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

Elsevier

Available online 5 December 2022

Computational and Structural Biotechnology JournalAuthor links open overlay panelAbstract

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

Spatial transcriptome

Statistic model

Regression

Machine learning

© 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

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