Welcome to Ma Lab@UMD

The advancement of modern technologies has generated a massive amount of omics data at different levels, from genomic and transcriptomic data at molecular level (usually from web-lab experiments), to radiomic (e.g. neuroimaging) data at organ level and clinical phenomic (e.g. EHR) data at system level (usually from observational studies). 

Unlike traditional dataset, omics data is featured by their high-dimensionality and complexity (e.g. complex hierarchy, complex interaction, and usually platform-specific). Our lab is interested in analyzing these omics data and a number of "-omics" data integration problems to further our understanding of health and disease. We develop timely, practical and rigorous statistical and machine learning methods and softwares to advance scientific discoveries which will ultimately promote health, prevent and treat diseases that cause illness and death. 

Our lab has close collaboration with researchers across multiple disciplines (psychiatry, infectious disease and cancer) from the University of Maryland (SPH, PSYC, NFSC at UMD; MPRC, UMGCCC at UMB) and other institutions. We are actively seeking for new collaborations with researchers both in and out of UMD. 

Please also visit our research group I co-directed with my collaborators at UMB: www.umdbright.com

Selected papers to highlight

Notes: ^: co-first author; *: corresponding author; students underlined; 

 

Zong W, Rahman T, Zhu L, Zeng X, Zhang Y, Zou J, Liu S, Ren Z, Litman D, Li JJ, Osterreich S, Ma T* and Tseng GC*. (2023). Transcriptomic congruence analysis for evaluating model organisms. Proceedings of the National Academy of Sciences, 120(6). https://doi.org/10.1073/pnas.2202584120. [Story on Maryland Today] [News on Genetic Engineering and Biotechnology (GEN)]


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, 12. https://doi.org/10.3389/fgene.2021.651546


Ma T^, Huo Z^, Kuo A^, ..., Song C and Tseng GC. (2019). MetaOmics - Comprehensive Analysis Pipeline and Web-based Software Suite for Transcriptomic Meta-Analysis. Bioinformatics, 35(9): 1597-1599. 10.1093/bioinformatics/bty825


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.  https://doi.org/10.1111/rssc.12199 (won 2017 ASA Section on Bayesian Statistical Science (SBSS) student paper award).


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. DOI: 10.1089/cmb.2017.0056 (selected to present at Dahshu Data Science Symposium: Computational Precision Health 2017 and won the best paper award).


Feng L^, Ye Z^, Mo C, Wang J, …, Chen S* and Ma T*. (2023). Elevated blood pressure accelerates white matter brain aging among late middle-aged women: a Mendelian Randomization study in the UK Biobank. Journal of Hypertension, 10-1097. PMID: 37682053.


Ye Z, Mo C, Liu S, Gao S, Feng L, Zhao B, Canida T, Wu Y, ..., Chen S* and Ma T*. (2023). Deciphering the causal relationship between blood pressure and white matter integrity: a Mendelian Randomization study in the UK Biobank. Journal of Neuroscience Research. 101(9), 1471-1483. doi.org/10.1002/jnr.25205


Mo C, Wang J, Ye Z, Ke H, ..., Kochunov P, Hong E, Ma T* and Chen S*. (2023). Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank. Addiction, 118(4): 739-749. 10.1111/add.16088


Mo C^, Ye Z^, Ke H^, Lu T, Canida T, ..., Hong E, Kochunov P, Ma T* and Chen S*. (2021). A new Mendelian Randomization method to estimate causal effects of multivariate brain imaging exposures. Pacific Symposium on Biocomputing (PSB) 2022, pp. 73-84. 


Canida T, Ke H, Chen S, Ye Z and Ma T*. (2024). Multivariate Bayesian variable selection with application to multi-trait genetic fine mapping. Under revision in Journal of the Royal Statistical Society: Series C. https://arxiv.org/abs/2212.13294


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, 13(1): 37. 10.3390/genes13010037



Ke H and Ma T*. (2024). NGP: a tool to detect noncoding RNA-gene regulatory pairs from expression data. Cancer Bioinformatics. 


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 and noncoding RNA regulators of gene expression. Bioinformatics, 38(17): 4078-4087. 10.1093/bioinformatics/btac518


    Ma T^, Yang^ F, Ke H and Ren Z. (2024+). Robust Distance Correlation for Variable Screening. Under revision in Biometrics. https://arxiv.org/abs/2212.13292 


Ma T, Ren Z and Tseng GC. (2020). Variable screening with multiple studies. Statistica Sinica, 30(2): 925-953. https://www.jstor.org/stable/26968963


News

FUNDING