Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis
| العنوان: | Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis |
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| المؤلفون: | Damien Arnol, Bernd Bodenmiller, Denis Schapiro, Julio Saez-Rodriguez, Oliver Stegle |
| المساهمون: | University of Zurich, Saez-Rodriguez, Julio |
| المصدر: | Cell reports 29(1), 202-211.e6 (2019). doi:10.1016/j.celrep.2019.08.077 Cell Reports, Vol 29, Iss 1, Pp 202-211.e6 (2019) Cell Reports Cell Reports, 29 (1) |
| بيانات النشر: | Elsevier BV, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | 0301 basic medicine, Multiplexed imaging, Computer science, Gene Expression, Breast Neoplasms, 610 Medicine & health, Genetics and Molecular Biology, Cell Communication, Computational biology, Spatial variance, Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, Component analysis, 1300 General Biochemistry, Genetics and Molecular Biology, Humans, Mass cytometry, Random effect model, lcsh:QH301-705.5, Spatial analysis, Gaussian Process, Analysis of Variance, Spatial structure, Gene Expression Profiling, Spatially resolved, Protein Expression Profiling, 030104 developmental biology, lcsh:Biology (General), General Biochemistry, RNA, Female, Spatial variability, 11493 Department of Quantitative Biomedicine, Software, 030217 neurology & neurosurgery |
| الوصف: | Summary Technological advances enable assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing molecular variations in physiological contexts. While these methods are increasingly accessible, computational approaches for studying the interplay of the spatial structure of tissues and cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying different dimensions of spatial variation and in particular quantifies the effect of cell-cell interactions on gene expression. In a breast cancer Imaging Mass Cytometry dataset, our model yields interpretable spatial variance signatures, which reveal cell-cell interactions as a major driver of protein expression heterogeneity. Applied to high-dimensional imaging-derived RNA data, SVCA identifies plausible gene families that are linked to cell-cell interactions. SVCA is available as a free software tool that can be widely applied to spatial data from different technologies. Graphical Abstract Highlights • Statistical method to assess cell-cell interactions in spatial expression data • Generally applicable to diverse data types and biological systems • Illustrated on IMC data in human cancer and seqFISH data in mouse hippocampus • Open source software available on github Arnol et al. present a statistical method for analyzing single-cell expression data in a spatial context. The method identifies the sources of gene expression variability by decomposing it into different components, each attributable to a different source. These sources include aspects of spatial variation, in particular cell-cell interactions. |
| وصف الملف: | Modeling_Cell-Cell_Interactions_from_Spatial_Molecular_Data_with_Spatial_Variance_Component_Analysis.pdf - application/pdf; application/application/pdf |
| تدمد: | 2211-1247 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::33adda09e80df1ab98599b79b20679e9Test https://doi.org/10.1016/j.celrep.2019.08.077Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....33adda09e80df1ab98599b79b20679e9 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22111247 |
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