Presented by Associate Professor Suoqin Jin, School of Mathematics and Statistics (Wuhan University, China) Title: Dissecting Cellular Heterogeneity and Communication via Integration of Single-cell Genomics Data Abstract: Recent advances of single-cell technologies, in particular single-cell RNA and ATAC sequencing, provide an unprecedented opportunity to dissect cellular heterogeneity and communication more comprehensively. To deconvolute heterogeneous single cells from both transcriptomic and epigenomic profiles, we developed a matrix factorization-based method, scAI, for integrating single cell RNA-seq data and ATAC-seq or DNA methylation data obtained from the same individual cells. To address the extremely sparse and binary nature of the epigenomic data, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms. In addition, single cell RNA-seq data also offers a great opportunity for probing underlying intercellular communications that often drive heterogeneity and cell state transitions in tissues. We developed an integrated method CellChat for systematic inference and quantitative analysis of cell-cell communication by integrating scRNA-seq data and prior knowledge of the interactions between signaling molecules. I will show how we can quantitatively build and analyze cell-cell communication networks in an easily interpretable way by applying systems biology and machine learning approaches. Applying CellChat to real datasets shows its ability to extract complex signaling patterns. Our versatile and easy-to-use toolkit CellChat will help discover novel intercellular communications and build cell-cell communication atlases in diverse tissues.