文獻分析 Squidpy: a scalable framework for spatial single cell analysis
阿新 • • 發佈：2021-08-20
| 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 |
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Squidpy enables calculation of spatial cellular statistics using spatial graphsneighborhood 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 |
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| 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 overlapbetweendifferentcluterresult | |||
| 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 | ||