Achievements

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic profiling technologies have emerged as popular ways to measure expression profiles of single cells with or without spatial location information. The measured data provide unprecedented level of information to understand disease associated changes in cell type compositions, cell-type specific expression, cell-cell communication networks, tissue microenvironments and so on. However, development of the corresponding computational methods to analyze such data is challenging due to the high noise level, prevalent batch effect, large data scale, prevalent non-linear relationship in the data. In this presentation, I will present various topics and novel computational methods developed by my team for the analyses of scRNA-seq and spatial transcriptomic, including identifying differential expressed genes from scRNA-seq data, cell-type deconvolution of spatial transcriptomic data, inferring cell-cell communication networks from spatial transcriptomic data, understanding changes in tissue microenvironment using spatial transcriptomic data and identifying patient heterogeneity using scRNA-seq data.