ArticleNature communications2022
Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data.
Article in Nature communications, 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.
- SpaDiff denoises sequence-based spatial transcriptomics via diffusion process.Cell reports methods · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Cross-Modal Denoising and Integration of Spatial Multi-Omics Data with CANDIES.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Network models for bridging denoising and identifying spatial domains of spatially resolved transcriptomics.PLoS computational biology · 2026Article
- Randomized Spatial PCA (RASP): A computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.PLoS computational biology · 2025Article
- Spatial gene expression profiling reveals the distinct microenvironment of tuberous sclerosis complex-associated angiomyolipoma.Scientific reports · 2025Article
- Denoising spatially resolved transcriptomics with consistency of heterogeneous spatial coordinates, transcription, and morphology.Briefings in bioinformatics · 2025Article
- Randomized Spatial PCA (RASP): a computationally efficient method for dimensionality reduction of high-resolution spatial transcriptomics data.bioRxiv : the preprint server for biology · 2025Article
- MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning.Genome medicine · 2025Article
- Article
- ReSort enhances reference-based cell type deconvolution for spatial transcriptomics through regional information integration.Bioinformatics advances · 2025Article
- Deciphering progressive lesion areas in breast cancer spatial transcriptomics via TGR-NMF.Briefings in bioinformatics · 2024Article
- Smoother: a unified and modular framework for incorporating structural dependency in spatial omics data.Genome biology · 2023Article
- Article
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3 authors.
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Abstract
Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack of tools to facilitate in silico detection and annotation of regions based on their molecular content. Therefore, we develop a computational tool for detecting molecular regions and region-based Missing value Imputation for Spatially Transcriptomics (MIST). We validate MIST-identified regions across multiple datasets produced by 10x Visium Spatial Transcriptomics, using manually annotated histological images as references. We benchmark MIST against a spatial k-nearest neighboring baseline and other imputation methods designed for single-cell RNA sequencing. We use holdout experiments to demonstrate that MIST accurately recovers spatial transcriptomics missing values. MIST facilitates identifying intra-tissue heterogeneity and recovering spatial gene-gene co-expression signals. Using MIST before downstream analysis thus provides unbiased region detections to facilitate annotations with the associated functional analyses and produces accurately denoised spatial gene expression profiles.
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