Principles and challenges of modeling temporal and spatial omics data

• Verfasst von: Velten, Britta [VerfasserIn]
• Verfasst von: Stegle, Oliver [VerfasserIn]
• Titel: Principles and challenges of modeling temporal and spatial omics data
• Verf.angabe: Britta Velten & Oliver Stegle
• E-Jahr: 2023
• Jahr: October 2023
• Umfang: 13 S.
• Illustrationen: Illustrationen
• Fussnoten: Online veröffentlicht: 14. September 2023 ; Gesehen am 07.11.2023
• Titel Quelle: Enthalten in: Nature methods
• Ort Quelle: London [u.a.] : Nature Publishing Group, 2004
• Jahr Quelle: 2023
• Band/Heft Quelle: 20(2023), 10 vom: Okt., Seite 1462-1474
• ISSN Quelle: 1548-7105
• DOI: doi:10.1038/s41592-023-01992-y
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Computational models
• Sach-SW: Genomics
• Sach-SW: Machine learning
• Sach-SW: Software
• Sach-SW: Statistical methods
• K10plus-PPN: 1869521382

Abstract

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.