Mengsha Tong

The paper identifies prognostic-associated subpopulations and develops recurrence risk signatures from scRNA-seq data of colorectal cancer using a rank-based framework.

The paper integrates single-cell transcriptome data from liver samples to identify cell lineage similarities and prognostic subpopulations between cirrhosis and liver cancer.

The paper introduces eNet, a method to build enhancer networks from single-cell data, showing complex networks regulate cell identity and disease genes better than existing models.

The paper develops a tool called IDEP to perform personalized differential expression analysis on cancer proteomes, showing it can identify patient-specific deregulated proteins more effectively than group-based methods.