emObject: domain specific data abstraction for spatial omics

Baker, Ethan A G; Huang, Mengyao; Lam, A. M.; Rahim, Maha K.; Bieniosek, Matthew F; Wang, B; Zhang, Nancy R.; Mayer, Aaron T.; Trevino, Alexandro E.

doi:10.1101/2023.06.07.543950

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…We present a new generation spatial analysis framework, Giotto Suite, which offers a fully integrated and comprehensive suite of tools that were built to provide end-to-end workflows encompassing every critical stage of working with data generated by the latest spatial technologies. In this manner, Giotto Suite differs significantly from the original Giotto package 21 and provides an all-in-one solution that is otherwise only offered through a combination of multiple recently developed tools [56][57][58][59][60][61] .…”

Section: Discussionmentioning

confidence: 99%

Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem

Chen,

Chávez-Fuentes,

O’Brien

et al. 2023

Preprint

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Emerging spatial omics technologies continue to advance the molecular mapping of tissue architecture and the investigation of gene regulation and cellular crosstalk, which in turn provide new mechanistic insights into a wide range of biological processes and diseases. Such technologies provide an increasingly large amount of information content at multiple spatial scales. However, representing and harmonizing diverse spatial datasets efficiently, including combining multiple modalities or spatial scales in a scalable and flexible manner, remains a substantial challenge. Here, we present Giotto Suite, a suite of open-source software packages that underlies a fully modular and integrated spatial data analysis toolbox. At its core, Giotto Suite is centered around an innovative and technology-agnostic data framework embedded in the R software environment, which allows the representation and integration of virtually any type of spatial omics data at any spatial resolution. In addition, Giotto Suite provides both scalable and extensible end-to-end solutions for data analysis, integration, and visualization. Giotto Suite integrates molecular, morphology, spatial, and annotated feature information to create a responsive and flexible workflow for multi-scale, multi-omic data analyses, as demonstrated here by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive ecosystem for spatial multi-omic data analysis.

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Section: Discussionmentioning

confidence: 99%

Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem

Chen,

Chávez-Fuentes,

O’Brien

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Shown are existing frameworks (rows), classified by their primary target goal (analysis framework versus database), access mode as well as supported data types, operations and interactive visualization capabilities. The following frameworks are considered: Voyager 28 (SpatialFeatureExperiments), SpatialExperiment 29 , Giotto 30 , MoleculeExperiment 31 , SODB 32 , STOmicsDB 33 , emObject 34 , Squidpy 15 . The Giotto 'Points aggregation' is classified as partial because the current implementation is limited to a regular grid as target geometry for aggregation.…”

Section: Reporting Summarymentioning

confidence: 99%

SpatialData: an open and universal data framework for spatial omics

Marconato,

Palla,

Yamauchi

et al. 2024

Nat Methods

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Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.

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“…Datasets used in this manuscript are available at: DLPFC: https://research.libd.org/spatialLIBD/ Lung IMC: https://doi.org/10.5281/zenodo.6376766 DKD Kidney, UCSF Derm, TR Kidney: datasets are available for download and visualization at https://app.enablemedicine.com/portal/atlas-library SCGP was implemented using emObject 59 (https://docs.enablemedicine.com/emobject/) and Annotated data 60 (https://anndata.readthedocs.io/en/latest/). The source code is publicly available at the following URL: https://gitlab.com/enable-medicine-public/scgp…”

Section: Data and Code Availabilitymentioning

confidence: 99%

Characterizing Tissue Structures from Spatial Omics with Spatial Cellular Graph Partition

Wu,

Kondo,

McGrady

et al. 2023

Preprint

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Spatial transcriptomic and proteomic measurements enable high-dimensional characterization of tissues. However, understanding organizations of cells at different spatial scales and extracting tissue structures of interest remain challenging tasks that require extensive human annotations. To address this need for consistent identification of tissue structures, in this work, we present a novel annotation method Spatial Cellular Graph Partitioning (SCGP) that allows unsupervised identification of tissue structures that reflect the anatomical and functional units of human tissues. We further present a reference-query extension pipeline SCGP-Extension that enables the generalization of existing reference tissue structures to previously unseen samples. Our experiments demonstrate reliable and robust partitionings of both spatial transcriptomics and proteomics datasets encompassing different tissue types and profiling techniques. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease and skin disorder, underscoring its potential in facilitating spatial analysis and driving new discoveries.

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emObject: domain specific data abstraction for spatial omics

Cited by 4 publications

References 29 publications

Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem

Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem

SpatialData: an open and universal data framework for spatial omics

Characterizing Tissue Structures from Spatial Omics with Spatial Cellular Graph Partition

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