使用VR - Omics在二维和三维中对多层空间转录组学数据进行自动整合。
Automated integration of multi-slice spatial transcriptomics data in 2D and 3D using VR-Omics.
作者信息
Bienroth Denis, Charitakis Natalie, Wong Dillon, Zhang Yunhan C, Jaeger-Honz Sabrina, Ding Jialin, Watt Kevin I, Stolper Julian, Chambers-Smith Hazel, MacGregor Duncan, Christiansen Bronwyn, Vivien Celine, Piers Adam T, Waylen Lisa N, Hoffmann Lucas B, Tang Jessica, La Hue M, Du Mei R M, Mohenska Monika, Polo Jose M, Grimmond Sean, Scott Ethan, Rossello Fernando J, Porrello Enzo R, Klein Karsten, Nim Hieu T, Elliott David A, Schreiber Falk, Ramialison Mirana
机构信息
Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia.
Department of Paediatrics, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, 3010, Australia.
出版信息
Genome Biol. 2025 Jul 2;26(1):182. doi: 10.1186/s13059-025-03630-6.
The field of spatial transcriptomics is rapidly evolving, with increasing sample complexity, resolution, and tissue size. Yet the field lacks comprehensive and intuitive solutions for automated integration and analysis of multi-slice data in either co-planar (2D) or stacked (3D) formation. To address this, we develop VR-Omics, a free, platform-agnostic software that provides end-to-end automated processing of multi-slice data through a biologist-friendly interface. Benchmarking against existing methods demonstrates VR-Omics' unique strengths to perform comprehensive end-to-end analysis of multi-slice stacked data. Through co-planar slice analysis, VR-Omics uncovers previously undetected, dysregulated metabolic networks within rare pediatric cardiac rhabdomyomas, demonstrating its potential for biological discoveries.
空间转录组学领域正在迅速发展，样本的复杂性、分辨率和组织大小都在不断增加。然而，该领域缺乏用于自动整合和分析共面（2D）或堆叠（3D）形式的多层数据的全面且直观的解决方案。为了解决这一问题，我们开发了VR-Omics，这是一款免费的、与平台无关的软件，它通过一个对生物学家友好的界面提供多层数据的端到端自动化处理。与现有方法的基准测试表明，VR-Omics在对多层堆叠数据进行全面的端到端分析方面具有独特优势。通过共面切片分析，VR-Omics在罕见的儿童心脏横纹肌瘤中发现了以前未检测到的、失调的代谢网络，证明了其在生物学发现方面的潜力。
Zotero 插件