ReviewAnnual review of biomedical data science2023
Single-Cell Multiomics.
Review in Annual review of biomedical data science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Charting Endocrine Progenitors Across Species and Organs.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Immune Landscape and Tumour Heterogeneity in Ovarian Cancer: Insights From Single-Cell RNA Sequencing.Journal of cellular and molecular medicine · 2026Review
- Article
- Evaluating the Utilities of Foundation Models in Single-Cell Data Analysis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Development of infrared desolvation device for single cell element analysis using droplet sample injection inductively coupled plasma spectrometry.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Article
- Parallel analysis of replication timing, gene expression, and copy number with PARTAGE.Genome research · 2026Article
- Challenges and Opportunities in Multi-Omics Data Acquisition and Analysis: Toward Integrative Solutions.Biomolecules · 2026Review
- CAR-T cells in solid tumors: engineering, biomarkers, translational pathways and the road ahead.Frontiers in immunology · 2026Review
- Editorial: Multiomics advances: bridging gaps in understanding intricate biological systems.Frontiers in plant science · 2026Article
- Exploring the Potential Molecular Targets of Cyanidin-3-O-glucoside for Type 2 Diabetes Mellitus Treatment.Endocrine, metabolic & immune disorders drug targets · 2026Article
- Review
- PARTAGE: Parallel analysis of replication timing and gene expression.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics.Briefings in bioinformatics · 2025Review
- Identifying similar populations across independent single cell studies without data integration.NAR genomics and bioinformatics · 2025Article
- Single-cell omics technologies - Fundamentals on how to create single-cell looking glasses for reproductive health.American journal of obstetrics and gynecology · 2025Review
- Multimodal single-cell profiling reveals neuronal vulnerability and pathological cell states in focal cortical dysplasia.iScience · 2024Article
- Single-cell RNA sequencing in diabetic kidney disease: a literature review.Renal failure · 2024Review
- CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis.Briefings in bioinformatics · 2024Article
- Integrated single-cell analysis reveals heterogeneity and therapeutic insights in osteosarcoma.Discover oncology · 2024Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Single-cell RNA sequencing methods have led to improved understanding of the heterogeneity and transcriptomic states present in complex biological systems. Recently, the development of novel single-cell technologies for assaying additional modalities, specifically genomic, epigenomic, proteomic, and spatial data, allows for unprecedented insight into cellular biology. While certain technologies collect multiple measurements from the same cells simultaneously, even when modalities are separately assayed in different cells, we can apply novel computational methods to integrate these data. The application of computational integration methods to multimodal paired and unpaired data results in rich information about the identities of the cells present and the interactions between different levels of biology, such as between genetic variation and transcription. In this review, we both discuss the single-cell technologies for measuring these modalities and describe and characterize a variety of computational integration methods for combining the resulting data to leverage multimodal information toward greater biological insight.
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.