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Sep 13, 2024;22 (3):

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

Xiuying Liu, Xianwen Ren
Author Information
  1. Xiuying Liu: Changping Laboratory, Beijing 102206, China. ORCID
  2. Xianwen Ren: Changping Laboratory, Beijing 102206, China. ORCID
PMID: 39110523 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.

Keywords

Cell type decomposition Cellular composition Single-cell sequencing Spatial transcriptomics Spot deconvolution

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MeSH Term

Humans
Algorithms
Transcriptome
Gene Expression Profiling
Computational Biology
Animals
Journal Article Review
Article has an altmetric score of 3

Word Cloud

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