Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously characterize gene expression and spatial arrangement information within tissues, organs or organisms. To fully leverage spatial information, the focus extends beyond individual two-dimensional (2D) slices. Two tasks known as slices alignment and data integration have been introduced to establish correlations between multiple slices, enhancing the effectiveness of downstream tasks. Currently, numerous related methods have been developed. In this review, we first elucidate the details and principles behind several representative methods. Then we report the testing results of these methods on various SRT datasets, and assess their performance in representative downstream tasks. Insights into the strengths and weaknesses of each method and the reasons behind their performance are discussed. Finally, we provide an outlook on future developments. The codes and details of experiments are now publicly available at .

中文翻译：

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空间分辨转录组数据比对和整合的计算方法

大多数复杂的生物调节活动发生在三个维度（3D）中。为了更好地分析生物过程，不仅需要破译大量细胞的分子信息，而且还需要了解它们的空间背景如何影响它们的行为。随着空间解析转录组学 (SRT) 技术的发展，SRT 数据集正在生成，以同时表征组织、器官或生物体内的基因表达和空间排列信息。为了充分利用空间信息，重点不限于单个二维 (2D) 切片。引入了切片对齐和数据集成两项任务来建立多个切片之间的相关性，从而增强下游任务的有效性。目前，已经开发出多种相关方法。在这篇综述中，我们首先阐明了几种代表性方法背后的细节和原理。然后我们报告这些方法在各种 SRT 数据集上的测试结果，并评估它们在代表性下游任务中的性能。讨论了每种方法的优点和缺点以及其性能背后的原因。最后，我们对未来的发展进行了展望。实验的代码和详细信息现已在 上公开。