ArticleNature methods2025
SpotSweeper: spatially aware quality control for spatial transcriptomics.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Unlocking the full potential of spatial omics in plants: practical challenges, solutions, and a path forward.The Plant cell · 2026Review
- Article
- Molecular programs in human locus coeruleus link APOE and neuromelanin to Alzheimer's vulnerability.Acta neuropathologica · 2026Article
- Multi-stromal organoid co-culture modelling reveals epithelial-fibroblast heterogeneity in pancreatic cancer and pancreatitis.Nature cell biology · 2026Article
- Amplification bias in sequencing-based spatial transcriptomics: sources, mechanisms, impacts, and mitigation strategies.Briefings in bioinformatics · 2026Review
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.Neuron · 2026Article
- Review
- SpatialArtifacts: a computational framework for tissue artifact detection in spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Spatially resolved molecular sex differences at single-cell resolution in the adult human ventromedial and arcuate hypothalamus.Cell reports · 2026Article
- Mapping spatially organized molecular and genetic signatures of schizophrenia across multiple scales in human prefrontal cortex.bioRxiv : the preprint server for biology · 2026Article
- Advances in Spatial Transcriptomics in Bone.Current osteoporosis reports · 2026Review
- From Raw Data to Biological Insights: A Practical Guide for Spatial Transcriptomics Analysis in R and Python.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Spatial multiomics to inform immunocytokine engineering: knowledge base, gaps, and QC solutions.Frontiers in immunology · 2026Review
- Pathogenomic analysis reveals clinically relevant epithelial-mesenchymal plasticity in esophageal squamous cell carcinoma.Theranostics · 2026Article
- Addressing the mean-variance relationship in spatially resolved transcriptomics data with spoon.Biostatistics (Oxford, England) · 2025Article
- Spotsweeper-py: spatially-aware quality control metrics for spatial omics data in the Python ecosystem.bioRxiv : the preprint server for biology · 2025Article
- Article
- Impact of Alzheimer's disease risk factors and local neuromelanin content on the transcriptomic landscape of the human locus coeruleus.bioRxiv : the preprint server for biology · 2025Article
- Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.bioRxiv : the preprint server for biology · 2025Article
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Abstract
Quality control (QC) is a crucial step to ensure the reliability of data obtained from RNA sequencing experiments, including spatially resolved transcriptomics (SRT). Existing QC approaches for SRT that have been adopted from single-cell or single-nucleus RNA sequencing methods are confounded by spatial biology and are inappropriate for SRT data. In addition, no methods currently exist for identifying histological tissue artifacts that are unique to SRT. Here, we introduce SpotSweeper, a spatially aware QC method that leverages local neighborhoods to correct for spatial confounding in order to identify both local outliers and regional artifacts in SRT. Using SpotSweeper on publicly available data, we identify a consistent set of Visium barcoded spots as systematically low quality and demonstrate that SpotSweeper accurately identifies two distinct types of regional artifacts. SpotSweeper represents a substantial advancement in spatially resolved transcriptomics QC for SRT, providing a robust, generalizable framework to ensure data reliability across diverse experimental conditions and technologies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.