ReviewTrends in genetics : TIG2018
Single-Cell Multi-omics: An Engine for New Quantitative Models of Gene Regulation.
Review in Trends in genetics : TIG, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 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
57 citing papers in PubMed.
- Synthetic lethality in cancer: mechanism exploration and therapeutic applications.Cell communication and signaling : CCS · 2026Review
- Optimal transport for label transfer in single-cell multi-omics integration.Briefings in bioinformatics · 2026Article
- SCMBench: benchmarking domain-specific and foundation models for single-cell multi-omics data integration.Nature communications · 2026Article
- A comprehensive benchmarking study on computational tools for cross-omics label transfer from single-cell RNA to ATAC data.G3 (Bethesda, Md.) · 2026Article
- Multi-omics to study chronic respiratory diseases and viral infections.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- SCREAM: Single-cell Clustering using Representation Autoencoder of Multiomics.bioRxiv : the preprint server for biology · 2025Article
- CellSTAR: a comprehensive resource for single-cell transcriptomic annotation.Nucleic acids research · 2024Article
- Quantifying orthogonal barcodes for sequence census assays.Bioinformatics advances · 2024Article
- Model construction and clinical therapeutic potential of engineered cardiac organoids for cardiovascular diseases.Biomaterials translational · 2024Review
- Review
- Review
- Understanding Epicardial Cell Heterogeneity during Cardiogenesis and Heart Regeneration.Journal of cardiovascular development and disease · 2023Review
- Methods and applications for single-cell and spatial multi-omics.Nature reviews. Genetics · 2023Review
- Studying stochastic systems biology of the cell with single-cell genomics data.bioRxiv : the preprint server for biology · 2023Article
- Multi-omics single-cell data integration and regulatory inference with graph-linked embedding.Nature biotechnology · 2022Article
- Mechanical Strain Drives Myeloid Cell Differentiation Toward Proinflammatory Subpopulations.Advances in wound care · 2022Article
- Simultaneous brain cell type and lineage determined by scRNA-seq reveals stereotyped cortical development.Cell systems · 2022Article
- Progress in multicellular human cardiac organoids for clinical applications.Cell stem cell · 2022Review
- New horizons in the stormy sea of multimodal single-cell data integration.Molecular cell · 2022Review
- Prognosis Prediction Through an Integrated Analysis of Single-Cell and Bulk RNA-Sequencing Data in Triple-Negative Breast Cancer.Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Cells in a multicellular organism fulfill specific functions by enacting cell-type-specific programs of gene regulation. Single-cell RNA sequencing technologies have provided a transformative view of cell-type-specific gene expression, the output of cell-type-specific gene regulatory programs. This review discusses new single-cell genomic technologies that complement single-cell RNA sequencing by providing additional readouts of cellular state beyond the transcriptome. We highlight regression models as a simple yet powerful approach to relate gene expression to other aspects of cellular state, and in doing so, gain insights into the biochemical mechanisms that are necessary to produce a given gene expression output.
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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.