ArticleNature biotechnology2023
TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics.
Article in Nature biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed.
- Article
- Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus.Nature methods · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.Research square · 2026Article
- Φ-Space ST: A platform-agnostic method to identify cell states in spatial transcriptomics studies.Cell reports methods · 2026Article
- Enhancing pan-cancer spatial transcriptomics at single-cell resolution with stPainter.Nature communications · 2026Article
- Binary-SPA: a reference-free method for cell annotation in high-resolution spatial transcriptomics.Nucleic acids research · 2026Article
- Uncovering ectopic GC-like niches for tumor reactive lymphocyte priming in lung adenocarcinoma using Stereo-XCR-seq.Nature communications · 2026Article
- Macrophage FOLR2 drives liver fibrosis via intercellular engagement of TGFβRII.Acta pharmacologica Sinica · 2026Article
- CTMAP: an adversarial cross-modal learning framework for accurate and robust cell-type annotation in single-cell resolution spatial transcriptomics.Briefings in bioinformatics · 2026Article
- Benchmarking cell type annotation in spatial transcriptomics: resolving cellular hierarchies, biological fidelity, and dynamic cell states.bioRxiv : the preprint server for biology · 2026Article
- Molecular cues from distinct neuron classes drive differential myelination in the neocortex.Developmental cell · 2026Article
- Review
- GraphTME: graph-based framework for predicting immunotherapy response by interpreting tumour microenvironment interactions using spatial transcriptomics.NPJ systems biology and applications · 2026Article
- SOFisher: reinforcement learning-guided experiment designs for spatial omics.Nature communications · 2026Article
- δ-catenin controls layer-specific transcriptional maturation of astrocytes via Zbtb20.bioRxiv : the preprint server for biology · 2026Article
- Scalable discovery of spatial multicellular patterns via neighborhood-to-sequence transformation.Communications biology · 2026Article
- Spatial Chromatin Accessibility Analysis of Intratumor Heterogeneity in Breast Cancer.Genomics, proteomics & bioinformatics · 2026Article
- Binary-SPA: A Reference-Free Method for Cell Annotation in High-Resolution Spatial Transcriptomics.bioRxiv : the preprint server for biology · 2026Article
- Defining subcellular synovial responses in TMJ osteoarthritis onset via mechanical stress and articular disk derangement models.International journal of oral science · 2026Article
- stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.Innovation (Cambridge (Mass.)) · 2026Article
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10 authors.
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Abstract
Transferring annotations of single-cell-, spatial- and multi-omics data is often challenging owing both to technical limitations, such as low spatial resolution or high dropout fraction, and to biological variations, such as continuous spectra of cell states. Based on the concept that these data are often best described as continuous mixtures of cells or molecules, we present a computational framework for the transfer of annotations to cells and their combinations (TACCO), which consists of an optimal transport model extended with different wrappers to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatiomolecular tissue structure at the cell and molecular level and resolve differentiation trajectories using synthetic and biological datasets. While matching or exceeding the accuracy of specialized tools for the individual tasks, TACCO reduces the computational requirements by up to an order of magnitude and scales to larger datasets (for example, considering the runtime of annotation transfer for 1 M simulated dropout observations).
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