SpatialOne：大规模端到端分析 Visium 数据。

SpatialOne: end-to-end analysis of visium data at scale.

机构信息

Digital R&D, Sanofi, Paris 75017, France.

Precision Medicine & Computational Biology, Sanofi, Vitry-sur-Seine 94400, France.

出版信息

Bioinformatics. 2024 Sep 2;40(9). doi: 10.1093/bioinformatics/btae509.

DOI:10.1093/bioinformatics/btae509

PMID:39152991

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11374018/

Abstract

MOTIVATION

Spatial transcriptomics allow to quantify mRNA expression within the spatial context. Nonetheless, in-depth analysis of spatial transcriptomics data remains challenging and difficult to scale due to the number of methods and libraries required for that purpose.

RESULTS

Here we present SpatialOne, an end-to-end pipeline designed to simplify the analysis of 10x Visium data by combining multiple state-of-the-art computational methods to segment, deconvolve, and quantify spatial information; this approach streamlines the analysis of reproducible spatial-data at scale.

AVAILABILITY AND IMPLEMENTATION

SpatialOne source code and execution examples are available at https://github.com/Sanofi-Public/spatialone-pipeline, experimental data is available at https://zenodo.org/records/12605154. SpatialOne is distributed as a docker container image.

摘要

动机

空间转录组学允许在空间背景下定量 mRNA 表达。然而，由于需要多种方法和文库，因此对空间转录组学数据的深入分析仍然具有挑战性且难以扩展。

结果

在这里，我们提出了 SpatialOne，这是一个端到端的管道，旨在通过结合多种最先进的计算方法来简化 10x Visium 数据的分析，以分割、去卷积和量化空间信息；这种方法简化了可重复空间数据的大规模分析。

可用性和实现

SpatialOne 的源代码和执行示例可在 https://github.com/Sanofi-Public/spatialone-pipeline 上获得，实验数据可在 https://zenodo.org/records/12605154 上获得。SpatialOne 以 Docker 容器映像的形式分发。