Heitz Matthieu, Ma Yujia, Kubal Sharvaj, Schiebinger Geoffrey
Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada; email:
Annu Rev Biomed Data Sci. 2024 Nov 14. doi: 10.1146/annurev-biodatasci-040324-030052.
Spatial transcriptomics (ST) brings new dimensions to the analysis of single-cell data. While some methods for data analysis can be ported over without major modifications, they are the exception rather than the rule. Trajectory inference (TI) methods in particular can suffer from significant challenges due to spatial batch effects in ST data. These can add independent sources of noise to each time point. Pioneering methods for TI on ST data have focused primarily on addressing the batch effects in physical arrangement, i.e., where tissues are deformed in different ways at different time points. However, other challenges arise due to the measurement granularity of ST technologies, as well as a bias from slicing. In this review, we examine the sources of these challenges, and we explore how they are addressed with current state-of-the-art STTI methods. We conclude by highlighting some opportunities for future method development.
空间转录组学（ST）为单细胞数据分析带来了新的维度。虽然一些数据分析方法可以在无需重大修改的情况下移植过来，但这只是例外而非普遍情况。特别是轨迹推断（TI）方法，由于ST数据中的空间批次效应，可能会面临重大挑战。这些效应会给每个时间点增加独立的噪声源。针对ST数据进行TI的开创性方法主要集中在解决物理排列中的批次效应，即组织在不同时间点以不同方式变形的情况。然而，由于ST技术的测量粒度以及切片偏差，还会出现其他挑战。在本综述中，我们研究了这些挑战的来源，并探讨了当前最先进的STTI方法是如何应对这些挑战的。我们通过强调未来方法开发的一些机会来结束本文。
