A comprehensive comparison on clustering methods for multi-slice spatially resolved transcriptomics data analysis

doi:10.1093/bib/bbaf471

2025-09-01

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DOI：10.1093/bib/bbaf471

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摘要

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空间转录组学（ST）数据通过提供空间信息，能够同时分析基因表达分布及其在组织内的空间模式。聚类或空间域检测代表了ST数据的一种基本方法，有助于探索具有共享基因表达或组织学特征的空间组织。传统上，ST的聚类算法主要关注单个组织切片。然而，来自同一或相似组织样本的众多连续组织切片的出现（无论是在个体内还是在个体间），推动了多切片聚类方法的发展。本研究评估了7种单切片和4种多切片聚类方法在两个模拟数据集和四个真实数据集上的表现。此外，我们还研究了预处理技术（包括空间坐标对齐，如PASTE，以及基因表达批次效应去除，如Harmony）对聚类性能的有效性。本研究为多切片ST数据的聚类方法提供了全面的比较，可作为不同场景下方法选择的实用指南。

Keyword:

spatial transcriptomics

clustering

multi-slice clustering

evaluation

期刊

Briefings in Bioinformatics

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7.7

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5.8K

被引数:

2.7W

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university of north carolina

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7.4W

论文数: 6.5W

被引数: 93

University of North Carolina School of Medicine

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1.6W

论文数: 1.1W

被引数: 20

University of North Carolina Chapel Hill

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3.9W

论文数: 3.1W

被引数: 46

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