ArticleBriefings in bioinformatics2022
SPCS: a spatial and pattern combined smoothing method for spatial transcriptomic expression.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Spider: a flexible and unified framework for simulating spatial transcriptomics data.Bioinformatics (Oxford, England) · 2026Article
- SLGCA: spatial cross-level graph contrastive autoencoder for multislice spatial domain identification and microenvironment exploration.Briefings in bioinformatics · 2025Article
- Enhancing Spatial Transcriptomics via Spatially Constrained Matrix Decomposition with EDGES.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- TriCLFF: a multi-modal feature fusion framework using contrastive learning for spatial domain identification.Briefings in bioinformatics · 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
- A comprehensive overview of graph neural network-based approaches to clustering for spatial transcriptomics.Computational and structural biotechnology journal · 2024Review
- Deciphering progressive lesion areas in breast cancer spatial transcriptomics via TGR-NMF.Briefings in bioinformatics · 2024Article
- GraphPCA: a fast and interpretable dimension reduction algorithm for spatial transcriptomics data.Genome biology · 2024Article
- Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma.Genome biology · 2024Article
- Precise detection of cell-type-specific domains in spatial transcriptomics.Cell reports methods · 2024Article
- Progress of single-cell RNA sequencing combined with spatial transcriptomics in tumour microenvironment and treatment of pancreatic cancer.Journal of translational medicine · 2024Review
- EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics.GigaScience · 2024Article
- Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment.Bioscience reports · 2023Review
- SpaGraphCCI: Spatial cell-cell communication inference through GAT-based co-convolutional feature integration.IET systems biologyArticle
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8 authors.
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Abstract
High-dimensional, localized ribonucleic acid (RNA) sequencing is now possible owing to recent developments in spatial transcriptomics (ST). ST is based on highly multiplexed sequence analysis and uses barcodes to match the sequenced reads to their respective tissue locations. ST expression data suffer from high noise and dropout events; however, smoothing techniques have the promise to improve the data interpretability prior to performing downstream analyses. Single-cell RNA sequencing (scRNA-seq) data similarly suffer from these limitations, and smoothing methods developed for scRNA-seq can only utilize associations in transcriptome space (also known as one-factor smoothing methods). Since they do not account for spatial relationships, these one-factor smoothing methods cannot take full advantage of ST data. In this study, we present a novel two-factor smoothing technique, spatial and pattern combined smoothing (SPCS), that employs the k-nearest neighbor (kNN) technique to utilize information from transcriptome and spatial relationships. By performing SPCS on multiple ST slides from pancreatic ductal adenocarcinoma (PDAC), dorsolateral prefrontal cortex (DLPFC) and simulated high-grade serous ovarian cancer (HGSOC) datasets, smoothed ST slides have better separability, partition accuracy and biological interpretability than the ones smoothed by preexisting one-factor methods. Source code of SPCS is provided in Github (https://github.com/Usos/SPCS).
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