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.
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. .
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. .
Kim S, Forno E, Yan Q, Jiang Y, Zhang R, Boutaoui N, Acosta-Pérez E, Canino G, Chen W, Celedón JC. SNPs identified by GWAS affect asthma risk through DNA methylation and expression of cis-genes in airway epithelium. .
Yan Q, Weeks DE, Xin H, Swaroop A, Chew EY, Huang H, Ding Y, Chen W. Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression. .
Sun Z, Chen L, Xin H, Jiang Y, Huang Q, Cillo AR, Tabib T, Kolls JK, Bruno TC, Lafyatis R, Vignali DAA, Chen K, Ding Y, Hu M, Chen W. A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies. .