bioRxivBioinformaticsPreprint
Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem
Jiaji George CheniD, Joselyn CháveziD, Matthew O’Brien, Junxiang XuiD, Edward C. RuiziD, Wen WangiD, Iqra Amin, Irzam SarfraziD, Pratishtha GuckhooliD, Adriana SistigiD, Guo‐Cheng YuaniD, Ruben DriesiD
- Citations
- 21
Abstract
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.
- assay: spatial transcriptomics
- research focus: benchmarking
- research focus: database / resource
- research focus: software / tool
- approach: statistical model
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