2022
DOI: 10.1038/s41592-021-01358-2
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Squidpy: a scalable framework for spatial omics analysis

Abstract: Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow… Show more

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Cited by 443 publications
(470 citation statements)
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References 59 publications
(98 reference statements)
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“…Given the relevance of scRNA-seq to spatial data, and how spatial data are often analyzed like scRNA-seq data in exploratory data analysis (EDA), popular scRNA-seq EDA ecosystems, such as Seurat 32 , SCANPY (which spatical single-cell analysis in Python (Squidpy) is built on) 117 , and SingleCellExperiment (extended by SpatialExperiment) 118 , have added functionalities for spatial data, such as updates to data containers and functions to facilitate visualization of gene expression and cell or spot metadata at spatial locations (Section 7.2 of Supplementary Information). EDA packages dedicated to spatial data with beautiful graphics and good documentation have also been written, such as Giotto 119 and STUtility 120 .…”
Section: Discussionmentioning
confidence: 99%
“…Given the relevance of scRNA-seq to spatial data, and how spatial data are often analyzed like scRNA-seq data in exploratory data analysis (EDA), popular scRNA-seq EDA ecosystems, such as Seurat 32 , SCANPY (which spatical single-cell analysis in Python (Squidpy) is built on) 117 , and SingleCellExperiment (extended by SpatialExperiment) 118 , have added functionalities for spatial data, such as updates to data containers and functions to facilitate visualization of gene expression and cell or spot metadata at spatial locations (Section 7.2 of Supplementary Information). EDA packages dedicated to spatial data with beautiful graphics and good documentation have also been written, such as Giotto 119 and STUtility 120 .…”
Section: Discussionmentioning
confidence: 99%
“…Alongside experimental development, computational frameworks have been developed in order to analyze these new data sets [11][12][13][14][15][16][17] . However, it still remains challenging to accurately recover spatial domains especially when these domains contain a multitude of cell types.…”
Section: Discussionmentioning
confidence: 99%
“…The recovery of anatomical structures containing multiple cell types becomes extremely arduous and generally has relied on manual isolation. One solution to this challenge is to link anatomical territories from companion hematoxylin and eosin (H&E) staining images to their spatial transcriptomic assay [14][15][16][17][18] . However, techniques such as Slide-Seq 9,10 do not provide these companion images.…”
Section: Main Textmentioning
confidence: 99%
“…gr . co_occurrence function (Palla et al 2022). Briefly, the ratio describes the normalized probability of locating a cell type i in a radius of size d , conditioned on the existence of a cell type j : …”
Section: Supplementary Textmentioning
confidence: 99%