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 wet-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 targeting at these data to advance scientific discoveries which will ultimately promote health, prevent and treat diseases that cause illness and death. 

Our methodology and application works are staying up-to-date with the emerging new databases (e.g. biobank level data like UK Biobank and All of Us), new types of omics data (e.g. single-cell and spatial multi-omics data) and new techniques (e.g. various types of new AI techniques, both discriminative and generative). 

Our lab has close collaboration with researchers across multiple disciplines (aging, psychiatry, neurology 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 the 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; 

• Multi-omics data from various layers (e.g. transcriptomics (coding and noncoding RNA), epigenomics, proteomics, etc.):

Ke H, Ye Z, Feng L, Xu Z, Li E, Liang M, ... and Ma T*. (2025+). Inferring noncoding-RNA gene regulatory network from transcriptomic data and curated database. Under review. 

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)] [Shiny] [R package]

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. [R package].  

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 [package]

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).

• Genomics- > phenomics  (e.g. fine mapping, Transcriptome-Wide Association Studies (TWAS), Mendelian Randomization (MR)):

Wang N, Ye Z and Ma T*. (2024). TIPS: a novel pathway-guided joint model for transcriptome-wide association analysis. Briefings in Bioinformatics, 25 (6), bbae587. [package]

Canida T, Ke H, Chen S, Ye Z and Ma T*. (2024). Multivariate Bayesian variable selection for multi-trait genetic fine mapping. Journal of the Royal Statistical Society: Series C, qlae055. https://arxiv.org/abs/2212.13294 (a preliminary version has been selected as the ICSA student paper honorable mention). [package]

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

• Radiomics (e.g. neuroimaging data from MRI), imaging genetic and brain aging

Feng L, Ye Z, Pan Y, ..., Chen S* and Ma T*. (2025). Adherence to Life’s Essential 8 is associated with delayed white matter aging. eBioMedicine (Lancet journal), 115. [Story on Maryland Today] [Report on New Scientist]

Ke H^, Adhikari B^, Pan Y, ..., Ma T* and Kochunov P*. (2025). Predicting cerebral blood flow using voxel-wise resting-state functional MRI. Under review in Imaging Neuroscience. [package]  

Feng L, Ye Z, Du Z, Pan Y, Canada T, Ke H, ..., Shenassa E* and Ma T*. (2024). Association between allostatic load and accelerated white matter brain aging: findings from the UK Biobank. American Journal of Epidemiology, kwae396. 

Ye Z^, Pan Y^, Mccoy R, Bi C, Chen M, Feng L,...., Ma T* and Chen S*. (2024). Contrasting association pattern of plasma low-density lipoprotein with white matter integrity in APOE4 carriers versus non-carriers. Neurobiology of Aging, 143:41-52.

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. (a preliminary version has won the APHS/STATA SCHOLAR AWARD at APHA 2023)

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

• General statistical methods for high-dimensional variable selection and screening: 

    Ma T^, Yang F^, Ke H and Ren Z. (2025). Robust Distance Correlation for Variable Screening. Under revision in Stat. https://arxiv.org/abs/2212.13292 [package]

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, 11(1): 3203.

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

• April, 2025, Congratulations to Travis for successfully defending his dissertation. Great job, Traivs ! 

• July, 2024, we got our K01 award funded by NIDA to develop new TWAS methods to study the neurogenetic mechanism of nicotine and cannabis addiction.   

• June, 2024, Congratulations to Hongjie for successfully defending his dissertation. Great job, Hongjie ! 

• May, 2024, Congratulations to Travis for being selected as 2024 ICSA Student Paper Honorable Mentions. Well done, Travis !

• Feb, 2024, Congratulations to Cindy for successfully defending her dissertation. She will start her post-doc fellowship at NCI ITEB in July 2024. Great job, Cindy !  

• Nov, 2023, Congratulations to Cindy for winning the APHS/STATA SCHOLAR AWARD at APHA 2023.

• February 2023, we got the Grand Challenge Grant funded by the University of Maryland to study the genetic and lifestyle risk factors of accelerated brain aging (see news and brief summary).

• January 2023, Congratulations to Hongjie Ke for winning Outstanding Graduate Assistant Award for AY 22-23 by the University of Maryland Graduate School (see SPH news).

FUNDING