Salim Agus, Bhuva Dharmesh D, Chen Carissa, Tan Chin Wee, Yang Pengyi, Davis Melissa J, Yang Jean Y H
Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, 3010, VIC, Australia.
Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, 3052, VIC, Australia.
Genome Biol. 2025 Apr 29;26(1):109. doi: 10.1186/s13059-025-03565-y.
Normalization of spatial transcriptomics data is challenging due to spatial association between region-specific library size and biology. We develop SpaNorm, the first spatially-aware normalization method that concurrently models library size effects and the underlying biology, segregates these effects, and thereby removes library size effects without removing biological information. Using 27 tissue samples from 6 datasets spanning 4 technological platforms, SpaNorm outperforms commonly used single-cell normalization approaches while retaining spatial domain information and detecting spatially variable genes. SpaNorm is versatile and works equally well for multicellular and subcellular spatial transcriptomics data with relatively robust performance under different segmentation methods.
由于区域特异性文库大小与生物学之间的空间关联，空间转录组学数据的标准化具有挑战性。我们开发了SpaNorm，这是第一种具有空间感知能力的标准化方法，它同时对文库大小效应和潜在生物学进行建模，分离这些效应，从而在不去除生物学信息的情况下消除文库大小效应。使用来自跨越4种技术平台的6个数据集的27个组织样本，SpaNorm在保留空间域信息和检测空间可变基因的同时，优于常用的单细胞标准化方法。SpaNorm具有通用性，对于多细胞和亚细胞空间转录组学数据同样适用，在不同的分割方法下具有相对稳健的性能。
