Abstract
AbstractSpatial 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.
Funder
Cancer Council Victoria
Australian Lions Childhood Cancer Research Foundation
Cure Brain Cancer Foundation
National Health and Medical Research Council
National Breast Cancer Foundation
Publisher
Springer Science and Business Media LLC
Reference37 articles.
1. Marx V. Method of the year: spatially resolved transcriptomics. Nat Methods. 2021;18:9–14.
2. Chen A, Liao S, Cheng M, Ma K, Wu L, Lai Y, Qiu X, Yang J, Xu J, Hao S, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022;185(1777–1792): e1721.
3. He S, Bhatt R, Brown C, Brown EA, Buhr DL, Chantranuvatana K, Danaher P, Dunaway D, Garrison RG, Geiss G, et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat Biotechnol. 2022;40(12):1794–806.
4. Janesick A, Shelansky R, Gottscho AD, Wagner F, Williams SR, Rouault M, Beliakoff G, Morrison CA, Oliveira MF, Sicherman JT, Kohlway A, Abousoud J, Drennon TY, Mohabbat SH, 10x Development Teams, Taylor SEB. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat Commun. 2023;14(1):8353.
5. Moses L, Pachter L. Museum of spatial transcriptomics. Nat Methods. 2022;19:534–46.