A Unified Probabilistic Framework for Modeling and Inferring Spatial Transcriptomic Data


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Abstract

Spatial transcriptomics (ST) can provide vital insights into tissue function with the spatial organization of cell types. However, most technologies have limited spatial resolution, i.e., each measured location contains a mixture of cells, which only quantify the average expression level across many cells in the location. Recently developed algorithms show the promise to overcome these challenges by integrating single-cell and spatial data. In this review, we summarize spatial transcriptomic technologies and efforts at cell-type deconvolution. Importantly, we propose a unified probabilistic framework, integrating the details of the ST data generation process and the gene expression process simultaneously for modeling and inferring spatial transcriptomic data.

About the authors

Zhiwei Huang

Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University

Email: info@benthamscience.net

Songhao Luo

Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University

Email: info@benthamscience.net

Zhenquan Zhang

Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University

Email: info@benthamscience.net

Zihao Wang

Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University

Email: info@benthamscience.net

Tianshou Zhou

Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University

Email: info@benthamscience.net

Jiajun Zhang

Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University

Author for correspondence.
Email: info@benthamscience.net

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