquan Chen*, Guan'ao Yan*, Wenyu Zhang, Jinzhao Li, Rui Jiang†, Zhixiang Lin†: RA3 is a reference-guided approach for epigenetic characterization of single cells. Nature Communications 2021, 12:2177. [paper link]
[18] Pengcheng Zeng, Zhixiang Lin†: 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]
2020
[17] Zeng P, Lin Z†: Elastic Coupled Co-clustering for Single-Cell Genomics Data. Preprint [paper link]
[16] 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]
[15] 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]
[14] 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]
2019
[13] 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]
2018
[12] 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.
[11] 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.
[10] 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]
2017
[9] 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]
[8] Wu M, Lin Z, S Ma, T Chen, R Jiang, WH Wong: Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks. Journal of Molecular Cell Biology. 2017, 9(6): 436-452. [paper link]
[7] 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]
2016
[6] 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]
2015
[5] Lin Z, Li M, Sestan N, and Zhao H: A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data. Statistical applications in genetics and molecular biology. 2015, 15 (2): 139-150. [paper link] [software link]
[4] 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]
2014 and before
[3] Gallagher JEG and Zheng W, Rong X, Miranda N, Lin Z, Dunn B, Zhao H and Snyder MP: Divergence in a master variator generates distinct phenotypes and transcriptional responses. Genes & Development 2014, 28 (4): 409-421.
[2] 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.
[1] Shen J, Zhou Y, Lu T, Peng J, Lin Z, Huang L, Pang Y, Yu L and Huang Y: An integrated chip for immunofluorescence and its application to analyze Lysosomal Storage Disorders. Lab Chip 2012, 12 (2): 317-324.
* first authors with equal contribution
† corresponding authors with equal contribution
2024
[34] Kai Zhao, Hon-Cheong So†, Zhixiang Lin†: scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis. Genome Biology 2024, 25: 223. [paper link]
[33] Kai Zhao, Sen Huang, Cuichan Lin, Pak Chung Sham, Hon-Cheong So†, Zhixiang Lin†: INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis. PLoS Genetics 2024, 20(3): e1011189. [paper link]
[32] Jinzhao Li, Jiong Wang, and Zhixiang Lin†: SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics. Briefings in Bioinformatics 2024, 25(1), bbad490. [paper link]
2023
[31] Chen Li, Ting-Fung Chan, Can Yang† and Zhixiang Lin†: stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics. Bioinformatics 2023, 39(10), btad642. [paper link]
[30] Xiaomeng Wan*, Jiashun Xiao*, Sindy Sing Ting Tam, Mingxuan Cai, Ryohichi Sugimura, Yang Wang, Xiang Wan, Zhixiang Lin†, Angela Ruohao Wu† and Can Yang†: Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nature Communications 2023, 14: 7848. [paper link]
[29] Pengcheng Zeng† and Zhixiang Lin: scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2023 (in press).
[28] Wenyu Zhang and Zhixiang Lin†: iPoLNG - An unsupervised model for the integrative analysis of single-cell multiomics data. Frontiers in Genetics. 2023; 14: 998504. [paper link]
2022
[27] Zheng Zhang, Shengquan Chen, Zhixiang Lin†: RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. Briefings in Bioinformatics 2022, bbac540. [paper link]
[26] Pengcheng Zeng, Yuanyuan Ma, Zhixiang Lin†: 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]
[25] Zhixiang Lin: Integrative analyses of single-cell multi-omics data: a review from a statistical perspective. Handbook of Statistical Bioinformatics (2nd Edition) 2022 (in press).
[24] Jingsi Ming*, Zhixiang Lin*, Xiang Wan, Can Yang†, Angela Ruohao Wu†: FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms. Briefings in Bioinformatics 2022, bbac167 [paper link].
[23] Jia Zhao*, Gefei Wang*, Jingsi Ming, Zhixiang Lin, Yang Wang, Tabula Microcebus Consortium, Angela Ruohao Wu†, Can Yang†. Adversarial domain translation networks enable fast and accurate large-scale atlas-level single-cell data integration. Nature Computational Science 2022, 2:317-330. [paper link]
[22] Yuanyuan Ma, Zexuan Sun, Pengcheng Zeng, Wenyu Zhang, Zhixiang Lin†: JSNMF enables effective and accurate integrative analysis of single-cell multiomics data. Briefings in Bioinformatics 2022, bbac105. [paper link]
[21] Xianghong Hu*, Jia Zhao*, Zhixiang Lin, Yang Wang, Heng Peng, Hongyu Zhao†, Xiang Wan†, Can Yang†: 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 (in press). [paper link]
2021
[20] Jiaxuan Wangwu, Zexuan Sun, Zhixiang Lin†: scAMACE: Model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation. Bioinformatics (in press) [paper link]
[19] Shengquan Chen*, Guan'ao Yan*, Wenyu Zhang, Jinzhao Li, Rui Jiang†, Zhixiang Lin†: RA3 is a reference-guided approach for epigenetic characterization of single cells. Nature Communications 2021, 12:2177. [paper link]
[18] Pengcheng Zeng, Zhixiang Lin†: 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]
2020
[17] Zeng P, Lin Z†: Elastic Coupled Co-clustering for Single-Cell Genomics Data. Preprint [paper link]
[16] 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]
[15] 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]
[14] 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]
2019
[13] 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]
2018
[12] 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.
[11] 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.
[10] 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]
2017
[9] 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]
[8] Wu M, Lin Z, S Ma, T Chen, R Jiang, WH Wong: Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks. Journal of Molecular Cell Biology. 2017, 9(6): 436-452. [paper link]
[7] 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]
2016
[6] 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]
2015
[5] Lin Z, Li M, Sestan N, and Zhao H: A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data. Statistical applications in genetics and molecular biology. 2015, 15 (2): 139-150. [paper link] [software link]
[4] 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]
2014 and before
[3] Gallagher JEG and Zheng W, Rong X, Miranda N, Lin Z, Dunn B, Zhao H and Snyder MP: Divergence in a master variator generates distinct phenotypes and transcriptional responses. Genes & Development 2014, 28 (4): 409-421.
[2] 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.
[1] Shen J, Zhou Y, Lu T, Peng J, Lin Z, Huang L, Pang Y, Yu L and Huang Y: An integrated chip for immunofluorescence and its application to analyze Lysosomal Storage Disorders. Lab Chip 2012, 12 (2): 317-324.