ArticleBioinformatics (Oxford, England)2022
MOJITOO: a fast and universal method for integration of multimodal single-cell data.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- scURL: uncertainty-sharpened representation learning for single-cell multi-omics clustering.Bioinformatics (Oxford, England) · 2026Article
- The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics.Nature cell biology · 2026Article
- Scalable joint non-negative matrix factorization for paired single cell gene expression and chromatin accessibility data.NAR genomics and bioinformatics · 2026Article
- Interpretable modality-aware mapping of gene regulation in single-cell multiomics with scMAGCA.Nature communications · 2026Article
- Multimodal-based analysis of single-cell ATAC-seq data enables highly accurate delineation of clinically relevant tumor cell subpopulations.Genome medicine · 2026Article
- Article
- Multitask benchmarking of single-cell multimodal omics integration methods.Nature methods · 2025Article
- PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories.Nature methods · 2025Article
- Multi-omics single-cell data alignment and integration with enhanced contrastive learning and differential attention mechanism.Bioinformatics (Oxford, England) · 2025Article
- Ocelli: an open-source tool for the analysis and visualization of developmental multimodal single-cell data.NAR genomics and bioinformatics · 2025Article
- Empowering integrative and collaborative exploration of single-cell and spatial multimodal data with SGS genome browser.Cell genomics · 2025Article
- AnnoGCD: a generalized category discovery framework for automatic cell type annotation.NAR genomics and bioinformatics · 2024Article
- TriTan: an efficient triple nonnegative matrix factorization method for integrative analysis of single-cell multiomics data.Briefings in bioinformatics · 2024Article
- Benchmarking algorithms for single-cell multi-omics prediction and integration.Nature methods · 2024Article
- Liam tackles complex multimodal single-cell data integration challenges.Nucleic acids research · 2024Article
- scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference.Bioinformatics advances · 2023Article
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3 authors.
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Abstract
motivationThe advent of multi-modal single-cell sequencing techniques have shed new light on molecular mechanisms by simultaneously inspecting transcriptomes, epigenomes and proteomes of the same cell. However, to date, the existing computational approaches for integration of multimodal single-cell data are either computationally expensive, require the delineation of parameters or can only be applied to particular modalities.
resultsHere we present a single-cell multi-modal integration method, named Multi-mOdal Joint IntegraTion of cOmpOnents (MOJITOO). MOJITOO uses canonical correlation analysis for a fast and parameter free detection of a shared representation of cells from multimodal single-cell data. Moreover, estimated canonical components can be used for interpretation, i.e. association of modality-specific molecular features with the latent space. We evaluate MOJITOO using bi- and tri-modal single-cell datasets and show that MOJITOO outperforms existing methods regarding computational requirements, preservation of original latent spaces and clustering. AVAILABILITY AND IMPLEMENTATION: The software, code and data for benchmarking are available at https://github.com/CostaLab/MOJITOO and https://doi.org/10.5281/zenodo.6348128. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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