South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, 5005, Australia.
Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, 3052, Australia.
Genome Biol. 2024 Apr 18;25(1):99. doi: 10.1186/s13059-024-03241-7.
Spatial molecular data has transformed the study of disease microenvironments, though, larger datasets pose an analytics challenge prompting the direct adoption of single-cell RNA-sequencing tools including normalization methods. Here, we demonstrate that library size is associated with tissue structure and that normalizing these effects out using commonly applied scRNA-seq normalization methods will negatively affect spatial domain identification. Spatial data should not be specifically corrected for library size prior to analysis, and algorithms designed for scRNA-seq data should be adopted with caution.
空间分子数据改变了疾病微环境的研究方式，但是，更大的数据集带来了分析挑战，这促使人们直接采用单细胞 RNA 测序工具，包括标准化方法。在这里，我们证明了文库大小与组织结构有关，并且使用常用的 scRNA-seq 标准化方法对这些影响进行归一化会负面影响空间域的识别。在进行分析之前，不应该专门针对文库大小对空间数据进行校正，并且应该谨慎采用专为 scRNA-seq 数据设计的算法。
