Publications:   (Link to Google Scholar Profile) 

∗Corresponding author; #Equal contribution; ^Group Member/Rotation Trainee.

•  Methodological:

1.  Rou, L.,  Zhan, X.*,Wang, T.* (2023). A Flexible Zero-inflated Poisson-Gamma Model with Application to Microbiome Sequence Count Data. Journal of the American Statistical Association, 118, 792-804.

2. Li, D., Srinivasan, A., Xue, L.*, and Zhan, X.* (2023). Robust Shape Matrix Estimation for High-Dimensional Compositional Data with Application to Microbial Inter-Taxa Analysis. Statistica Sinica, 33, 1577-1602.

3. Jiang, Z., Zhang, H., Aheran, T.U., Garcia-Closas, M., Chatterjee, N., Zhu, H., Zhan, X.*, and Zhao, N*. (2023). Sequence kernel association test with multi-categorical outcomes with application to breast cancer genome-wide association studies. Genetic Epidemiology, 47, 432-449.

4. Srinivasan, A^., Xue, L.*, and Zhan, X.* (2023). Identification of microbial features in multivariate regression under false discovery rate control. Computational Statistics & Data Analysis, 181, 107621.

5. Li, C., Li, R., Wen, J.*, Yang, S.*, Zhan, X.# (2023). Regularized Linear Programming Discriminant Rule with Folded Concave Penalty for Ultrahigh-dimensional Data. Journal of Computational and Graphical Statistics, 32, 1074-1082. 

6. Liu, H., Ling, W., Hua, X., Moon, J. Y., Williams-Nguyen, J. S., Zhan, X., ... & Wu, M. C. (2023)  and Michael C. Wu* (2023) Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity. Microbiome, 11, 80. 

7. Banerjee, K.^*, Chen, J., and Zhan,.X.* (2022). An adaptive test for microbiome association studies via feature selection. NAR Genomics and Bioinformatics, 4(1), lqab120.

8. Wang, T. Lin, W., Plangtinga, A. M., Wu, M. C. and Zhan, X.* (2022). Testing microbiome association using integrated quantile regression models. Bioinformatics, 38(2), 419-425.

9. Jiang, Z., He, M., Chen, J., Zhao, N.*, and Zhan, X.* (2022) MiRKAT-MC: a distance-based microbiome kernel association test with multi-categorical outcomes. Frontiers in Genetics, 13: 841764.

10. Cho, Y.*, Zhan, X.* and Ghosh, D. (2022). Nonlinear predictive directions in clinical trials. Computational Statistics & Data Analysis, 174, 107476. 

11. Srinivasan, A.^, Xue, L.*, and Zhan, X.* (2021). Compositional knockoff filter for high-dimensional regression analysis of microbiome data. Biometrics, 77(3), 984-995. 

12. Zhan, X.*  Banerjee, K.^ and Chen, J* (2021). Variant‐set association test for generalized linear mixed model. Genetic Epidemiology,  42(4), 402-412.

13. Jiang, L., Liu, X., He, X., Jin, Y., Cao, Y., Zhan, X., ... & Wu, R.* (2021). A behavioral model for mapping the genetic architecture of gut-microbiota networks. Gut Microbes, 13(1), e1820847.

14. Wilson, N., Zhao, N., Zhan, X., Koh, H., Fu, W., Chen, J., ... & Plantinga, A. M.* (2021). MiRKAT: Kernel machine Regression-Based global association tests for the microbiome. Bioinformatics, 37 (11) 1595-1597.

15. Yang, S., Wen, J., Eckert, S. T., Wang, Y., Liu, D. J., Wu, R., ... & Zhan, X.* (2020). Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning. Bioinformatics, 36(12), 3811-3817. [R Software] 

16. Banerjee, K.^, Zhao, N., Srinivasan, A., Xue, L., Hicks, S. D., Middleton, F. A., ... & Zhan, X.* (2019). An adaptive multivariate two-sample test with application to microbiome differential abundance analysis. Frontiers in Genetics, 10, 350. [R Software]

17. Zhan, X.* (2019). Relationship between MiRKAT and coefficient of determination in similarity matrix regression. Processes, 7, 79.

18. Wang, Q., Liu, X., Jiang, L., Cao, Y., Zhan, X., Griffin, C.H., and Wu, R.* (2019). Interrogation of Internal Workings in Microbial Community Assembly: Play a Game through a Behavioral Network? mSystems, 4(5), e00550-19.

19. Yang, S., Wen, J., Zhan, X., and Kifer, D. (2019). ET-Lasso: A New Efficient Tuning of Lasso-type Regularization for High-Dimensional Data. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 607-616). ACM.

20. Koh, H., Li, Y., Zhan, X., Chen, J., and Zhao, N.* (2019). An adaptive distance-based kernel association test based on the generalized linear mixed effect model for correlated microbiome studies. Frontiers in Genetics, 10: 458.

21. Zhan, X.* and Wu, M. C.* (2018). A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine. Biometrics, 74, 764–766.

22. Zhan, X., Xue, L., Zheng, H., Plantinga, A., Wu, M.C., Schaid, D.J., Zhao, N.* and Chen, J.* (2018). A small-sample kernel association test for correlated data with application to microbiome association studies. Genetic Epidemiology, 42, 772–782. [R Software] [Preprint].

23. Zhao, N.*, Zhan, X., Guthrie, K.A., Mitchell, C.M. and Larson, J. (2018). Generalized Hotellings test for paired compositional data with application to human microbiome studies. Genetic Epidemiology, 42, 459–469.

24. Zhao, N.*, Zhan, X., Huang, Y. T., Almli, L., Smith, A., Ressler, K., Binder, E., Epstein, M. P., Conneely, K. and Wu, M. C.*(2018).  Kernel machine methods for integrative analysis of genome-wide methylation and genotyping studies. Genetic Epidemiology, 42, 156-167.

25. Zhan, X.*, Plantinga, A., Zhao, N. and Wu, M. C.* (2017). A fast small-sample kernel independence test for microbiome community-level association analysis. Biometrics, 73, 1453–1463. 

26. Zhan, X.#, Tong, X.#,  Zhao, N., Maity, A., Wu, M. C.* and Chen, J.* (2017). A small-sample multivariate kernel machine test for microbiome association studies. Genetic Epidemiology, 41, 210-220. [R Software]

27.  Zhan, X., Zhao, N., Plantinga, A., Thornton, T., Conneely, K., Epstein, M. P. and Wu, M. C.* (2017). Powerful genetic association analysis for common or rare variants with high dimensional structured traits. Genetics, 206, 1779–1790. 

28. Plantinga, A., Zhan, X., Zhao, N., Chen, J., Jenq, R. R. and Wu, M. C.* (2017). MiRKAT-S: a community-level test of association between the microbiota and survival times. Microbiome, 5:17. [R Software]

29. Zhan, X.*, Girirajan, S., Zhao, N., Wu, M. C., and Ghosh, D.* (2016). A novel copy number variants kernel association test with application to autism spectrum disorders studies. Bioinformatics, 32, 3603–3610. [R Software]

30. Zhan, X.* and Ghosh, D. (2016). A novel power-based approach to Gaussian kernel selection in the kernel-based association test. Statistical Methodology, 33, 180-191.

31. Zhan, X., Patterson, A. D. and Ghosh, D.*(2015). Kernel approaches for differential expression analysis of mass spectrometry-based metabolomics data. BMC Bioinformatics, 16:77. [R Software]

32. Zhan, X.* and Ghosh, D. (2015). Incorporating auxiliary information for improved prediction using combination of kernel machines. Statistical Methodology, 22, 47-57.

33. Zhan, X.*, Epstein, M. and Ghosh, D. (2015). An adaptive genetic association test using double kernel machines. Statistics in Biosciences, 7, 262–281.

• Collaborative :

34. Li, Y#., Hu, Y#., Zhan, X.#, Song, Y., Xu, M., Wang, S., ... & Xu, Z. Z.* (2023). Meta-analysis reveals Helicobacter pylori mutual exclusivity and reproducible gastric microbiome alterations during gastric carcinoma progression. Gut Microbes, 15, 2197835.  

35. Zachary T. Nolan, Kalins Banerjee^, Zhaoyuan Cong, Samantha L. Gettle, Amy L. Longenecker, Yuka I. Kawasawa, Andrea L. Zaenglein, Diane M. Thiboutot, George W. Agak, Xiang Zhan, Amanda M. Nelson* (2023). Treatment response to isotretinoin correlates with specific shifts in Cutibacterium acnes strain composition within the follicular microbiome. Experimental Dermatology, 32, 955-964.

36. Schneider A, Nolan Z, Banerjee K^, Paine A, Cong Z, Gettle S, Longenecker A,, Zhan X, Agak G and Nelson A* (2023). Evolution of the skin microbiome during puberty in normal and acne skin. Journal of the European Academy of Dermatology and Venereology, 37(1), 166-175. 

[Journal 2021 IF=9.228, ranked 4/68 in Dermatology]

37.  Chen, H.#, Ji, T.#, Zhan, X., Liu, X., Yu, G., Wang, W., Jiang, Y.*, and Zhou X-H* (2022). Seizures Prediction and Epileptogenic Focus Localization via Dynamic Functional Brain Connectivity View from Scalp EEG.  Computational Intelligence and Neuroscience, 2022: 2183562.

38. Colello, J., Ptasinski, A., Zhan, X., Kaur, S., & Craig, T. J.* (2022). Assessment of Patient Perspectives and Barriers to Self-Infusion of Augmentation Therapy for Alpha-1 Antitrypsin Deficiency During the COVID-19 Pandemic.  Pulmonary Therapy, 8, 95-103.

39. Carney, M. C., Zhan, X., Rangnekar, A., Chroneos, M. Z., Craig, S. J., Makova, K. D., ... & Hicks, S. D.*  (2021). Associations between stool micro-transcriptome, gut microbiota, and infant growth. Journal of Developmental Origins of Health and Disease, 12(6), 876--882.

40. Bagley, J.J., Piazza, B.,  Lazarus, M. D.,  Fox E. J.* & Zhan, X. (2021). Resident Training and the Assessment of Orthopaedic Surgical Skills. JBJS Open Access,  6(4), e20.00173.

41. Ruzieh, M., Rogers, A. M., Banerjee, K.^, Soleymani, T., Zhan, X., Foy, A. J., & Peterson, B. R.* (2020). Safety of Bariatric Surgery in Patients with Coronary Artery Disease. Surgery for Obesity and Related Diseases,  16(12), 2031--2037.

42. Agarwal, A., Wen, T., Chen, A., Zhang, A. Y., Niu, X., Zhan, X., ... & Brantley, S. L. (2020). Assessing Contamination of Stream Networks near Shale Gas Development Using a New Geospatial Tool. Environmental Science & Technology, 54, 8632-8639. 

43. Schneider, A. M., Cook, L. C., Zhan, X., Banerjee, K.^, Cong, Z., Imamura-Kawasawa, Y., Gettle, S.L., Longenecker, A.L., Kirby, J.S., and Nelson, A. M.*  (2020). Response to Ring: In silico predictive metagenomic analyses highlight key metabolic pathways impacted in the HS skin microbiome. Journal of Investigative Dermatology, 140 (7), 1476-1479.

44. Gandhi C.*, Patel, J. and Zhan, X. (2020). Trend of influenza vaccine Facebook posts in last four years: a content analysis. American Journal of Infection Control, 48(4), 361-367. 

45. Schneider, A. M., Cook, L. C., Zhan, X., Banerjee, K.^, Cong, Z., Imamura-Kawasawa, Y., Gettle, S.L., Longenecker, A.L., Kirby, J.S., and Nelson, A. M.* (2020). Loss of skin microbial diversity and alteration of bacterial metabolic function in Hidradenitis Suppurativa. Journal of Investigative Dermatology, 140(3), 716-720.

46. Patel, V. A., Dunklebarger, M., Banerjee, K.^, Shokri, T., Zhan, X., and Isildak,H.*  (2020). Surgical Management of Vestibular Schwannoma: Practice Pattern Analysis via NSQIP. Annals of Otology, Rhinology & Laryngology, 129(3), 230-237.

47. Ozdemir, T., Bowers, D., Zhan, X., Ghosh, D., Brown J.L.* (2019). Identification of Key Signaling Pathways Orchestrating Substrate Topography Directed Osteogenic Differentiation Through High-Throughput siRNA Screening. Scientific Reports, 9, 1001.

48. Mitchell, C.*, Srinivasan, S., Zhan, X., Wu, M. C., Reed, S., Guthrie, K., LaCroix, A., Fiedler, T., Munch, M., Liu, C., Hoffman, N., Blair, I., Newton, K., Freeman, E., Joffe, H., Cohen, L., Fredricks. D. (2017). Vaginal microbiota and genitourinary symptoms of menopause: A cross sectional analysis. Menopause, 24, 1160-1166.

• Other works without Peer-Review:

49. Poulsen, A., Jang, D., Khan, M., Al-Mohtaseb, Z. N., Chen, M., Zhan, X., ... & Pantanelli, S. M. (2020). Repeatability of a Dual-Scheimpflug Placido Disc Corneal Tomographer/Topographer in Eyes with Keratoconus. medRxiv.  https://doi.org/10.1101/2020.05.13.20067710.