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

Yingkun Zhang^1,2, Xinrui Lin¹, Zhixian Yao¹

¹Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.

Computational and Structural Biotechnology Journal

|December 22, 2022

Summary

Spatial transcriptomics provides gene expression data with location information but lacks single-cell resolution. This review details computational deconvolution tools to determine spot cellular composition, aiding downstream analysis.

Keywords:

Deconvolution Machine learning Regression Spatial transcriptome Statistic model

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Area of Science:

Genomics and Molecular Biology
Bioinformatics and Computational Biology

Background:

Spatial transcriptomics enables gene expression profiling with spatial context.
Current high-throughput methods (spot-based) lack single-cell resolution, with spots containing multiple cell types.
This limitation hinders detailed understanding of complex biological processes.

Purpose of the Study:

To review and categorize available computational deconvolution tools for spatial transcriptomics.
To guide researchers in selecting appropriate deconvolution tools based on their strategies, advantages, and limitations.
To enhance downstream data mining by improving single-cell resolution and spot composition analysis.

Main Methods:

Literature review of existing spatial transcriptome deconvolution tools.
Categorization of tools based on their underlying computational strategies.
Detailed explanation of the advantages and limitations of each tool.

Main Results:

Identification and classification of various deconvolution tools for spatial transcriptomics.
Analysis of the strengths and weaknesses of different deconvolution approaches.
Highlighting the impact of tool selection on downstream data analysis and interpretation.

Conclusions:

Deconvolution tools are essential for overcoming the resolution limitations of current spatial transcriptomics technologies.
Choosing the right deconvolution tool is critical for accurate downstream analysis, including single-cell expression profiling and cell composition estimation.
This review provides a comprehensive guide to aid researchers in selecting the most suitable deconvolution tool for their specific spatial transcriptomics studies.