The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of disease molecular mechanisms. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but linking cellular heterogeneity with disease phenotypes requires careful computational analysis.

One focus of my lab is to develop computational strategies for the analysis of single-cell data for various purposes, including batch effect removal, missing gene imputation, cell type identification, and cell differentiation trajectory analysis. We also explore how single-cell RNA sequencing data can be jointly analyzed with other modalities, including proteomics and metabolomics data, to better understand disease mechanisms from a holistic perspective.

Related publications: ItClust, Xu et al.

In collaboration with: Mingyao Li, Rui Xiao.