Squidpy: a scalable framework for spatial omics analysis

By Giovanni Palla, Hannah Spitzer, Michal Klein, David S. Fischer, Anna C. Schaar, L. B. Kuemmerle, Sergei Rybakov, I. Ibarra, Olle Holmberg, I. Virshup and 3 more. Nature Methods, 2022

911 citations recorded by the source corpus.

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 to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data. Squidpy enables comprehensive analysis and visualization of spatial omics data and image with high efficiency.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e6bbf162df74bef3a74d3bf5d8585db53518c5b8

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In Molecular Biology

Citations in the corpus

Best practices for single-cell analysis across modalities (2023, background): “Tangram 224 , GridNet 230 and eggplant 231 generate common coordinate frameworks across samples to mitigate this issue 232 .”
PyDESeq2: a python package for bulk RNA-seq differential expression analysis (2022, background): “, 2018) or the squidpy package (Palla et al., 2022) for single-cell and spatial RNA-seq, both of which are part of the scverse (Virshup et al.”
DeepST: identifying spatial domains in spatial transcriptomics by deep learning (2022, background): “DeepST presents stronger domain re-gionality and continuity than ground truth (annotated by A. Goeva and E. Macosko (50)), which may result in low ARI values.”
Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, methodology): “22–24).”
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, neutral): “…number of false positives in predicted LR interactions between cell types compared to existing methods that either do not incorporate this information, or do so less optimally, including Squidpy 37 , CellPhoneDB 38 , NATMI 24 , SingleCellSignalR 39 , CellChat 40 , NCEM 41 , SpaTalk 42 and spaOTsc.”
Methods and applications for single-cell and spatial multi-omics (2023, background): “The expansion of these models to integrate paired, unpaired and multimodal spatial assays will require robust data standards 160,161 for scalable analysis of multimodal data in addition to well-established benchmarks to evaluate their efficacy.”
An introduction to spatial transcriptomics for biomedical research (2022, background): “Third, they provide functions for data visualization, combining spatial transcriptomic data with overlays such as microscopy data [81, 83].”