ReviewMilitary Medical Research2022
Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications.
Review in Military Medical Research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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
75 citing papers in PubMed.
- Single nucleus transcriptomics reveals ITGA8-driven fibroblast remodeling in rheumatic valvular atrial fibrillation.iScience · 2026Article
- scFlowReport: A Reproducible Workflow for Comparative Downstream Biological Analysis of Single-Cell RNA-seq Data.Biomolecules · 2026Article
- Advancing targeted protein degradation: pLIRTAC's role in glioma and CAR-T cell therapy.Autophagy · 2026Article
- Host immune network-guided reverse vaccinology approach for prioritizing candidate antigens in Schistosoma japonicum-associated liver fibrosis.PLoS neglected tropical diseases · 2026Article
- Spatial single-cell landscape of tumor-associated macrophages and their crosstalk with the tumor microenvironment.Cell discovery · 2026Article
- Gene regulatory networks orchestrating oocyte fate bifurcation in primordial follicles revealed by single-cell transcriptomics.Communications biology · 2026Article
- New biological insights into osteosarcoma-lessons from single cell sequencing studies.Cancer metastasis reviews · 2026Review
- Multiomics Reveals IL-17 Drives Epithelial Keratinization and Proliferation via EHF in Odontogenic Keratocysts.International journal of molecular sciences · 2026Article
- Mapping of Neutrophils in Cancers: Insights From Spatial Omics Technologies.European journal of immunology · 2026Review
- Exploring single-cell and multi-omics technologies and their role in unraveling tumor heterogeneity of hepatocellular carcinoma.Journal of liver cancer · 2026Review
- Uncovering the role of integrated stress in Alzheimer's disease through single-cell and transcriptomic analysis.Scientific reports · 2026Article
- CellMap: precision mapping of cellular landscape in spatial transcriptomics.Nucleic acids research · 2026Article
- Macrophage-Mediated Cellular Communication Networks in Lung Squamous Cell Carcinoma and Adenocarcinoma Revealed by Single-Cell Sequencing.Mediators of inflammation · 2026Article
- Decoding Dementia Mechanisms: Identification of Key Oligodendrocyte- Associated Genes through Integrative Bioinformatics and Machine Learning.Current topics in medicinal chemistry · 2026Article
- Prognostic gene screening and experimental validation in renal clear cell carcinoma based on spatial transcriptomics and single-cell sequencing.Frontiers in immunology · 2026Article
- METTL14 modulates the nasopharyngeal carcinoma microenvironment via mFrontiers in immunology · 2026Article
- A clustering method for single-cell RNA sequencing data based on denoising and masking learning.Frontiers in bioinformatics · 2026Article
- Spatial transcriptomics in Alzheimer's disease: technologies, challenges and discoveries.Molecular neurodegeneration advances · 2026Review
- FastSCODE: an accelerated SCODE algorithm for inferring gene regulatory networks on manycore processors.Bioinformatics (Oxford, England) · 2025Article
- Single-cell insights into tumor microenvironment heterogeneity and plasticity: transforming precision therapy in gastrointestinal cancers.Journal of experimental & clinical cancer research : CR · 2025Review
15 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
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
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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.