ReviewFrontiers in genetics2019
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis.
Review in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 545 papers, 2 of them syntheses that pooled it.
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
545 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of the Application of Graph Neural Networks to Extract Candidate Genes and Biological Associations.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Pooled it
- Pooled it
- A Method for Imputing scRNA-seq Data Based on Mobile Graph Convolutional Generative Adversarial Networks.Biochemical genetics · 2026Article
- E12.5 pancreatic bud-derived cells are detected in recipient pancreata after intraplacental transplantation, but Pdx1-deficient recipients lack a macroscopically detectable pancreas.Scientific reports · 2026Article
- scFlowReport: A Reproducible Workflow for Comparative Downstream Biological Analysis of Single-Cell RNA-seq Data.Biomolecules · 2026Article
- Decoding the senescent endothelial landscape in ischemic stroke through single-cell and bulk RNA sequencing.Functional & integrative genomics · 2026Article
- Tissue-resident colon macrophages shape the microbiome and regulate mucosal healing via ferroportin.Cell reports · 2026Article
- IntegrateRigor: annotation-free integration optimization for cell identity recovery reveals cancer-immune interface niches.bioRxiv : the preprint server for biology · 2026Article
- Review
- Single-Cell Omics Advances in Understanding Tissue Development and Complex Trait Formation in Sheep and Goats.Animals : an open access journal from MDPI · 2026Review
- Single-cell RNA sequencing in ovarian cancer: decoding the tumor microenvironment for personalized therapy.Journal of ovarian research · 2026Review
- Elevated labile iron contributes to membrane repair deficits in facioscapulohumeral muscular dystrophy.iScience · 2026Article
- Comparison between a conventional tool and deep learning models for RNA velocity analysis of scRNA-Seq data.Molecular genetics and genomics : MGG · 2026Article
- Persistent viral infection in the Drosophila fat body is associated with immune activation at the single cell level.BMC genomics · 2026Article
- BiGER: Bayesian rank aggregation in genomics with extended ranking schemes.Nature communications · 2026Article
- scDeepAPA: a deep learning framework for single-cell alternative polyadenylation identification.Briefings in bioinformatics · 2026Article
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Pre-implantation embryo metabolism identified by PEMA reveals endogenous lactate insufficiency contributes to pre-implantation development arrest.Fundamental research · 2026Article
- Immune cell annotation in the single-cell studies: technologies, challenges, and integrative solutions.Immunologic research · 2026Review
- Multi-omics single-cell dissection of malignant epithelial heterogeneity identifies GJB2 as an EMT-driving biomarker in triple-negative breast cancer.BMC medical genomics · 2026Article
485 more citing papers are in PubMed but not listed here.
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
No grant is acknowledged in the PubMed record.
Abstract
Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A number of scRNA-seq protocols have been developed, and these methods possess their unique features with distinct advantages and disadvantages. Due to technical limitations and biological factors, scRNA-seq data are noisier and more complex than bulk RNA-seq data. The high variability of scRNA-seq data raises computational challenges in data analysis. Although an increasing number of bioinformatics methods are proposed for analyzing and interpreting scRNA-seq data, novel algorithms are required to ensure the accuracy and reproducibility of results. In this review, we provide an overview of currently available single-cell isolation protocols and scRNA-seq technologies, and discuss the methods for diverse scRNA-seq data analyses including quality control, read mapping, gene expression quantification, batch effect correction, normalization, imputation, dimensionality reduction, feature selection, cell clustering, trajectory inference, differential expression calling, alternative splicing, allelic expression, and gene regulatory network reconstruction. Further, we outline the prospective development and applications of scRNA-seq technologies.
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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.