Group members are highlighted in boldface
* first authors with equal contribution
† corresponding authors with equal contribution
2021
[21] 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. Submitted. [paper link]
[20] Chen S*, Yan G*, Zhang W, Li J, Jiang R†, LinZ†: A reference-guided approach for epigenetic characterization of single cells. Nature Communications (Accepted). [paper link]
[19] Zeng P, Lin Z†: coupleCoC+: an information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data. Submitted. [paper link]
2020
[18] Ming J*, Lin Z*, Wan X, Yang C†, Wu A.R.†: FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms. Preprint [paper link].
[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. New Frontiers of Biostatistics and Bioinformatics (In press).
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
2021
[21] 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. Submitted. [paper link]
[20] Chen S*, Yan G*, Zhang W, Li J, Jiang R†, LinZ†: A reference-guided approach for epigenetic characterization of single cells. Nature Communications (Accepted). [paper link]
[19] Zeng P, Lin Z†: coupleCoC+: an information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data. Submitted. [paper link]
2020
[18] Ming J*, Lin Z*, Wan X, Yang C†, Wu A.R.†: FIRM: Fast Integration of single-cell RNA-sequencing data across Multiple platforms. Preprint [paper link].
[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. New Frontiers of Biostatistics and Bioinformatics (In press).
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.