Emerging artificial intelligence applications in Spatial Transcriptomics analysis

Review

. 2022 Jun 2;20:2895-2908.

doi: 10.1016/j.csbj.2022.05.056.

eCollection 2022.

Affiliations

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Review

Yijun Li et al.

Comput Struct Biotechnol J.

2022.

. 2022 Jun 2;20:2895-2908.

doi: 10.1016/j.csbj.2022.05.056.

eCollection 2022.

Affiliations

¹ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
² Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

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Abstract

Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artificial intelligence (AI) methods have been developed to utilize various machine learning and deep learning techniques for computational ST analysis. This review provides a comprehensive and up-to-date survey of current AI methods for ST analysis.

Keywords:

Artificial intelligence; Deep learning; Machine learning; Spatial transcriptomics.

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1

Overview of AI methodologies and application areas in ST data analysis. (a) Timeline of emerging AI methods in ST analysis, (b) characteristics of ST data, the potential reference datasets such as associated histology image and scRNA-Seq data, and the application areas in computational ST analysis: SVG detection, clustering, communication analysis, deconvolution, and enhancement.

**Fig. 2**

General schematic of (a) the fully connected neural network, (b) the convolutional neural network, (c) the graph convolutional neural network, and (d) the autoencoder.

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