Library size confounds biology in spatial transcriptomics data
- Author(s)
- Bhuva, DD; Tan, CW; Salim, A; Marceaux, C; Pickering, MA; Chen, J; Kharbanda, M; Jin, X; Liu, N; Feher, K; Putri, G; Tilley, WD; Hickey, TE; Asselin-Labat, ML; Phipson, B; Davis, MJ;
- Details
- Publication Year 2024-04-18,Volume 25,Issue #1,Page 99
- Journal Title
- Genome Biology
- 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.
- Publisher
- BMC
- Keywords
- Sequence Analysis, RNA/methods; *Single-Cell Analysis/methods; *Gene Expression Profiling/methods; Algorithms; Biology
- Research Division(s)
- Bioinformatics; Personalised Oncology
- PubMed ID
- 38637899
- Publisher's Version
- https://doi.org/10.1186/s13059-024-03241-7
- Open Access at Publisher's Site
https://doi.org/10.1186/s13059-024-03241-7- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2024-06-24 11:29:47
Last Modified: 2024-06-24 11:34:08