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Abstract <p>Recent advancements in spatial transcriptomics (ST) technologies have greatly revolutionized our understanding of tissue heterogeneity and cellular functions. However, popular ST, such as 10x Visium, still fall short in achieving true single-cell resolution, underscoring an urgent need for in-silico methods that can accurately resolve cell type composition within ST data. While several methods have been proposed, most rely solely on gene expression profiles, often neglecting spatial context, which results in suboptimal performance. Additionally, many deconvolution methods dependent on scRNA-seq data fail to align the distribution of ST and scRNA-seq reference data, consequently affecting the accuracy of cell type mapping. In this study, we propose stGNN, a novel spatially-informed graph learning framework powered by statistical modeling for resolving fine-grained cell type compositions in ST. To capture comprehensive features, we develop a dual encoding module, utilizing both a graph convolutional network (GCN) and an auto-encoder to learn spatial and non-spatial representations respectively. Following that, we further design an adaptive attention mechanism to integrate these representations layer-by-layer, capturing multi-scale spatial structures from low to high order and thus improving representation learning. Additionally, for model training, we adopt a negative log-likelihood loss function that aligns the distribution of ST data with scRNA-seq (or snRNA-seq) reference data, enhancing the accuracy of cell type proportion prediction in ST. To assess the performance of stGNN, we applied our proposed model to six ST datasets from various platforms, including 10x Visium, Slide-seqV2, and Visium HD, for cell type proportion estimation. Our results demonstrate that stGNN consistently outperforms seven state-of-the-art methods. Notably, when applied to mouse brain tissues, stGNN successfully resolves clear cortical layers at a high resolution. Additionally, we show that stGNN is able to effectively resolve ST at different resolutions. In summary, stGNN provides a powerful framework for analyzing the spatial distribution of diverse cell populations in complex tissue structures. stGNN’s code is openly shared on <a href="https://github.com/LiangSDNULab/stGNN">https://github.com/LiangSDNULab/stGNN</a>.</p> Graphical Abstract <p>Overview of stGNN. stGNN consists of three main modules: an auto-encoder module, a GCN-based encoder module, and an attentive integration module. First, the auto-encoder takes the preprocessed gene expressions as inputs to learn non-spatial representations, capturing the fundamental gene expression patterns. Next, the GCN-based encoder is to learn spatial representations by incorporating the representation learned by the auto-encoder and spatial context. To learn more informative representations, an attention mechanism is introduced to adaptively integrate the non-spatial and spatial representations layer by layer. The last layer of the GCN-based encoder is designed as a multiple classification layer (predictor) to predict cell type proportions. To align the distribution of ST data and scRNA-seq data and facilitate model training, a negative log-likelihood loss function is introduced, where the mean and dispersion parameter of ST data is estimated by the scvi-tools package from scRNA-seq reference data.</p>

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stGNN: Spatially Informed Cell-Type Deconvolution Based on Deep Graph Learning and Statistical Modeling

  • 期刊论文

  • 发表日期：

    2025年6月26日

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  • Juntong Zhu,
  • Daoyuan Wang,
  • Siqi Chen,
  • Lili Meng,
  • Yahui Long,
  • Cheng Liang

摘要

Abstract

Recent advancements in spatial transcriptomics (ST) technologies have greatly revolutionized our understanding of tissue heterogeneity and cellular functions. However, popular ST, such as 10x Visium, still fall short in achieving true single-cell resolution, underscoring an urgent need for in-silico methods that can accurately resolve cell type composition within ST data. While several methods have been proposed, most rely solely on gene expression profiles, often neglecting spatial context, which results in suboptimal performance. Additionally, many deconvolution methods dependent on scRNA-seq data fail to align the distribution of ST and scRNA-seq reference data, consequently affecting the accuracy of cell type mapping. In this study, we propose stGNN, a novel spatially-informed graph learning framework powered by statistical modeling for resolving fine-grained cell type compositions in ST. To capture comprehensive features, we develop a dual encoding module, utilizing both a graph convolutional network (GCN) and an auto-encoder to learn spatial and non-spatial representations respectively. Following that, we further design an adaptive attention mechanism to integrate these representations layer-by-layer, capturing multi-scale spatial structures from low to high order and thus improving representation learning. Additionally, for model training, we adopt a negative log-likelihood loss function that aligns the distribution of ST data with scRNA-seq (or snRNA-seq) reference data, enhancing the accuracy of cell type proportion prediction in ST. To assess the performance of stGNN, we applied our proposed model to six ST datasets from various platforms, including 10x Visium, Slide-seqV2, and Visium HD, for cell type proportion estimation. Our results demonstrate that stGNN consistently outperforms seven state-of-the-art methods. Notably, when applied to mouse brain tissues, stGNN successfully resolves clear cortical layers at a high resolution. Additionally, we show that stGNN is able to effectively resolve ST at different resolutions. In summary, stGNN provides a powerful framework for analyzing the spatial distribution of diverse cell populations in complex tissue structures. stGNN’s code is openly shared on https://github.com/LiangSDNULab/stGNN.

Graphical Abstract

Overview of stGNN. stGNN consists of three main modules: an auto-encoder module, a GCN-based encoder module, and an attentive integration module. First, the auto-encoder takes the preprocessed gene expressions as inputs to learn non-spatial representations, capturing the fundamental gene expression patterns. Next, the GCN-based encoder is to learn spatial representations by incorporating the representation learned by the auto-encoder and spatial context. To learn more informative representations, an attention mechanism is introduced to adaptively integrate the non-spatial and spatial representations layer by layer. The last layer of the GCN-based encoder is designed as a multiple classification layer (predictor) to predict cell type proportions. To align the distribution of ST data and scRNA-seq data and facilitate model training, a negative log-likelihood loss function is introduced, where the mean and dispersion parameter of ST data is estimated by the scvi-tools package from scRNA-seq reference data.