Protocols for single-cell RNA-seq and spatial gene expression integration and interactive visualization

Sona, Surbhi; Bradley, Matthew; Ting, Angela H.

doi:10.1016/j.xpro.2023.102047

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Grid Layout: In spot-based technologies like Visium, where spots are arranged in a fixed grid, edge construction is relatively straightforward. Graph edges are typically defined based on direct neighbors in this grid, leading to a structured, regular graph topology ( Dries et al 2021 , Palla et al 2022b , Sona et al 2023 ).…”

Section: Methodsmentioning

confidence: 99%

GraphCompass: spatial metrics for differential analyses of cell organization across conditions

Ali,

Kuijs,

Hediyeh-zadeh

et al. 2024

Bioinformatics

View full text Add to dashboard Cite

Summary Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions. GraphCompass builds upon the Squidpy spatial omics toolbox and encompasses various statistical approaches to perform cross-condition analyses at the level of individual cell types, niches, and samples. Additionally, GraphCompass provides custom visualization functions that enable effective communication of results. We demonstrate how GraphCompass can be used to address key biological questions, such as how cellular organization and tissue architecture differ across various disease states and which spatial patterns correlate with a given pathological condition. GraphCompass can be applied to various popular omics techniques, including, but not limited to, spatial proteomics (e.g. MIBI-TOF), spot-based transcriptomics (e.g. 10× Genomics Visium), and single-cell resolved transcriptomics (e.g. Stereo-seq). In this work, we showcase the capabilities of GraphCompass through its application to three different studies that may also serve as benchmark datasets for further method development. With its easy-to-use implementation, extensive documentation, and comprehensive tutorials, GraphCompass is accessible to biologists with varying levels of computational expertise. By facilitating comparative analyses of cell spatial organization, GraphCompass promises to be a valuable asset in advancing our understanding of tissue function in health and disease.

show abstract

Section: Methodsmentioning

confidence: 99%