用单细胞数据补全空间转录组未测基因，提升预测精度。
CASPER: Cross-modal Alignment of Spatial and single-cell Profiles for Expression Recovery
- 基于跨注意力机制，融合单细胞数据的中心点表示来预测空间基因表达。
- 在12项指标中9项超越基线模型，跨数据集验证效果稳定。
- 适合从事空间转录组与单细胞数据融合研究的科研人员。
空间转录组技术可在组织原生环境中定位基因表达，但受实验限制和成本过高，仅能检测有限基因。为解决此问题，计算模型结合单细胞RNA测序数据与空间转录组数据，以预测未测量基因的表达。本文提出CASPER，一种基于跨注意力机制的框架，利用单细胞RNA测序的中心点表示来预测空间转录组中未测量的基因表达。我们在四个现有空间转录组/单细胞RNA测序数据对上，对四种主流基线模型进行了严格测试。CASPER在十二项指标中的九项表现显著优于基线模型。本工作为后续空间转录组向单细胞数据的模态转换研究奠定了基础。CASPER代码已公开于https://github.com/AI4Med-Lab/CASPER。
原文摘要 · Abstract (English)
Spatial Transcriptomics enables mapping of gene expression within its native tissue context, but current platforms measure only a limited set of genes due to experimental constraints and excessive costs. To overcome this, computational models integrate Single-Cell RNA Sequencing data with Spatial Transcriptomics to predict unmeasured genes. We propose CASPER, a cross-attention based framework that predicts unmeasured gene expression in Spatial Transcriptomics by leveraging centroid-level representations from Single-Cell RNA Sequencing. We performed rigorous testing over four state-of-the-art Spatial Transcriptomics/Single-Cell RNA Sequencing dataset pairs across four existing baseline models. CASPER shows significant improvement in nine out of the twelve metrics for our experiments. This work paves the way for further work in Spatial Transcriptomics to Single-Cell RNA Sequencing modality translation. The code for CASPER is available at https://github.com/AI4Med-Lab/CASPER.
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