Library size confounds biology in spatial transcriptomics data

Genome Biol. 2024 Apr 18;25(1):99. doi: 10.1186/s13059-024-03241-7.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biology
  • Gene Expression Profiling* / methods
  • Sequence Analysis, RNA / methods
  • Single-Cell Analysis* / methods