SINGLE CELL ANALYSIS, WHAT IS IN THE FUTURE?

Single-cell genomics technology is an exciting emerging area that holds the promise to revolutionize our understanding of diseases and associated biological processes. It allows us to explore processes active in bulk tissue samples, survey tissue complexity, characterize heterogeneous cell populations and explore the role of cellular heterogeneity and interactions in disease. To deal with these new experimental data, new computational methods, software, and data portals to analyze, integrate and interpret the complexity of the system are clearly needed. The many areas where new analytical methods are needed include: (1) computational methods to identify bona fide patterns of gene expression, mutations, or DNA methylation among single cells; (2) imaging of gene expression or in situ transcriptomic analysis to allow study of the spatial-temporal relationships of single cells in complex tissues; (3) new tools and methods to integrate multi-omics single cell data that can handle the sparsity associated with those data, and (4) new software packages and data portals to enable cloud/HPC deployment to both developers and non-informatics end-users. Here we briefly review the state-of-the-art single cell analysis methods, ranging from clustering to visualization, and discuss the future directions of single cell bioinformatics that overcomes the computational and technical challenges as well as promotes the wide-spread adoption in biomedical research labs.