STGIC: A graph and image convolution-based method for spatial transcriptomic clustering

• Published in: PLoS computational biology Vol. 20; no. 2; p. e1011935
• Main Authors: Zhang, Chen, Gao, Junhui, Chen, Hong-Yu, Kong, Lingxin, Cao, Guangshuo, Guo, Xiangyu, Liu, Wei, Ren, Bin, Wei, Dong-Qing
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.02.2024; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Clustering; Convolution; Datasets; Deep learning; Efficiency; Feature extraction; Gene expression; Graphs; Labels; Medicine and Health Sciences; Neural networks; Physical Sciences; Prefrontal cortex; Research and Analysis Methods; Signal transduction; Spatial discrimination; Spatial resolution; Transcription; Transcriptomics
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011935

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC ( s patial t ranscriptomic clustering with g raph and i mage c onvolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback–Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it’s capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.