Dataset Information

NeST: nested hierarchical structure identification in spatial transcriptomic data.

ABSTRACT: Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial structure through coexpression hotspots-regions exhibiting localized spatial coexpression of some set of genes. Coexpression hotspots identify structure on any spatial scale, over any possible subset of genes, and are highly explainable. NeST also performs spatial analysis of cell-cell interactions via ligand-receptor, identifying active areas de novo without restriction of cell type or other groupings, in both two and three dimensions. Through application on ST datasets of varying type and resolution, we demonstrate the ability of NeST to reveal a new level of biological structure.

SUBMITTER: Walker BL

PROVIDER: S-EPMC10582109 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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NeST: nested hierarchical structure identification in spatial transcriptomic data.

Walker Benjamin L BL Nie Qing Q

Nature communications 20231017 1

Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial st ...[more]

PMID: 37848426

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Project description:A major challenge in community ecology is to understand the underlying factors driving metacommunity (i.e., a set of local communities connected through species dispersal) dynamics. However, little is known about the effects of varying spatial scale on the relative importance of environmental and spatial (i.e., dispersal related) factors in shaping metacommunities and on the relevance of different dispersal pathways. Using a hierarchy of insect metacommunities at three spatial scales (a small, within-stream scale, intermediate, among-stream scale, and large, among-sub-basin scale), we assessed whether the relative importance of environmental and spatial factors shaping metacommunity structure varies predictably across spatial scales, and tested how the importance of different dispersal routes vary across spatial scales. We also studied if different dispersal ability groups differ in the balance between environmental and spatial control. Variation partitioning showed that environmental factors relative to spatial factors were more important for community composition at the within-stream scale. In contrast, spatial factors (i.e., eigenvectors from Moran's eigenvector maps) relative to environmental factors were more important at the among-sub-basin scale. These results indicate that environmental filtering is likely to be more important at the smallest scale with highest connectivity, while dispersal limitation seems to be more important at the largest scale with lowest connectivity. Community variation at the among-stream and among-sub-basin scales were strongly explained by geographical and topographical distances, indicating that overland pathways might be the main dispersal route at the larger scales among more isolated sites. The relative effect of environmental and spatial factors on insect communities varied between low and high dispersal ability groups; this variation was inconsistent among three hierarchical scales. In sum, our study indicates that spatial scale, connectivity, and dispersal ability jointly shape stream metacommunities.

Project description:BackgroundSpatial resolved transcriptomics (SRT) encompasses a rapidly developing set of technologies that enable the measurement of gene expression in tissue while retaining spatial localization information. SRT technologies and the enabled SRT studies have provided unprecedent insights into the structural and functional underpinnings of complex tissues. As SRT technologies have advanced and an increasing number of SRT studies have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the analysis and interpretation of SRT data. However, despite the growing popularity of SRT studies and the widespread availability of SRT analysis methods, analysis of large-scale and complex SRT datasets remains challenging and not easily accessible to researchers with limited statistical and computational backgrounds.ResultsHere, we present SRT-Server, the first webserver designed to carry out comprehensive SRT analyses for a wide variety of SRT technologies while requiring minimal prior computational knowledge. Implemented with cutting-edge web development technologies, SRT-Server is user-friendly and features multiple analytic modules that can perform a range of SRT analyses. With a flowchart-style interface, these different analytic modules on the SRT-Server can be dragged into the main panel and connected to each other to create custom analytic pipelines. SRT-Server then automatically executes the desired analyses, generates corresponding figures, and outputs results-all without requiring prior programming knowledge. We demonstrate the advantages of SRT-Server through three case studies utilizing SRT data collected from two common platforms, highlighting its versatility and values to researchers with varying analytic expertise.ConclusionsOverall, SRT-Server presents a user-friendly, efficient, effective, secure, and expandable solution for SRT data analysis, opening new doors for researchers in the field. SRT-Server is freely available at https://spatialtranscriptomicsanalysis.com/ .

Dataset Information

NeST: nested hierarchical structure identification in spatial transcriptomic data.

Publications

NeST: nested hierarchical structure identification in spatial transcriptomic data.

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