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Integrating Graph Convolutional Networks for Missing Gene Expression Imputation
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DOI：10.1109/TCBBIO.2025.3605719.png)
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Single-cell RNA sequencing (scRNA-seq) techniques are emerging to revolutionize modern biomedical sciences by providing a detailed landscape of individual cells. However, these methods often lack crucial spatial localization information. To address this gap, spatial transcriptomic technologies have developed, enabling gene expression profiling while mapping cells spatial information. Yet, the gene throughput in spatial transcriptomic technologies makes it challenging to characterize whole-transcriptome-level data for single cells in space. In this context, approaches for predicting the spatial distribution of genes are still under development. Here, we present GCNgene, a novel method to predict the spatial distribution of the undetected RNA transcripts, through integrating spatial and scRNA-seq datasets. GCNgene leverages a graph convolutional network to embed spatial transcriptomics data and then applies a learned rule to reconstruct gene expression by combining the reference single-cell data with the calculated cell-type proportions. Ultimately, this learned paradigm enables accurate predictions of gene expression levels.
Keyword:
Transcriptomics
Spatial databases
Gene expression
Training
Sequential analysis
RNA
Electronic mail
Data models
Bioinformatics
Predictive models
Gene expression prediction
graph convolutional network
feature embedding
spatial transcriptomics
deep learning
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论文数:
151
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