Research Highlights

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

• Ke H , Ren Z, Qi J, Chen S, Tseng G, Ye Z and Ma T*. (2022). High-dimension to high-dimension screening for detecting genome-wide epigenetic regulators of gene expression. Under revision in Bioinformatics .
• 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

• Ke H , Ren Z, Qi J, Chen S, Tseng G, Ye Z and Ma T*. (2022). High-dimension to high-dimension screening for detecting genome-wide epigenetic regulators of gene expression. Under revision in Bioinformatics .
• Mo C^, Ye Z^ , Ke H^ , Lu T, Canida T, Liu S, ..., Kochunov P*, Ma T* and Chen S*. (2022). A new Mendelian Randomization method to estimate causal effects of multivariate brain imaging exposures. Accepted at 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.
• Liao S, Hartmaier RJ, McGuire KP, Puhalla SL, Luthra S, Chandran UR, Ma T, Bhargava R, Davidson NE, Benz S, Lee AV, Tseng GC and Oesterreich S. (2015). The molecular landscape of premenopausal breast cancer. Breast Cancer Research, 17:104. doi: 10.1186/s13058-015-0618-8. PMID: 26251034. (discussed in an interview; Nature, 527: S108-109)

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, Gao S, ..., Chen S* and Ma T*. (2022). Deciphering the causal relationship between blood pressure and white matter integrity: a Mendelian Randomization study in the UK Biobank. Under revision in Neurology
• Ye Z^ , Mo C^, Ke H^ , Yan Q, ..., Chen S* and Ma T*. (2022). Meta-analysis of transcriptome-wide association studies across 13 brain tissues identified novel clusters of genes associated with nicotine addiction. Genes , Accepted.
• 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*. (2022+). 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]