ArticleGigaScience2024
EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics.
Article in GigaScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Spatial transcriptomics reveal developmental dynamics of the human cerebral cortex and striatum.Science China. Life sciences · 2026Article
- Article
- 4D single-cell spatial transcriptomics reveals dynamic morphogenetic gradients and regenerative domains in planarians.GigaScience · 2026Article
- CSRefiner: a lightweight framework for fine-tuning cell segmentation models with small datasets.Briefings in bioinformatics · 2026Article
- Poly pipeline: a polyvalent spatial transcriptomics workflow validated across polyploid and diploid organisms.GigaByte (Hong Kong, China) · 2026Article
- Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression Prediction from Whole Slide Histopathology Images.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- STMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model.Briefings in bioinformatics · 2024Article
- Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder.Briefings in bioinformatics · 2024Article
- EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics.GigaScience · 2024Article
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Authors and funding
8 authors.
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Abstract
backgroundThe emergence of high-resolved spatial transcriptomics (ST) has facilitated the research of novel methods to investigate biological development, organism growth, and other complex biological processes. However, high-resolved and whole transcriptomics ST datasets require customized imputation methods to improve the signal-to-noise ratio and the data quality.
findingsWe propose an efficient and adaptive Gaussian smoothing (EAGS) imputation method for high-resolved ST. The adaptive 2-factor smoothing of EAGS creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, and then utilizes the weights to restore the gene expression profiles. We assessed the performance and efficiency of EAGS using simulated and high-resolved ST datasets of mouse brain and olfactory bulb.
conclusionsCompared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.
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