The Jiang Lab’s primary research interests lie in statistical modeling and method development in genetics and genomics, with application to data from large-scale cohort studies of human health and disease. We collaborate with biologists and clinicians to address statistical and computational challenges presented by new cutting-edge technologies and provide data-driven statistical methods to biomedical researchers for better data analysis and experimental design. Our particular focus is on detecting structural variants, assessing intratumor heterogeneity, interrogating genome-wide DNA damage and repair, and deciphering cellular heterogeneity by single-cell omics approaches. Our research is currently supported by R35 GM138342 from the National Institute of General Medical Sciences.

1. Mann B, Zhang X, Bell N, Adefolaju A, Thang M, Dasari R, Valdivia A, Quinsey C, Rauf Y, Cassidy N, Yang Y, Viziri C, Rego S, Jiang Y, Dunn D, Floyd S, Baldwin A, Hingtgen S, Satterlee A. A normalized ex vivo platform for functional precision diagnosis of patient brain tumor tissue. Cell Reports Medicine, 4 (6), 101042, 2023. [Impact factor: 16.99]
2. Guan PY, Lee JS, Wang L, Lin KZ, Wen M, Chen L, Jiang Y†. Destin2: integrative and cross-modality analysis of single-cell chromatin accessibility data. Frontiers in Genetics, 14:1089936, 2023. [Impact factor: 4.77; corresponding author]
3. Agarwal A, Zhao F, Jiang Y, Chen L. TIVAN-indel: A computational framework for annotating and predicting noncoding regulatory small insertion and deletion. Bioinformatics, btad060, 2023. [Impact factor 6.94]
4. Jiang Y†, Harigaya Y, Zhang Z, Zhang H, Zang C, Zhang NR†. Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions. Cell Systems, 13 (9), 737-751, 2022. [Impact factor 11.09; first and co-corresponding author]
5. Wang R, Lin D†, Jiang Y†. EPIC: inferring relevant cell types for complex traits by integrating genome-wide association studies and single-cell RNA sequencing. PLOS Genetics, 18 (6), e1010251, 2022. [Impact factor 6.02; co-corresponding author]
6. Dong M, He Y, Jiang Y, Zou F. Joint gene network construction by single-cell RNA sequencing data. Biometrics, biom.13645, 2022. [Impact factor 1.70] 8.
7. Dong M, Thennavan A, Urrutia E, Li Y, Perou CM, Zou F†, Jiang Y†. SCDC: bulk gene expression deconvolution by single-cell RNA sequencing. Briefings in Bioinformatics, 22 (1), 416-427, 2021. [Impact factor 11.62; co-corresponding author]
8. Mei W, Jiang Z, Chen L, Chen Y, Sancar A†, Jiang Y†. Genome-wide circadian rhythm detection methods: systematic evaluations and practical guidelines. Briefings in Bioinformatics, 22 (3), bbaa135, 2021. [Impact factor 11.62; co-corresponding author]
9. Liu Z, Ruter DL, Quigley K, Jiang Y, Bautch VL. Single-cell RNA sequencing reveals endothelial cell gene expression heterogeneity under homeostatic shear stress. Arteriosclerosis, Thrombosis, and Vascular Biology, 41 (10), 2575-2584, 2021. [Impact factor 10.51]
10. Wang Y, Jiang Y, Yao B, Huang K, Liu Y, Wang Y, Qin X, Chen L. WEVar: a novel supervised learning framework for predicting functional noncoding variants. Briefings in Bioinformatics, bbab189, 2021. [Impact factor 11.62]