Smoothie: Efficient Inference of Spatial Co-expression Networks from Denoised Spatial Transcriptomics Data
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| Veröffentlicht in: | bioRxiv (Mar 2, 2025) |
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Cold Spring Harbor Laboratory Press
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| 001 | 3172859094 | ||
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| 022 | |a 2692-8205 | ||
| 024 | 7 | |a 10.1101/2025.02.26.640406 |2 doi | |
| 035 | |a 3172859094 | ||
| 045 | 0 | |b d20250302 | |
| 100 | 1 | |a Chase Holdener | |
| 245 | 1 | |a Smoothie: Efficient Inference of Spatial Co-expression Networks from Denoised Spatial Transcriptomics Data | |
| 260 | |b Cold Spring Harbor Laboratory Press |c Mar 2, 2025 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Finding correlations in spatial gene expression is fundamental in spatial transcriptomics, as co-expressed genes within a tissue are linked by regulation, function, pathway, or cell type. Yet, sparsity and noise in spatial transcriptomics data pose significant analytical challenges. Here, we introduce Smoothie, a method that denoises spatial transcriptomics data with Gaussian smoothing and constructs and integrates genome-wide co-expression networks. Utilizing implicit and explicit parallelization, Smoothie scales to datasets exceeding 100 million spatially resolved spots with fast run times and low memory usage. We demonstrate how co-expression networks measured by Smoothie enable precise gene module detection, functional annotation of uncharacterized genes, linkage of gene expression to genome architecture, and multi-sample comparisons to assess stable or dynamic gene expression patterns across tissues, conditions, and time points. Overall, Smoothie provides a scalable and versatile framework for extracting deep biological insights from high-resolution spatial transcriptomics data.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://doi.org/10.5281/zenodo.14933147 | |
| 653 | |a Gene expression | ||
| 653 | |a Genomes | ||
| 653 | |a Transcriptomics | ||
| 700 | 1 | |a Iwijn De Vlaminck | |
| 773 | 0 | |t bioRxiv |g (Mar 2, 2025) | |
| 786 | 0 | |d ProQuest |t Biological Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3172859094/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u https://www.biorxiv.org/content/10.1101/2025.02.26.640406v1 |