ArticleNature communications2024
DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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
13 citing papers in PubMed.
- Microenvironment-aware transcriptome reconstruction in spatial transcriptomics.Nature communications · 2026Article
- Transposable elements and homotypic niches drive immune dynamics and resistance in melanoma epigenetic-based immunotherapy.Science advances · 2026Article
- AddaGCN: Spatial transcriptomics deconvolution using graph convolutional networks with adversarial discriminative domain adaptation.PLoS computational biology · 2026Article
- A multimodal interrogation of Broca's area in the pediatric and adult human brain.bioRxiv : the preprint server for biology · 2026Article
- A stretch-responsive fibroblast program promotes epidermal stem cell self-renewal during skin expansion.Nature communications · 2026Article
- Topography-aware optimal transport for alignment of spatial omics data.Cell reports methods · 2026Article
- SpaJoint: a transfer learning method for spatial transcriptomics deconvolution.Briefings in bioinformatics · 2026Article
- Cell-type deconvolution methods for spatial transcriptomics.Nature reviews. Genetics · 2025Review
- Location and aetiology are determinants of fibroblast activation and heterogeneity in the failing human heart.Genome medicine · 2025Article
- Quantitative characterization of tissue states using multiomics and ecological spatial analysis.Nature genetics · 2025Article
- COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.Bioinformatics (Oxford, England) · 2025Article
- Single-cell genomics and spatial transcriptomics in islet transplantation for diabetes treatment: advancing towards personalized therapies.Frontiers in immunology · 2025Review
- DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics.Nature communications · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single-cell transcriptomics and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. However, they suffer from lack of spatial information and a trade-off of resolution and gene coverage, respectively. We propose DOT, a multi-objective optimization framework for transferring cellular features across these data modalities, thus integrating their complementary information. DOT uses genes beyond those common to the data modalities, exploits the local spatial context, transfers spatial features beyond cell-type information, and infers absolute/relative abundance of cell populations at tissue locations. Thus, DOT bridges single-cell transcriptomics data with both high- and low-resolution spatially-resolved data. Moreover, DOT combines practical aspects related to cell composition, heterogeneity, technical effects, and integration of prior knowledge. Our fast implementation based on the Frank-Wolfe algorithm achieves state-of-the-art or improved performance in localizing cell features in high- and low-resolution spatial data and estimating the expression of unmeasured genes in low-coverage spatial data.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.