A team led by Prof. ZHANG Shihua from the Academy of Mathematics and Systems Science of CAS has proposed a new computational tool, STAGATE, to decipher tissue substructures from spatial resolved transcriptomics (STs). This model was developed by applying artificial intelligence to integrate the spatial location information and gene expression profiles of spatial spots. It introduces a graph attention auto-encoder with a graph attention mechanism in the middle hidden layer, which can adaptively learn heterogeneous similarities between neighboring spots. The study was published in Nature Communications.
Deciphering tissue substructures or spatial domains (i.e., tissue regions with similar spatial expression patterns) is one of the great challenges from STs. For example, the laminar organization of the human cerebral cortex is especially related to its biological functions, in which cells residing within different cortical layers often differ in expressions, morphology and physiology.
However, most existing clustering methods do not efficiently use the available spatial information, which results in very discrete tissue substructures. Also they are highly susceptible to technical noise.
The new model first converts the spatial location information into a spatial neighbor network between spatial spots, and then feeds the gene expression information and the spatial network into a graph attention autoencoder to learn a low-dimensional representation of spot. It also combines the characteristics of 10x Visium data, and suggests a cell type-aware module based on expression information pre-clustering to better describe the boundary of the cell space domain.
Intriguingly, the new model can reduce the batch effect between different sections by introducing a spatial network between adjacent sections, and improve the performance of three-dimensional tissue sub-structures.
The superiority of STAGATE for deciphering tissue substructures or spatial domains has been validated in diverse datasets. It is worth noting that it can be used to analyze spatial transcriptomics data of different sequencing technology platforms (including 10x Visium, Slide-seq, Stereo-seq, etc.) with diverse space resolutions.
"With the rapid development of spatial omics technology and the continuous accumulation of data, this new model STAGATE can facilitate the precise analysis of large-scale spatial transcriptome data and advance our understanding of the tissue substructures." says ZHANG Shihua, Ph.D., an expert of machine learning and computational biology, and lead author of the study.
JOURNAL
Nature Communications
DOI
10.1038/s41467-022-29439-6
ARTICLE TITLE
Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder
ARTICLE PUBLICATION DATE
1-April-2022
