MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial omics data in bioconductor.

IF 4.4 3区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS Bioinformatics Pub Date : 2023-09-02 DOI:10.1093/bioinformatics/btad550
Bárbara Zita Peters Couto, Nicholas Robertson, Ellis Patrick, Shila Ghazanfar
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Abstract

Motivation: Imaging-based spatial transcriptomics (ST) technologies have achieved subcellular resolution, enabling detection of individual molecules in their native tissue context. Data associated with these technologies promise unprecedented opportunity toward understanding cellular and subcellular biology. However, in R/Bioconductor, there is a scarcity of existing computational infrastructure to represent such data, and particularly to summarize and transform it for existing widely adopted computational tools in single-cell transcriptomics analysis, including SingleCellExperiment and SpatialExperiment (SPE) classes. With the emergence of several commercial offerings of imaging-based ST, there is a pressing need to develop consistent data structure standards for these technologies at the individual molecule-level.

Results: To this end, we have developed MoleculeExperiment, an R/Bioconductor package, which (i) stores molecule and cell segmentation boundary information at the molecule-level, (ii) standardizes this molecule-level information across different imaging-based ST technologies, including 10× Genomics' Xenium, and (iii) streamlines transition from a MoleculeExperiment object to a SpatialExperiment object. Overall, MoleculeExperiment is generally applicable as a data infrastructure class for consistent analysis of molecule-resolved spatial omics data.

Availability and implementation: The MoleculeExperiment package is publicly available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html. Source code is available on Github at: https://github.com/SydneyBioX/MoleculeExperiment. The vignette for MoleculeExperiment can be found at https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html.

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MoleculeExperiment为生物导管中分子解析的空间组学数据提供了一致的基础设施。
动机:基于成像的空间转录组学(ST)技术已经实现了亚细胞分辨率,能够在其天然组织环境中检测单个分子。与这些技术相关的数据为理解细胞和亚细胞生物学提供了前所未有的机会。然而,在R/Bioconductor中,缺乏现有的计算基础设施来表示这些数据,特别是为单细胞转录组学分析中广泛采用的现有计算工具总结和转换这些数据,包括单细胞实验和空间实验(SPE)类。随着基于成像的ST的几种商业产品的出现,迫切需要在单个分子水平上为这些技术开发一致的数据结构标准。结果:为此,我们开发了MoleculeExperiment,一种R/生物导体包,它(i)在分子水平上存储分子和细胞分割边界信息,(ii)在不同的基于成像的ST技术(包括10×Genomics的Xenium)中标准化这种分子水平的信息,以及(iii)简化从分子实验对象到空间实验对象的转换。总体而言,MoleculeExperiment通常适用于作为一个数据基础设施类,用于对分子解析的空间组学数据进行一致分析。可用性和实施:MoleculeExperiment软件包可在Bioconductor上公开获取,网址为https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html.源代码可在Github上获得,网址为:https://github.com/SydneyBioX/MoleculeExperiment.MoleculeExperiment的小插曲可以在https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Bioinformatics
Bioinformatics 生物-生化研究方法
CiteScore
11.20
自引率
5.20%
发文量
753
审稿时长
2.1 months
期刊介绍: The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.
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