ReviewGenomics, proteomics & bioinformatics2022
Computational Methods for Single-cell Multi-omics Integration and Alignment.
Review in Genomics, proteomics & bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed.
- Epilepsy as a Multiscale Network Disorder: Integrating Precision Therapeutics and Emerging Experimental Platforms.Pharmaceutics · 2026Review
- Cardiac immunity and heart repair: Mechanism, challenge, and future direction.Chinese medical journal · 2026Review
- Leveraging single-cell and spatial omics for brain tumour insights to improve therapeutic strategies.Molecular brain · 2026Review
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- MultiPert: An adversarial alignment and dual attention framework for single-cell multi-omics perturbation prediction.PLoS computational biology · 2026Article
- Gut-Brain Axis Dysregulation in Inflammatory Bowel Disease: Implications for Coagulation Abnormalities and Extraintestinal Manifestations.International journal of general medicine · 2026Review
- Exploring microRNAs, One Cell at a Time.Non-coding RNA · 2025Article
- Trimodal Single-Cell Gene Regulatory Networks Reveal Principles of Stemness Loss and Cell Fate Acquisition in Human Hematopoiesis.bioRxiv : the preprint server for biology · 2025Article
- COEXIST: Coordinated single-cell integration of serial multiplexed tissue images.PLoS computational biology · 2025Article
- Decoding human cardiovascular development and disease through single-cell transcriptomic and epigenomic profiling.Trends in cell biology · 2025Review
- Facilitate integrated analysis of single cell multiomic data by binarizing gene expression values.Nature communications · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- Single-cell RNA-seq data have prevalent blood contamination but can be rescued by Originator, a computational tool separating single-cell RNA-seq by genetic and contextual information.Genome biology · 2025Article
- Single-Cell Transcriptomic Approaches for Decoding Non-Coding RNA Mechanisms in Colorectal Cancer.Non-coding RNA · 2025Review
- FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis.Briefings in bioinformatics · 2025Article
- Article
- Single-cell RNA-Seq data have prevalent blood contamination but can be rescued by Originator, a computational tool separating single-cell RNA-Seq by genetic and contextual information.bioRxiv : the preprint server for biology · 2025Article
- Spatial proteomics of the tumor microenvironment in melanoma: current insights and future directions.Frontiers in immunology · 2025Review
- The single-cell revolution in transplantation: high-resolution mapping of graft rejection, tolerance, and injury.Frontiers in immunology · 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
4 authors.
Funding
Abstract
Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. The problem of integrating different omics data with very different dimensionality and statistical properties remains, however, quite challenging. A growing body of computational tools is being developed for this task, leveraging ideas ranging from machine translation to the theory of networks, and represents another frontier on the interface of biology and data science. Our goal in this review is to provide a comprehensive, up-to-date survey of computational techniques for the integration of single-cell multi-omics data, while making the concepts behind each algorithm approachable to a non-expert audience.
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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.