Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome

Yingkun Zhang; Xinrui Lin; Zhixian Yao; Di Sun; Xin Lin; Xiaoyu Wang; Chaoyong Yang; Jia Song

doi:10.1016/j.csbj.2022.12.001

Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome

Comput Struct Biotechnol J. 2022 Dec 5:21:176-184. doi: 10.1016/j.csbj.2022.12.001. eCollection 2023.

Authors

Yingkun Zhang^{1

2}, Xinrui Lin¹, Zhixian Yao¹, Di Sun¹, Xin Lin^{1

3}, Xiaoyu Wang², Chaoyong Yang^{1

2}, Jia Song¹

Affiliations

¹ Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
² State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
³ Chemistry and Materials Science College, Shanghai Normal University, Shanghai 200234, China.

Abstract

The spatial transcriptome has enabled researchers to resolve transcriptome expression profiles while preserving information about cell location to better understand the complex biological processes that occur in organisms. Due to technical limitations, the current high-throughput spatial transcriptome sequencing methods (known as next-generation sequencing with spatial barcoding methods or spot-based methods) cannot achieve single-cell resolution. A single measurement site, called a spot, in these technologies frequently contains multiple cells of various types. Computational tools for determining the cellular composition of a spot have emerged as a way to break through these limitations. These tools are known as deconvolution tools. Recently, a couple of deconvolution tools based on different strategies have been developed and have shown promise in different aspects. The resulting single-cell resolution expression profiles and/or single-cell composition of spots will significantly affect downstream data mining; thus, it is crucial to choose a suitable deconvolution tool. In this review, we present a list of currently available tools for spatial transcriptome deconvolution, categorize them based on the strategies they employ, and explain their advantages and limitations in detail in order to guide the selection of these tools in future studies.

Keywords: Deconvolution; Machine learning; Regression; Spatial transcriptome; Statistic model.

Publication types

Review