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

被引：0
|
作者：
Liu, Xiuying [1 ]
Ren, Xianwen [1 ]
机构：
[1] Changping Lab, Beijing 102206, Peoples R China
来源：
GENOMICS PROTEOMICS & BIOINFORMATICS | 2024年 / 22卷 / 03期
关键词：
Spatial transcriptomics; Single-cell sequencing; Cellular composition; Spot deconvolution; Cell type decomposition; GENE-EXPRESSION;
D O I：
10.1093/gpbjnl/qzae057
中图分类号：
Q3 [遗传学];
学科分类号：
071007 ; 090102 ;
摘要：
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 mu m 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.
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页数：10
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