ArticleNature methods2022
Sprod for de-noising spatially resolved transcriptomics data based on position and image information.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- SpaDiff denoises sequence-based spatial transcriptomics via diffusion process.Cell reports methods · 2026Article
- Amplification bias in sequencing-based spatial transcriptomics: sources, mechanisms, impacts, and mitigation strategies.Briefings in bioinformatics · 2026Review
- ViMST: vision transformer-based dual modality multi-task graph contrastive network for spatial transcriptomics microenvironments investigation.BMC biology · 2026Article
- Macrophage signaling associates with fibrogenic program activation in periductal fibroblasts in pediatric primary sclerosing cholangitis.JCI insight · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE.bioRxiv : the preprint server for biology · 2026Article
- MicNet: integrating spatially resolved transcriptomes and pathology images by contrastive deep neural network.Genome biology · 2026Article
- Accurate, scalable and cross-platform cell identification for high-resolution spatial transcriptomics.Nature genetics · 2026Article
- MultiSP deciphers tissue structure and multicellular communication from spatial multi-omics data.Cell genomics · 2026Article
- Spatial Mapping of Single Cells via Correlation and Importance Between Cells and Spots.Interdisciplinary sciences, computational life sciences · 2026Article
- spRefine denoises and imputes spatial transcriptomic data with a reference-free framework powered by genomic language model.Genome research · 2026Article
- MetaScreener: a robust dual-mode framework for directional prioritization of actionable signatures through multi-dataset and multi-approach integration.Journal of translational medicine · 2026Article
- Spatial transcriptomics: challenges and future directions in musculoskeletal diseases.Current opinion in rheumatology · 2026Review
- Uncovering the signatures of aging and senescence in the human dorsolateral prefrontal cortex.Cell genomics · 2026Article
- CD8+ T cells in the tumor microenvironment modulate the response to endocrine therapy in breast cancer.The Journal of clinical investigation · 2026Article
- Advancing spatial cellular communication inference with ligand diffusion and transport model.Communications biology · 2026Article
- Signal-based spatial domain identification of spatially resolved transcriptomics with multigraph fusion.Briefings in bioinformatics · 2026Article
- Spider: a flexible and unified framework for simulating spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- Spatially varying gene regulation network inference from spatial transcriptomics.Bioinformatics advances · 2026Article
- Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics.PLoS computational biology · 2026Article
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Authors and funding
8 authors.
Funding
Abstract
Spatially resolved transcriptomics (SRT) provide gene expression close to, or even superior to, single-cell resolution while retaining the physical locations of sequencing and often also providing matched pathology images. However, SRT expression data suffer from high noise levels, due to the shallow coverage in each sequencing unit and the extra experimental steps required to preserve the locations of sequencing. Fortunately, such noise can be removed by leveraging information from the physical locations of sequencing, and the tissue organization reflected in corresponding pathology images. In this work, we developed Sprod, based on latent graph learning of matched location and imaging data, to impute accurate SRT gene expression. We validated Sprod comprehensively and demonstrated its advantages over previous methods for removing drop-outs in single-cell RNA-sequencing data. We showed that, after imputation by Sprod, differential expression analyses, pathway enrichment and cell-to-cell interaction inferences are more accurate. Overall, we envision de-noising by Sprod to become a key first step towards empowering SRT technologies for biomedical discoveries.
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