ReviewMolecules and cells2023
Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods.
Review in Molecules and cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 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
47 citing papers in PubMed.
- Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.Genome research · 2026Article
- RegFormer: a single-cell foundation model powered by gene regulatory hierarchies.Nature communications · 2026Article
- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Human Systems Immunology in the Omics Era: Challenges, Methods, and Emerging Directions.European journal of immunology · 2026Review
- Machine learning approaches for biomarker discovery using single-cell RNA sequencing.Frontiers in bioinformatics · 2026Review
- Article
- Simple molecules make difference: short peptides play a novel role in slowing senescence.npj aging · 2025Review
- Integrating single-cell RNA-seq datasets with substantial batch effects.BMC genomics · 2025Article
- Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in single-cell experiments.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-based cell type profiles reveal signatures of Alzheimer's disease resilience and resistance.Brain : a journal of neurology · 2025Article
- T-CLASS: An Online Tool for the Identification and Classification of Aging and Senescence Using Transcriptome Data.Aging cell · 2025Article
- Establishing single cell RNA transcriptomics: a brief guide.Frontiers in zoology · 2025Review
- Advances and applications in single-cell and spatial genomics.Science China. Life sciences · 2025Review
- A spatially resolved transcriptome landscape during thyroid cancer progression.Cell reports. Medicine · 2025Article
- SCITUNA: single-cell data integration tool using network alignment.BMC bioinformatics · 2025Article
- Exploration of Conserved Human Adipose Subpopulations Using Targeted Single-Nuclei RNA Sequencing Data Sets.Journal of the American Heart Association · 2025Article
- Benchmarking cross-species single-cell RNA-seq data integration methods: towards a cell type tree of life.Nucleic acids research · 2025Article
- Harmonizing heterogeneous single-cell gene expression data with individual-level covariate information.Bioinformatics advances · 2025Article
- Recent progress in regulation of aging by non-coding RNAs inAnimal cells and systems · 2025Review
- Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research.Acta biochimica Polonica · 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
No grant is acknowledged in the PubMed record.
Abstract
With the increased number of single-cell RNA sequencing (scRNA-seq) datasets in public repositories, integrative analysis of multiple scRNA-seq datasets has become commonplace. Batch effects among different datasets are inevitable because of differences in cell isolation and handling protocols, library preparation technology, and sequencing platforms. To remove these batch effects for effective integration of multiple scRNA-seq datasets, a number of methodologies have been developed based on diverse concepts and approaches. These methods have proven useful for examining whether cellular features, such as cell subpopulations and marker genes, identified from a certain dataset, are consistently present, or whether their condition-dependent variations, such as increases in cell subpopulations in particular disease-related conditions, are consistently observed in different datasets generated under similar or distinct conditions. In this review, we summarize the concepts and approaches of the integration methods and their pros and cons as has been reported in previous literature.
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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.