ReviewNature methods2023
Principles and challenges of modeling temporal and spatial omics data.
Review in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
54 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
- Deep learning in single-cell and spatial transcriptomics data analysis: advances and challenges from a data science perspective.Briefings in bioinformatics · 2025Pooled it
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.Nature methods · 2026Article
- Weighted sliced inverse regression for scalable supervised dimensionality reduction of spatial transcriptomics data.Nature communications · 2026Article
- lagCI enables inference of temporal causal relationships from dense multi-omic time series.Briefings in bioinformatics · 2026Article
- SpatioTemporal Omics Consortium: a global effort for biological discovery across species, space and time.Nature methods · 2026Article
- Article
- Computational analysis in spatial transcriptomics: methods and perspectives.Briefings in bioinformatics · 2026Review
- Review
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- Spatially multimodal and multiscale network for representation learning from spatial multi-omics.BMC genomics · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- SpaPheno: linking spatial transcriptomics to clinical phenotypes with interpretable machine learning.Genome medicine · 2026Article
- MINGL Quantifies Borders, Gradients, and Heterogeneity in Multicellular Tissue Organization.bioRxiv : the preprint server for biology · 2026Article
- Spatial architecture of development and disease.Nature reviews. Genetics · 2026Review
- Advances in the pathophysiological study of brain development: application of cerebral organoid combined with Spatial omics technology.Stem cell research & therapy · 2026Review
- Spatiotemporal decoding of skin biology: development, aging, disease, and regeneration.Burns & trauma · 2026Article
- Spatial multiomics to inform immunocytokine engineering: knowledge base, gaps, and QC solutions.Frontiers in immunology · 2026Review
- Dynamic graph learning framework based seasonal and trend decomposition approach for potato crop evapotranspiration prediction.Scientific reports · 2025Article
- Spatial omics: applications and utility in profiling the tumor microenvironment.Cancer metastasis reviews · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.
Identifiers
37710019What Socratic holds
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 Socratic graph.