Tóm tắt

The cell is the fundamental unit of life. The biological functions of an organ or tissue are results of coordinated action of a large number of cells, each having its own properties and dynamic behavior. While it is traditional to classify cells with similar function and morphol ogy as cell types, it is also well-recognized that, even within each cell type, there remain significant differences, or states, among individual cells. Current knowledge of the reper toire of cell types and cell states, as well as their dynamic changes, remains highly incomplete. Systematic, comprehensive characterization of spatial and temporal organization of cellular heterogeneity, along with the mechanisms underlying cell-type/state transition and main tenance, has important implications in development and diseases. It is not until recently that it has become feasible to systematically investigate cellular heterogeneity at the single-cell resolution, thanks to the rapid development of a number of advanced technologies including sequencing, imaging, and microfluidic devices. Collec tively, single-cell technologies have created exciting opportunities to systematically charac terize the molecular behavior of individual cells at the omics scale. At the same time, the analysis and integration of single-cell omic data are difficult due to a number of challenges such as sparsity, technical variability, and spatial-temporal complexity. During the past few years, numerous computational methods and software packages have been developed to overcome these challenges. The aim of this book is to introduce to the community the state of the art of computational approaches in single-cell data analysis. Each chapter presents a computational toolbox that is aimed to overcome a specific chal lenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis. Rather than explaining the mathematical details, here the focus is on hands-on implementation of computational methods for analyzing experimental data. Taken together, these chapters cover a wide range of tasks and may serve as a handbook for single-cell data analysis. Finally, I would like to thank Prof. John M. Walker for his kind invitation and sustained support throughout the preparation of this book. I would also like to express my sincere gratitude to all the contributors for sharing their protocols.