Research Highlights

1. High-dimensional variable screening, selection and categorization with multiple studies

• Ye Z^ , Ke H^ , Chen S, Cruz-Cano R, He X, Zhang J, Dorgan J, Milton D and Ma T*. (2021). Biomarker categorization in transcriptomic meta-analysis by concordant patterns with application to Pan-cancer studies. Frontiers in Genetics , Accepted. [Link] [software]
• Saegusa T, Zhao Z , Ke H , Ye Z , Xu Z, Chen S and Ma T* . (2021). Detecting survival-associated biomarkers from heterogeneous populations. Scientific Reports , Accepted. [pdf] [software]
• Ma T , Ren Z and Tseng GC. (2020). Variable screening with multiple studies. Statistica Sinica , 30(2): 925–953.[pdf]

2. Big data (multi-omics data, neuroimaging and genetic data) integration

• Mo C^, Ye Z^ , Ke H^ , Lu T, Canida T, Liu S, ..., Kochunov P*, Ma T* and Chen S*. (2021). A new Mendelian Randomization method to estimate causal effects of multivariate brain imaging exposures. Under revision in Pacific Symposium on Biocomputing (PSB) 2022 .
• Ye Z^ , Mo C^, Liu S^, Hatch K, Gao S, Hong E, ..., Kochunov P*, Chen S* and Ma T*. (2021). White matter integrity and nicotine dependence: evaluating vertical and horizontal pleiotropy. Frontiers in Neuroscience , Accepted. [bioRxiv version]
• Zhu L, Huo Z, Ma T, Tseng GC. (2019). Bayesian indicator variable selection to incorporate hierarchical overlapping group structure in multi-omics applications. Annals of Applied Statistics , 13(4): 2611-2636. (a preliminary version won the ENAR distinguished student paper award).
• Huo Z, Zhu L, Ma T, Liu H, Han S, Liao D, Zhao J and Tseng GC. (2019). Two-way Horizontal and Vertical Omics Integration for Disease Subtype Discovery. Statistics in Biosciences , 1-22.
• Fang Z, Ma T, Tang G, Zhu L, Yan Q, Wang T, Celedón JC, Chen W and Tseng GC.(2018). Bayesian integrative model for multi-omics data with missingness. Bioinformatics , 34(22): 3801-3808. PMID: 30184058.

3. Post-GWAS statistical genetic methods (e.g. fine mapping, Polygenic risk score (PRS), Mendelian randomization (MR) based causal inference, Transcriptome wide association analysis (TWAS)).

• Ye Z^ , Mo C^, Liu S^, Hatch K, Gao S, Hong E, ..., Kochunov P*, Chen S* and Ma T*. (2021). White matter integrity and nicotine dependence: evaluating vertical and horizontal pleiotropy. Frontiers in Neuroscience , Accepted. [bioRxiv version]
• Mo C^, Ye Z^ , Hatch K, Zhang Y, Wu Q, Liu S and Kochunov P, Ma T* and Chen S*. (2021+). Genetic Fine-mapping with Dense Linkage Disequilibrium Blocks: genetics of nicotine dependence. bioRxiv. [Link]

4. Cross species resemblance analysis of genomic response.

• Zong W, Rahman T, Zhu L, Zeng X, Zhang Y, Zou J, Liu S, Li JJ, Ma T* and Tseng GC*. (2021+). Congruence evaluation for model organisms in transcriptomic response. Ready to submit.
• (Motivating PNAS paper #1) Seok et al. (2013). Genomic responses in mouse models poorly mimic human inflammatory diseases. Proceedings of the National Academy of Sciences , 110(9), 3507-3512.
• (Motivating PNAS paper #2) Takao, K., & Miyakawa, T. (2015). Genomic responses in mouse models greatly mimic human inflammatory diseases. Proceedings of the National Academy of Sciences , 112(4), 1167-1172.

5. Meta-analysis of multiple transcriptomic studies

• Ma T^, Huo Z^, Kuo A^, Zhu L, ..., Song C and Tseng GC. (2019). MetaOmics - Comprehensive Analysis Pipeline and Web-based Software Suite for Transcriptomic Meta-Analysis. Bioinformatics . PMID: 30304367. [pdf] [software]
• Ma T , Liang F. and Tseng GC. (2017). Biomarker detection and categorization in ribonucleic acid sequencing meta-analysis using Bayesian hierarchical model. Journal of the Royal Statistical Society: Series C. 66(4): 847-867. [pdf]
• Ma T , Liang F, Oesterreich S and Tseng GC. (2017). A joint Bayesian modeling for integrating microarray and RNA-seq transcriptomic data. Journal of Computational Biology. 24(7): 647-662. [pdf]