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Shi, X.*,#; Yang, Y.#; Ma X.S; Zhou Y.; Guo Z.; Wang C.; Liu J.* (2023) Probabilistic cell/domain-type assignment of spatial transcriptomics data with SpatialAnno, Nucleic Acids Research, gkad1023
Zhang, X.S; Liu, X.; Shi, X.* (2023) Model Selection for Varying Coefficient Nonparametric Transformation Model, Econometric Journal, 26(3), 492-512
Zhang, X.S; Shi, X.*; Liu, Y.; Liu, X.; Ma, S. (2023) A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data, Journal of Computational and Graphical Statistics, 32(3), 873-883
Liu W.; Liao X; Luo Z.; Yang Y.; Lau M.; Jiao Y.; Shi X.; Zhai W.; Ji H.; Yeong J.; Liu J. (2023) Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST, Nature Communications, 14(296), doi: 10.1038/s41467-023-35947-w
Liu, W.; Liao, X.; Yang, Y.; Lin, H.; Yeong , J.; Zhou, X.*; Shi,X. *; Liu J.* (2022) Joint dimension reduction and clustering analysis of single-cell RNA-seq and spatial transcriptomics data, Nucleic Acids Research, 50(12), gkac219
Yang, Y.#; Shi, X.#; Zhou, Q.; Sun, L.; Yeong, J.?, Liu, J.? (2022) SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes, Briefing in Bioinformatics, 23(1): bbab466
Cheng, Q.; Qiu, T.; Chai, X.; Sun, B.; Xia, Y.; Shi, X.*; Liu, J.* (2022) MR-Corr2: A two-sample Mendelian randomization method that accounts for correlated horizontal pleiotropy using correlated instrumental variants, Bioinformatics, 38(2): 303-310
Shi, X. and Chai, X. and Yang, Y. and Cheng, Q. and Jiao, Y. and Chen, H. and Huang, J. and Yang, C. and Liu, J. (2020) A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies. Nucleic Acids Research, 48(19): e109 [SCI, Impact Factor: 17]
Cheng Q., Yang Y., Shi X., Yeung K., Yang C., Peng H., Liu J. (2020). MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy. NAR Genomics and Bioinformatics, 2(2):lqaa028
Shi, X. and Ma, S. and Huang, Y. (2020). Promoting sign consistency in the Cox proportional hazards curemodel selection.Statistical Methods in Medical Research,29(1):15-28. [SCI]
Yang Y., Shi X., Jiao Y., Huang J., Chen M., Zhou X., Sun L., Lin X., Yang C. and Liu J. (2020) CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies. Bioinformatics, 36(7): 2009-16[SCI]
Liao X., Chai X., Shi X., Chen L., Liu J.(2020) The statistical practice of the GTEx Project: from single to multiple tissues. Quantitative Biology [SCI]
史兴杰，王赛旎，李扬. (2020). 高维数据的稳健二分类方法. 统计研究.37(9):95-105
张晶，方匡南*，张喆，史兴杰，郑陈璐. (2020). 基于稀疏结构连续比率模型的消费金融风控研究. 统计研究. 37(11):57-67
Shi, X. and Yang Y. and Jiao Y. and Cheng C. and Yang C. and Lin X. and Liu J. (2019). VIMCO: variational inference for multiple correlated outcomes in genome-wide association studies. Bioinformatics, 35(19), 3693-3700. [SCI]
S. Wang, X. Shi, M. Wu, and S. Ma (2019) Horizontal and vertical integrative analysis methods for mental disorders omics data. Scientific Report, 9(1):13430 [SCI]
孙怡凡，吴梦云，史兴杰. (2019).高维大数据基因网络中的社区发现——以NC方法为例. 统计研究, 36(3), 124-128.
Shi, X., Huang, Y. and Huang, J and Ma, S. (2018). A forward and backward stagewise algorithm for nonconvex loss functions and adaptive lasso. Computational Statistics and Data Analysis, 124, 235-251.
Chai, H*. and Shi, X.* and Zhang, Q and Zhao, Q and Huang, Y and Ma, S. (2017). Analysis of cancer gene expression data with an assisted robust marker identification approach. Genetic Epidemiology,41, 779– 789. [SCI]
Liu, J. and Yang, C. and Shi, X. and Li, C. and Huang, J. and Zhao, H. and Ma, S. (2016). Analyzing Association Mapping in Pedigree‐Based GWAS Using a Penalized Multitrait Mixed Model. Genetic Epidemiology, 40(5), 382-393. [SCI]
Jiang, Y*. andShi, X. * and Zhao, Q. and M. Krauthammer and BE Rothberg and Ma, S. (2016). Integrated analysis ofmultidimensional omics data on cutaneous melanoma prognosis. Genomics,107(6), 223-30.
Shi, X. and Zhao, Q. and Huang, J. and Xie, Y. and Ma, S. (2015). Deciphering the association between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach. Bioinformatics, 31(24), 3977-3983. [SCI]
Shi, X. * and Yi, H* and Ma, S. (2015). Measures for the degree of overlap of gene signatures and applications to TCGA. Briefings in Bioinformatics,16(5), 735-744. [SCI]
Wu, C. and Shi, X. and Cui, Y. and Ma, S. (2015). A penalized robust semiparametric approach for gene–environment interactions. Statistics in Medicine, 34(30), 4016-30. [SCI]
Zhao, Q.* and Shi, X.* and Xie, Y. and Huang, J. and Ben-Chang Shia and Ma, S. (2015). Combining Multidimensional Genomic Measurements for Predicting Cancer Prognosis: Observations from TCGA. Briefing in Bioinformatics, 16(2), 291-303. [SCI]
Zhao, Q. and Shi, X. and Huang, J. and Liu, J. and Li, Y. and Ma, S. (2015). Integrative analysis of -omics data using penalty functions. WIREs Computational Statistics,7(1), 99-108.
Shi, X. and Liu, J. and Huang, J. and Zhou, Y. and Ben-Chang Shia and Ma, S. (2014). Integrative Analysis of Cancer Prognosis Data with Contrasted Group Bridge Penalization. Genetic Epidemiology, 38(2), 141-151. [SCI]
Shi,X. and Liu, J. and Huang, J. and Zhou, Y. and Xie, Y. and Ma, S. (2014). A Penalized Robust Method for Identifying Gene-Environment Interactions. Genetic Epidemiology, 38(3), 220-230. [SCI]
Shi, X. and Shen, S. and Liu, J. and Huang, J. and Zhou, Y. and Ma, S.(2014). Similarity of Markers Identified from Cancer Gene Expression Studies: Observations from GEO. Briefing in Bioinformatics, 15(5), 671-684. [SCI]
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