ArticleNature communications2023
Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 80 papers.
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Who cites it
80 citing papers in PubMed.
- Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data.Nature communications · 2026Article
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection.Bioinformatics (Oxford, England) · 2026Article
- Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.Neuron · 2026Article
- Spatio-molecular gene expression reflects dorsal anterior cingulate cortex structure and function in the human brain.Cell reports · 2026Article
- Atlas-Level Single-Cell and Spatial Transcriptomics Data Integration via PRIME.bioRxiv : the preprint server for biology · 2026Article
- STGAT: spatial domain identification of consecutive slices based on graph contrastive learning.Briefings in bioinformatics · 2026Article
- Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.Nature genetics · 2026Article
- Mapping spatially organized molecular and genetic signatures of schizophrenia across multiple scales in human prefrontal cortex.bioRxiv : the preprint server for biology · 2026Article
- Multimodal analyses of early, untreated systemic sclerosis skin identify a proinflammatory vascular niche of macrophage-fibroblast signaling.JCI insight · 2026Article
- JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- The interpretable multimodal dimension reduction framework SpaHDmap enhances resolution in spatial transcriptomics.Nature cell biology · 2026Article
- Spatial transcriptomics reveals brain-wide circadian disruption in an Alzheimer's disease model.bioRxiv : the preprint server for biology · 2026Article
- Spatial omics of neuroinflammation: insights across brain diseases.Frontiers in immunology · 2026Review
- Spatial transcriptomic alignment, integration, and 3D reconstruction by STAIR.Genome biology · 2025Article
- Vector-guided graph learning for spatial multi-slice multi-omics alignment.Cell reports methods · 2025Article
- BatchSVG: identifying batch-biased genes in the application of spatially variable gene detection.bioRxiv : the preprint server for biology · 2025Article
- Unified integration of spatial transcriptomics across platforms with LLOKI.Genome research · 2025Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them are for single-cell RNA-seq datasets without consideration of spatial information. Thus, methods that can integrate spatial transcriptomics data from multiple tissue slides, possibly from multiple individuals, are needed. Here, we present PRECAST, a data integration method for multiple spatial transcriptomics datasets with complex batch effects and/or biological effects between slides. PRECAST unifies spatial factor analysis simultaneously with spatial clustering and embedding alignment, while requiring only partially shared cell/domain clusters across datasets. Using both simulated and four real datasets, we show improved cell/domain detection with outstanding visualization, and the estimated aligned embeddings and cell/domain labels facilitate many downstream analyses. We demonstrate that PRECAST is computationally scalable and applicable to spatial transcriptomics datasets from different platforms.
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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 OpenQuestion graph.