Xinjun Wang

This paper introduces BayesMAGPIE, a Bayesian method for identifying driver mutations in oncogenic pathways using mutual exclusivity, showing improved accuracy over MAGPIE in simulation and TCGA data.

SECANT improves clustering and annotation of single-cell multi-omics data by integrating surface protein and RNA information with biology-guided semi-supervised learning.

This study integrates spatial transcriptome, single-cell RNA-seq, and single-cell ATAC-seq data from mouse lungs after immunization to identify cell types and their spatial dynamics.

This paper introduces GMM-Demux, a method to identify and remove multiplets in scRNA-seq data, showing improved accuracy and error reduction.

BREM-SC is a Bayesian model that jointly clusters single-cell transcriptomic and proteomic data, improving accuracy and quantifying clustering uncertainty.