MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China.
Wiley Interdiscip Rev RNA. 2024 Mar-Apr;15(2):e1839. doi: 10.1002/wrna.1839.
Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.
空间转录组学在各个领域极大地改变了生物和医学研究。它能够以单细胞、多细胞或亚细胞分辨率进行转录组谱分析，同时保留复杂组织中细胞的几何定位信息。细胞空间信息及其分子特征的结合产生了一种新的多模态高通量数据源，这为数据分析挖掘方法的发展带来了新的挑战。空间转录组数据通常非常复杂、嘈杂且存在偏差，给数据分析和产生生物学见解带来了一系列尚未解决的困难。此外，为了跟上不断发展的空间转录组实验技术，现有的分析理论和工具需要相应地进行更新和改革。在这篇综述中，我们提供了对挖掘空间转录组学数据的当前计算方法的概述和讨论。提出了未来的方法学设计方向，以激发对新分析模型和算法的进一步讨论和进展。本文属于：RNA 方法 > 在细胞中进行 RNA 分析 RNA 进化和基因组学 > 对 RNA 的计算分析 RNA 输出和定位 > RNA 定位。
