High-precision cell-type mapping and annotation of single-cell spatial transcriptomics with STAMapper

IF 10.1 1区生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Genome Biology Pub Date : 2025-10-07 DOI:10.1186/s13059-025-03773-6

Qunlun Shen, Kangning Dong, Shuqin Zhang, Shihua Zhang

引用次数: 0

Abstract

In this paper, we develop a heterogeneous graph neural network, STAMapper, to transfer the cell-type labels from single-cell RNA-sequencing (scRNA-seq) data to single-cell spatial transcriptomics (scST) data. We collect 81 scST datasets consisting of 344 slices and 16 paired scRNA-seq datasets from eight technologies and five tissues to validate the efficiency of STAMapper. STAMapper achieves the best performance on 75 out of 81 datasets compared to competing methods in accuracy. STAMapper demonstrates enhanced performance over manual annotations, particularly at the boundaries of cell clusters, enables the unknown cell-type detection in scST data, and exhibits precise cell subtype annotations.

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利用STAMapper进行单细胞空间转录组学的高精度细胞类型定位和注释

在本文中，我们开发了一个异构图神经网络STAMapper，将细胞类型标记从单细胞rna测序（scRNA-seq）数据转移到单细胞空间转录组学（scST）数据。我们收集了来自8种技术和5种组织的81个scST数据集，包括344个切片和16个配对的scRNA-seq数据集，以验证STAMapper的效率。与竞争方法相比，STAMapper在81个数据集中的75个数据集上实现了最佳性能。STAMapper展示了比手动注释更强的性能，特别是在细胞簇的边界，能够在scST数据中进行未知的细胞类型检测，并展示了精确的细胞亚型注释。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Genome Biology Biochemistry, Genetics and Molecular Biology-Genetics

CiteScore

21.00

自引率

3.30%

发文量

241

审稿时长

2 months

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