School of Big Data and Software Engineering, Chongqing University, Chongqing, China.
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
Commun Biol. 2024 Oct 5;7(1):1271. doi: 10.1038/s42003-024-06964-2.
Image-based spatial transcriptomic sequencing technologies have enabled the measurement of gene expression at single-cell resolution, but with a limited number of genes. Current computational approaches attempt to overcome these limitations by imputing missing genes, but face challenges regarding prediction accuracy and identification of cell populations due to the neglect of gene-gene relationships. In this context, we present stImpute, a method to impute spatial transcriptomics according to reference scRNA-seq data based on the gene network constructed from the protein language model ESM-2. Specifically, stImpute employs an autoencoder to create gene expression embeddings for both spatial transcriptomics and scRNA-seq data, which are used to identify the nearest neighboring cells between scRNA-seq and spatial transcriptomics datasets. According to the neighbored cells, the gene expressions of spatial transcriptomics cells are imputed through a graph neural network, where nodes are genes, and edges are based on cosine similarity between the ESM-2 embeddings of the gene-encoding proteins. The gene prediction uncertainty is further measured through a deep learning model. stImpute was shown to consistently outperform state-of-the-art methods across multiple datasets concerning imputation and clustering. stImpute also demonstrates robustness in producing consistent results that are insensitive to model parameters.
基于图像的空间转录组测序技术使我们能够以单细胞分辨率测量基因表达，但检测到的基因数量有限。目前的计算方法试图通过内插缺失的基因来克服这些限制，但由于忽略了基因-基因关系，它们在预测准确性和细胞群体识别方面面临挑战。在这种情况下，我们提出了 stImpute，这是一种根据参考 scRNA-seq 数据对空间转录组学进行内插的方法，该方法基于从蛋白质语言模型 ESM-2 构建的基因网络。具体来说，stImpute 使用自动编码器为空间转录组学和 scRNA-seq 数据创建基因表达嵌入，这些嵌入用于识别 scRNA-seq 和空间转录组学数据集之间的最近邻细胞。根据邻接细胞，通过图神经网络对空间转录组学细胞的基因表达进行内插，其中节点是基因，边是基于基因编码蛋白的 ESM-2 嵌入之间的余弦相似度。通过深度学习模型进一步测量基因预测的不确定性。stImpute 在多个数据集的内插和聚类方面均优于最先进的方法。stImpute 还表现出在产生一致结果方面的稳健性，对模型参数不敏感。
