A Matrix normal Graphical Model for inferring Gene Spatial co-expression

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Chichun Tan (1), Ying Ma (1)

(1) Brown University, Providence, RI

Ying Ma  
Brown University

Chichun Tan  
Brown University

Chichun Tan  
N/A

Recent advances in spatially resolved transcriptomics (SRT) have illuminated gene co-expression networks in spatial contexts, offering insights into disease mechanisms. However, current methods, mainly designed for single-cell studies, tend to overlook the intricate interactions between spatial location and gene expression networks. None of them are able to handle the increasingly prevalent large-scale datasets. To address these limitations, we propose a novel matrix normal based method, spMGM, for inferring gene co-expression networks in SRT studies. spMGM accounts for intricate interactions between spatial context and gene expression. Through extensive simulations, both model-based and non-model based, spMGM accurately recovers the underlying gene co-expression network, improving accuracy by 40% - 50% compared to existing methods. Moreover, spMGM can efficiently handle large-scale datasets like 10x Xenium, with 10 times faster than the most advanced method. Applying spGMM to breast cancer tissue demonstrates its ability to detect breast cancer-related hub genes that have not been identified by the other methods.

Gene Co-expression Network|Spatial Transcriptomics|Matrix Normal Model| | |

IMS

Statistical Methodology