文献分析 Squidpy: a scalable framework for spatial single cell analysis

 

	Prograss	Challenge	demand
background	Dissociation-based single cell technologies	cellular diversity constitutes tissue organization
	Spatially-resolved molecular technologies	acquire data in greatly diverse forms	development of interoperable and broad analysis methods; solutions both in terms of efficient data representation as well as comprehensive analysis and visualization methods
	existing analysis frameworks	lack of a unified data representation and modular API	community-driven scalable analysis of both spatial neighborhood graph and image, along with an interactive visualization module
solve	what	how	effect
	Squidpy, a Python framework ( Spatial Quantification of Molecular Data in Python)	brings together tools from omics and image analysis; built on top of Scanpy and Anndata	scalable description of spatial molecular data store + manipulate + interactively a common data representation a common set of analysis and interactive visualization tools
result	Squidpy provides technology-agnostic data representations for spatial graphs and images a neighborhood graph from spatial coordinates large source images ：  Image Container
	Squidpy enables calculation of spatial cellular statistics using spatial graphs neighborhood enrichment analysis ：  cluster is co-enriched several clusters to be co-enriched in their cellular neighbors --------------------------------------------------------------------------------------------------------------- computes a co-occurrence score for clusters ：  subcellular measurements    The cluster “Nucleolus” is found to be co-enriched at short distances with the “Nucleus” and the “Nuclear envelope” clusters. a fast and broader implementation of CellPhoneDB Ligand-receptor interactions from the cluster “Hippocampus” to clusters “Pyramidal Layer” and “Pyramidal layer dentate gyrus”. Shown are a subset of significant ligand-receptor pairs queried using Omnipath database. ------------ Ripley’s K function ---------- average clustering ---------- degree and closeness centrality
	Squidpy allows analysis of images in spatial omics analysis workflows an example of segmentation-based features -------------------------------------------------------------------------------------------------------------------------------------- feature extraction pipeline enables direct comparison and joint analysis of image data and omics data overlap between different cluter result
Conclusion& Discussion	Squidpy could contribute to building a bridge between the molecular omics community and the image analysis and computer vision community to develop the next generation of computational methods for spatial omics technologies