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Prograss
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Challenge
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demand
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background
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Dissociation-based single cell technologies
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cellular diversity constitutes tissue organization
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Spatially-resolved molecular technologies
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acquire data in greatly diverse forms
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development of interoperable and broad analysis methods;
solutions both in terms of efficient data representation as well as comprehensive analysis and visualization methods
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existing analysis frameworks
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lack of a unified data representation and modular API
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community-driven scalable analysis of both spatial neighborhood graph and image, along with an interactive visualization module
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solve
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what
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how
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effect
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Squidpy, a Python framework ( Spatial Quantification of Molecular Data in Python)
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brings together tools from omics and image analysis;
built on top of Scanpy and Anndata
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scalable description of spatial molecular data
store + manipulate + interactively
a common data representation
a common set of analysis and interactive visualization tools
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result
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Squidpy provides technology-agnostic data representations for spatial graphs and images
a neighborhood graph from spatial coordinates
large source images ： Image Container
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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
 
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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.
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Ripley’s K function
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average clustering
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degree and closeness centrality
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Squidpy allows analysis of images in spatial omics analysis workflows
an example of segmentation-based features
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feature extraction pipeline enables direct comparison and joint analysis of image data and omics data
overlap between different cluter result
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Conclusion& Discussion
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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
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