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

 

a neighborhood graph from spatial coordinates

large source images ：  Image Container

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

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

 

 

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