Publications:

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

Selected Publications:

l 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.

l Srinivasan, A.^, Xue, L.*, and Zhan, X.* (2021). Compositional knockoff filter for high-dimensional regression analysis of microbiome data. Biometrics, 77(3), 984-995. [Selected as one of Biometrics 2021-2022 top cited papers]

l 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.

l 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. [Selected as an ENAR2016 Distinguished Student Paper Award, and an ASA JSM2017 David P. Byar Paper Award, and one of Biometrics 2017-2018 top download papers]

l 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

l 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.

l 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.

l 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.

l 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.

l 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. [Selected as an ASA JSM2021 David P. Byar Paper Award, and the IISA2021 Best Applied Statistics Student Paper Award]

Full List of Publications:

2023 and beyond:

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. 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.

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. Jiang, Z., Zhang, H., Aheran, T.U., Garcia-Closas, M., Chatterjee, N., Zhan, X.*, and Zhao, N.* (2023). The sequence kernel association test for multi-categorical outcomes with application to a breast cancer genome-wide association study. Genetic Epidemiology, 47(6), 432-449.

6. 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(1), 2197835.

[Journal 2022 IF=12.2, ranked 11/135 in Microbiology]

7. Hongjiao Liu, Wodan Ling, Xing Hua, Jee-Young Moon, Jessica Williams-Nguyen, Xiang Zhan, Ni Zhao, Angela Zhang, Ramon A. Durazo-Arvizu, Robert D. Knight, Qibin Qi, Robert Burke, Robert Kaplan and Michael C. Wu* (2023) Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic drivers of beta-diversity. Microbiome, 11:80.

[Journal 2022 IF=15.5, ranked 7/135 in Microbiology]

8. 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 2022 IF=9.2, ranked 4/70 in Dermatology]

9. 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 (7), 955-964.

2022:

10. 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.

[An earlier version won the first author a JSM 2021 Biometrics Section paper award]

[An earlier version won the first author a Best Student Paper Competition Award in IISA 2021 May Virtual Conference]

11. 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.

12. 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.

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

14. 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, 2183502.

15. 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.

2021 :

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

[Selected as a top cited article in Biometrics 2021-2022]

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

18. 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.

19. 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.

20. 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.

21. 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.

2020 :

22. 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.

23. 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.

24. 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.

25. 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.

26. 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.

27. 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.

28. 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.

2019:

29. 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]

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

31. 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.

32. 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.

33. 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.

34. 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.

2018:

35. 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.

36. 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.

37. 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.

38. 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.

2017 and before:

39. 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.

[Selected as a top downloaded article in Biometrics 2017-2018]

[An earlier version won an JSM 2017 Biometrics Section Young Investigator Paper Award]

40. 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.

41. 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.

[Highlight paper of the August 2017 issue of Genetics selected by The Genetics Society of America (GSA) ]

[An earlier version won an ENAR 2016 Distinguished Student Paper Award]

42. 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.

43. 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.

44. 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.

45. 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.

46. 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.

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

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