Multi-Slice Spatial Transcriptomics Data Integration Analysis with STG3Net
This work addresses batch correction challenges for researchers analyzing spatial transcriptomics data, though it appears incremental as it builds upon existing techniques like anchor pair selection and autoencoders.
The paper tackles the problem of batch effects in multi-slice spatial transcriptomics data by developing STG3Net, a method that integrates masked graph convolutional autoencoders with generative adversarial learning, achieving the best overall performance in accuracy, consistency, and F1LISI metrics compared to existing methods.
With the rapid development of the latest Spatially Resolved Transcriptomics (SRT) technology, which allows for the mapping of gene expression within tissue sections, the integrative analysis of multiple SRT data has become increasingly important. However, batch effects between multiple slices pose significant challenges in analyzing SRT data. To address these challenges, we have developed a plug-and-play batch correction method called Global Nearest Neighbor (G2N) anchor pairs selection. G2N effectively mitigates batch effects by selecting representative anchor pairs across slices. Building upon G2N, we propose STG3Net, which cleverly combines masked graph convolutional autoencoders as backbone modules. These autoencoders, integrated with generative adversarial learning, enable STG3Net to achieve robust multi-slice spatial domain identification and batch correction. We comprehensively evaluate the feasibility of STG3Net on three multiple SRT datasets from different platforms, considering accuracy, consistency, and the F1LISI metric (a measure of batch effect correction efficiency). Compared to existing methods, STG3Net achieves the best overall performance while preserving the biological variability and connectivity between slices. Source code and all public datasets used in this paper are available at https://github.com/wenwenmin/STG3Net and https://zenodo.org/records/12737170.