Welcome to Chen Lab

Wei Chen

Professor of Pediatrics

University of Pittsburgh

Principal Investigator

My lab focuses on developing statistical and computational methods for the analysis of large-scale biomedical data and applying them to study complex diseases. Collaborating with our colleagues, we use cutting-edge technologies including DNA-seq, RNA-seq, methylation, ATAC-seq and single cell sequencing to study human diseases such as childhood asthma and age-related macular degeneration. Chen lab welcomes collaborators and trainees to move science forward. Our lab is generously supported by National Institute of Health, National Science Foundation, University of Pittsburgh and UPMC.

Recent News

8/2022 Chen Lab received NSF (2225775) with Dr. Heng Huang to develop statistical and machine learning methods for single-cell multi-omics data
7/2022 Chen Lab received NIH R01 (R01EB034116) with Dr. Heng Huang to develop advanced machine learning methods for ocular data
7/2022 Zhongli has two paper published in Genome Biology and iScience
5/2022 Chen Lab received P01 renewal of Bioinformatics Core to study Severe Asthma with Drs. Ray and Wenzel
3/2022 Wei Chen was appointed as Adjunct Professor of Biomedical Engineering (BME) at Carnegie Mellon University

more news

Recent Publications

Xu Z, Heidrich-O’Hare E, Chen W, Duerr R. Comprehensive benchmarking of CITE-seq versus DOGMA-seq single cell multimodal omics. Genome Biol 23, 135 (2022).
Yan Q, Forno E, C Celedón J, Chen W. A region-based method for causal mediation analysis of DNA methylation data. Epigenetics. 2022 Mar;17(3):286-296.
Yan Q, Ding Y, Weeks DE, Chen W. AMD Genetics: Methods and Analyses for Association, Progression, and Prediction. Adv Exp Med Biol. 2021;1256:191-200.
Xin H, Lian Q, Jiang Y, Luo J, Wang X, Erb C, Xu Z, Zhang X, Heidrich-O’Hare E, Yan Q, Duerr RH, Chen K, Chen W. GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing. Genome Biol. 2020 Jul 30;21(1):188.
Wang X, Sun Z, Zhang Y, Xu Z, Xin H, Huang H, Duerr RH, Chen K, Ding Y, Chen W. BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data. Nucleic Acids Res. 2020 Jun 19;48(11):5814-5824.

more publications