Header

UZH-Logo

Maintenance Infos

Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis


Arnol, Damien; Schapiro, Denis; Bodenmiller, Bernd; Saez-Rodriguez, Julio; Stegle, Oliver (2019). Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis. Cell Reports, 29(1):202-211.e6.

Abstract

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.

Abstract

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.

Statistics

Citations

Dimensions.ai Metrics
5 citations in Web of Science®
5 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

12 downloads since deposited on 07 Feb 2020
12 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Quantitative Biomedicine
Dewey Decimal Classification:610 Medicine & health
Scopus Subject Areas:Life Sciences > General Biochemistry, Genetics and Molecular Biology
Uncontrolled Keywords:General Biochemistry, Genetics and Molecular Biology
Language:English
Date:1 October 2019
Deposited On:07 Feb 2020 10:45
Last Modified:11 May 2020 19:36
Publisher:Cell Press (Elsevier)
ISSN:2211-1247
OA Status:Gold
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.celrep.2019.08.077
PubMed ID:31577949

Download

Gold Open Access

Download PDF  'Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis'.
Preview
Content: Published Version
Language: English
Filetype: PDF
Size: 1MB
View at publisher
Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)