Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100084, China.
Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100084, China.
Genome Biol. 2022 Mar 25;23(1):83. doi: 10.1186/s13059-022-02653-7.
The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches. Furthermore, with the continuous evolution of sequencing protocols, the underlying assumptions of current analytical methods need to be re-evaluated and adjusted to harness the increasing data complexity. To motivate and aid future model development, we herein review the recent development of statistical and machine learning methods in spatial transcriptomics, summarize useful resources, and highlight the challenges and opportunities ahead.
近年来，空间转录组学技术取得了重大进展，实现了细胞转录组和空间位置的多重分析。随着实验技术的容量和效率不断提高，对分析方法的开发提出了新的需求。此外，随着测序技术的不断发展，需要重新评估和调整当前分析方法的基本假设，以充分利用日益复杂的数据。为了激发和帮助未来的模型开发，我们在此综述了空间转录组学中统计和机器学习方法的最新进展，总结了有用的资源，并强调了未来的挑战和机遇。
