Bento: a toolkit for subcellular analysis of spatial transcriptomics data.

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

书目信息

引用：Mah CK, Ahmed N, Lopez NA, Lam DC, Pong A, Monell A, Kern C, Han Y, Prasad G, Cesnik AJ, Lundberg E, Zhu Q, Carter H, Yeo GW. (2024). Bento: a toolkit for subcellular analysis of spatial transcriptomics data. Genome biology. PMID 38566187 · PMC11289963 · DOI 10.1186/s13059-024-03217-7

证据类型：PRIMARY_RESEARCH

主题：rna-seq、spatial-omics

被引次数（采集时）：49

原始记录：[Europe PMC](https://europepmc.org/article/MED/38566187)

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

作者摘要（按来源许可复用）

The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell-cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.

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