ReviewGenomics, proteomics & bioinformatics2023
Computational Approaches and Challenges in Spatial Transcriptomics.
Review in Genomics, proteomics & bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
86 citing papers in PubMed.
- STAT3D as a new image-tailored Xenium-based spatial transcriptomics tool for accurately dissecting the tumor microenvironment.Nucleic acids research · 2026Article
- OT-knn: A neighborhood-aware optimal transport framework for aligning spatial transcriptomics data.PLoS computational biology · 2026Article
- Spatial cell-cell communication inference based on cell-spot-ligand-receptor heterogeneous graphs.PLoS computational biology · 2026Article
- spMosaic: multi-sample integration for scalable spatial domain discovery based on spatial transcriptomics.Briefings in bioinformatics · 2026Article
- STWave: Fine-Scale Spatial Structure Discovery in Microscopic-Resolution Spatial Transcriptomics via Patchwise Wavelet Graphs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- DeSpaST: deconvoluting spatial transcriptomics signals to cell-level resolution using histology images.Briefings in bioinformatics · 2026Article
- SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.BMC bioinformatics · 2026Article
- Review
- AGCLD: an adaptive graph contrastive learning method with denoising for spatial domain identification.Briefings in bioinformatics · 2026Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Tumor microenvironment-specific nanomedicine: from biology-driven to multi-omics-guided precision engineering.Journal of hematology & oncology · 2026Review
- Wheat's war against stripe rust: Integrating host immunity, genomics and breeding for durable resistance.The plant genome · 2026Review
- SECTOR: structural entropy-based learning of spatiotemporal organisation in spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- transFusion: a novel comprehensive platform for integration analysis of single-cell and spatial transcriptomics.Bioinformatics (Oxford, England) · 2026Article
- Impact and correction of segmentation errors in spatial transcriptomics.Nature genetics · 2026Article
- CELLama: Foundation Model for Single Cell and Spatial Transcriptomics by Cell Embedding Leveraging Language Model Abilities.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multimodal foundation transformer models for multiscale genomics.Nature methods · 2026Review
- Advances in the pathophysiological study of brain development: application of cerebral organoid combined with Spatial omics technology.Stem cell research & therapy · 2026Review
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.
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Registered trials
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.