Computational workflows and data infrastructures for spatial omics analysis
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Computational workflows and data infrastructures for spatial omics analysis

Margaret Alexander
1,2,3
,
Yutian Liu
1,2
,
Felipe Segato Dezem
1,2
,
Hannah Chasteen
1,2
,
Jasmine Plummer
1,2,4,*
*Correspondence to: Jasmine Plummer, Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA. E-mail: jasmine.plummer@stjude.org
EXO. 2026;1:202607. 10.70401/EXO.2026.0010
Received: February 11, 2026Accepted: May 13, 2026Published: May 15, 2026
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This manuscript is made available in its unedited form to allow early access to the reported findings. Further editing will be completed before final publication. As such, the content may include errors, and standard legal disclaimers are applicable.

Abstract

Spatial omics is a broad term referring to technologies that allow for biomolecules to be observed within their native tissue context. These technologies have been used by biomedical researchers to gain a better understanding of cellular interactions, tumor microenvironment dynamics, and immune cell infiltration. While the basic outputs, such as spatial coordinates, segmentation masks, and transcript/protein matrices, are provided by the instrument software, the true biological insights come from several downstream, specialized analysis steps. Since spatial omics remains a relatively new field, no unified analysis pipeline has yet been established to encompass all platforms. Most workflows are adapted from single-cell RNA sequencing analysis frameworks, while incorporating additional steps that are specific to spatial data, especially for imaging-based technologies. At the same time, the diversity of platforms, data modalities, and output formats has introduced substantial challenges for data representation, interoperability, and cross-platform integration, highlighting the need for flexible, spatially aware, and user-friendly data structures made specifically for imaging-based data not merely adapted from other methods. This review summarizes the general analytical steps following spatial omics data acquisition, commonly used data infrastructures and tools, existing gaps, and future directions in the field.

Keywords

Spatial omics, data analysis, computation

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Alexander M, Liu Y, Dezem FS, Chasteen H, Plummer J. Computational workflows and data infrastructures for spatial omics analysis. EXO. 2026;1:202607. https://doi.org/10.70401/EXO.2026.0010

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