Library size confounds biology in spatial transcriptomics data
Dharmesh D. Bhuva, Chin Wee Tan, Agus Salim, Claire Marceaux, Marie Pickering, Jinjin Chen, Malvika Kharbanda, Xinyi Jin, Ning Liu, Kristen Feher, Givanna Putri, Wayne D. Tilley, Theresa E. Hickey, Marie-Liesse Asselin-Labat, Belinda Phipson, Melissa J. Davis
Abstract
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.