Library size confounds biology in spatial transcriptomics data.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：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. (2024). Library size confounds biology in spatial transcriptomics data. Genome biology. PMID 38637899 · PMC11025268 · DOI 10.1186/s13059-024-03241-7

证据类型：PRIMARY_RESEARCH

主题：rna-seq、single-cell、spatial-omics

被引次数（采集时）：50

原始记录：[Europe PMC](https://europepmc.org/article/MED/38637899)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

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.

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