CellsFromSpace:一种快速、准确、无参考文献的工具,用于对空间分布的 omics 数据进行解卷积和注释。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Bioinformatics advances Pub Date : 2024-05-30 eCollection Date: 2024-01-01 DOI:10.1093/bioadv/vbae081
Corentin Thuilliez, Gaël Moquin-Beaudry, Pierre Khneisser, Maria Eugenia Marques Da Costa, Slim Karkar, Hanane Boudhouche, Damien Drubay, Baptiste Audinot, Birgit Geoerger, Jean-Yves Scoazec, Nathalie Gaspar, Antonin Marchais
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引用次数: 0

摘要

动机空间转录组学通过捕捉数百万个细胞在其空间环境中的转录组特征,能够分析健康和患病器官中的细胞串扰。然而,空间转录组学方法也对与空间坐标相关的多维数据分析提出了新的计算挑战:在此背景下,我们推出了一种基于独立成分分析(ICA)的新型分析框架 CellsFromSpace,它允许用户在不依赖单细胞参考数据集的情况下分析各种商用技术。CellsFromSpace 中采用的 ICA 方法可将空间转录组学数据分解为与不同细胞类型或活动相关的可解释成分。ICA 还能减少噪音或伪影,并通过选择成分对感兴趣的细胞类型进行子集分析。我们利用真实世界的样本展示了 CellsFromSpace 的灵活性和性能,证明 ICA 能够成功识别空间分布的细胞以及罕见的弥散细胞,并对 Visium、Slide-seq、MERSCOPE 和 CosMX 技术的数据集进行定量解旋。与目前其他无参照解卷积工具的对比分析也凸显了 CellsFromSpace 在处理复杂甚至多样本数据集方面的速度、可扩展性和准确性。CellsFromSpace 还提供用户友好型图形界面,使非生物信息学家也能根据空间分布和贡献基因注释和解释成分,并进行全面的下游分析:CellsFromSpace(CFS)以 R 软件包的形式发布,可在 github 上获取 https://github.com/gustaveroussy/CFS 以及教程、示例和详细文档。
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CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data.

Motivation: Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates.

Results: In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset. The ICA approach deployed in CellsFromSpace decomposes spatial transcriptomics data into interpretable components associated with distinct cell types or activities. ICA also enables noise or artifact reduction and subset analysis of cell types of interest through component selection. We demonstrate the flexibility and performance of CellsFromSpace using real-world samples to demonstrate ICA's ability to successfully identify spatially distributed cells as well as rare diffuse cells, and quantitatively deconvolute datasets from the Visium, Slide-seq, MERSCOPE, and CosMX technologies. Comparative analysis with a current alternative reference-free deconvolution tool also highlights CellsFromSpace's speed, scalability and accuracy in processing complex, even multisample datasets. CellsFromSpace also offers a user-friendly graphical interface enabling non-bioinformaticians to annotate and interpret components based on spatial distribution and contributor genes, and perform full downstream analysis.

Availability and implementation: CellsFromSpace (CFS) is distributed as an R package available from github at https://github.com/gustaveroussy/CFS along with tutorials, examples, and detailed documentation.

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