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计算策略和算法,用于推断空间转录组学数据的细胞组成。

Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data.

机构信息

Changping Laboratory, Beijing 102206, China.

出版信息

Genomics Proteomics Bioinformatics. 2024 Sep 13;22(3). doi: 10.1093/gpbjnl/qzae057.

Abstract

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses in estimating the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.

摘要

空间转录组学技术是描绘分子水平组织架构的重要且强大的方法。然而,由于当前空间技术的限制,不能直接测量细胞信息,而是对直径从 0.2 到 100μm 不等的空间斑点进行特征化。因此,应用计算策略来推断每个空间斑点内的细胞组成至关重要。本文综述的主要目的是总结目前在精确估计每个空间斑点的细胞比例方面的最新进展,并展望该领域的未来方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c200/11398939/242b108038b4/qzae057f1.jpg

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