文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期，新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

SpatialData: an open and universal data framework for spatial omics.

作者信息

Marconato Luca, Palla Giovanni, Yamauchi Kevin A, Virshup Isaac, Heidari Elyas, Treis Tim, Vierdag Wouter-Michiel, Toth Marcella, Stockhaus Sonja, Shrestha Rahul B, Rombaut Benjamin, Pollaris Lotte, Lehner Laurens, Vöhringer Harald, Kats Ilia, Saeys Yvan, Saka Sinem K, Huber Wolfgang, Gerstung Moritz, Moore Josh, Theis Fabian J, Stegle Oliver

机构信息

European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.

Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany.

出版信息

Nat Methods. 2025 Jan;22(1):58-62. doi: 10.1038/s41592-024-02212-x. Epub 2024 Mar 20.

DOI:10.1038/s41592-024-02212-x
PMID:38509327
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11725494/
Abstract

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/82865658b718/41592_2024_2212_Fig9_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/285d3d27a55f/41592_2024_2212_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/e1496b3240c4/41592_2024_2212_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/7ef497a5e54b/41592_2024_2212_Fig3_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/5b81241dcb98/41592_2024_2212_Fig4_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/4949e2f55038/41592_2024_2212_Fig5_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/c958ff827efc/41592_2024_2212_Fig6_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/3aac69208d90/41592_2024_2212_Fig7_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/4aa895288860/41592_2024_2212_Fig8_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/82865658b718/41592_2024_2212_Fig9_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/285d3d27a55f/41592_2024_2212_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/e1496b3240c4/41592_2024_2212_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/7ef497a5e54b/41592_2024_2212_Fig3_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/5b81241dcb98/41592_2024_2212_Fig4_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/4949e2f55038/41592_2024_2212_Fig5_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/c958ff827efc/41592_2024_2212_Fig6_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/3aac69208d90/41592_2024_2212_Fig7_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/4aa895288860/41592_2024_2212_Fig8_ESM.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6df4/11725494/82865658b718/41592_2024_2212_Fig9_ESM.jpg

相似文献

[1]
SpatialData: an open and universal data framework for spatial omics.

Nat Methods. 2025-1

[2]
Sopa: a technology-invariant pipeline for analyses of image-based spatial omics.

Nat Commun. 2024-6-11

[3]
Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model.

Nat Commun. 2024-8-2

[4]
HyperGCN: an effective deep representation learning framework for the integrative analysis of spatial transcriptomics data.

BMC Genomics. 2024-6-5

[5]
Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.

Brief Bioinform. 2024-5-23

[6]
Resolving tissue complexity by multimodal spatial omics modeling with MISO.

Nat Methods. 2025-3

[7]
CITEMO: A flexible single-cell multimodal omics analysis framework to reveal the heterogeneity of immune cells.

RNA Biol. 2022-1

[8]
Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows.

Nat Methods. 2025-4

[9]
Spatial integration of multi-omics single-cell data with SIMO.

Nat Commun. 2025-2-1

[10]
Deciphering spatial domains from spatial multi-omics with SpatialGlue.

Nat Methods. 2024-9

引用本文的文献

[1]
Segmentation Matters: Recognizing the Cell Segmentation Challenge in Spatial Transcriptomics.

bioRxiv. 2025-8-29

[2]
PRISM: a Python package for interactive and integrated analysis of multiplexed tissue microarrays.

NAR Genom Bioinform. 2025-8-21

[3]
Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering.

bioRxiv. 2025-6-27

[4]
Spatial isoform sequencing at sub-micrometer single-cell resolution reveals novel patterns of spatial isoform variability in brain cell types.

bioRxiv. 2025-6-25

[5]
Defining Keypoints to Align H&E Images and Xenium DAPI-Stained Images Automatically.

Cells. 2025-6-30

[6]
Multimodal bioimaging across disciplines and scales: challenges, opportunities and breaking down barriers.

Npj Imaging. 2024-3-1

[7]
Driving innovations in cancer research through spatial metabolomics: a bibliometric review of trends and hotspot.

Front Immunol. 2025-6-10

[8]
Single-cell transcriptomic and chromatin dynamics of the human brain in PTSD.

Nature. 2025-6-18

[9]
Spatial omics technology potentially promotes the progress of tumor immunotherapy.

Br J Cancer. 2025-6-2

[10]
Analysis-ready VCF at Biobank scale using Zarr.

Gigascience. 2025-1-6

本文引用的文献

[1]
WebAtlas pipeline for integrated single-cell and spatial transcriptomic data.

Nat Methods. 2025-1

[2]
High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis.

Nat Commun. 2023-12-19

[3]
STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization.

Nucleic Acids Res. 2024-1-5

[4]
MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial omics data in bioconductor.

Bioinformatics. 2023-9-2

[5]
OME-Zarr: a cloud-optimized bioimaging file format with international community support.

Histochem Cell Biol. 2023-9

[6]
The scverse project provides a computational ecosystem for single-cell omics data analysis.

Nat Biotechnol. 2023-5

[7]
Methods and applications for single-cell and spatial multi-omics.

Nat Rev Genet. 2023-8

[8]
SODB facilitates comprehensive exploration of spatial omics data.

Nat Methods. 2023-3

[9]
Spatial omics technologies at multimodal and single cell/subcellular level.

Genome Biol. 2022-12-13

[10]
Spatial biology of cancer evolution.

Nat Rev Genet. 2023-5

文献AI研究员

20分钟写一篇综述，助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索