ReviewMolecular therapy. Methods & clinical development2018
An Introduction to the Analysis of Single-Cell RNA-Sequencing Data.
Review in Molecular therapy. Methods & clinical development, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
What it found
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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
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Intestinal Stem Cells: Regulation, Plasticity, and Therapeutic Potential.Methods in molecular biology (Clifton, N.J.) · 2027Review
- Article
- Comparison between a conventional tool and deep learning models for RNA velocity analysis of scRNA-Seq data.Molecular genetics and genomics : MGG · 2026Article
- Linking Targeted Pancreatic Cancer Genes With Metabolic Disorders: A Cross-Species Translational Pathway.Cancer medicine · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- DGM: deep graph clustering with mincut for analysis of single-cell transcriptomics.BMC genomics · 2026Article
- Benign Uroandrological Ecosystem: A Thorough Overview from Single-Cell and Spatial Transcriptomics.Research (Washington, D.C.) · 2026Review
- Leucine Attenuates Osteoarthritis via mTORC1/LXRα-Mediated Macrophage Reprogramming and Rspo2/β-Catenin Axis Suppression.Cartilage · 2025Article
- Beyond metaphor: quantitative reconstruction of Waddington landscape and exploration of cellular behavior.Briefings in bioinformatics · 2025Review
- Systemic chemotherapy in metastatic TNBC polarizes effector T cell differentiation.Cancer cell international · 2025Article
- DeepAtlas: a tool for effective manifold learning.bioRxiv : the preprint server for biology · 2025Article
- Transcriptional characterization of sepsis in a LPS porcine model.Molecular genetics and genomics : MGG · 2025Article
- Integration of single-cell sequencing and mendelian randomization reveals novel causal pathways between monocytes and hepatocellular carcinoma.Discover oncology · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Multi-omics integration analysis and association study reveal the potential of ADIPOQ function in gestational diabetes mellitus.Nutrition & diabetes · 2025Article
- ShinyDegSEM: an interactive application for pathway perturbation analysis in gene expression studiesPeerJ · 2025Article
- Advancements in the Application of scRNA-Seq in Breast Research: A Review.International journal of molecular sciences · 2024Review
- Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis.Briefings in bioinformatics · 2024Article
- Article
7 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
Abstract
The recent development of single-cell RNA sequencing has deepened our understanding of the cell as a functional unit, providing new insights based on gene expression profiles of hundreds to hundreds of thousands of individual cells, and revealing new populations of cells with distinct gene expression profiles previously hidden within analyses of gene expression performed on bulk cell populations. However, appropriate analysis and utilization of the massive amounts of data generated from single-cell RNA sequencing experiments are challenging and require an understanding of the experimental and computational pathways taken between preparation of input cells and output of interpretable data. In this review, we will discuss the basic principles of these new technologies, focusing on concepts important in the analysis of single-cell RNA-sequencing data. Specifically, we summarize approaches to quality-control measures for determination of which single cells to include for further examination, methods of data normalization and scaling to overcome the relatively inefficient capture rate of mRNA from each cell, and clustering and visualization algorithms used for dimensional reduction of the data to a two-dimensional plot.
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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.