ReviewFrontiers in genetics2022
From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis.
Review in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single-cell and Mendelian randomization analyses.Oncology letters · 2026Article
- Single-cell RNA sequencing in ovarian cancer: decoding the tumor microenvironment for personalized therapy.Journal of ovarian research · 2026Review
- Integrated transcriptome and single-cell RNA sequencing identifies small GTPase-associated biomarkers in ulcerative colitis.Frontiers in immunology · 2026Article
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- Research progress of single cell RNA sequencing in nervous system.Molecular biology reports · 2025Review
- PhytoCluster: a generative deep learning model for clustering plant single-cell RNA-seq data.aBIOTECH · 2025Article
- High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution.Scientific reports · 2024Article
- High-throughput gene expression analysis with TempO-LINC sensitively resolves complex brain, lung and kidney heterogeneity at single-cell resolution.bioRxiv : the preprint server for biology · 2024Article
- Article
- Bioinformatics for wet-lab scientists: practical application in sequencing analysis.BMC genomics · 2023Article
- RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding.Scientific reports · 2023Article
- Cortical type: a conceptual tool for meaningful biological interpretation of high-throughput gene expression data in the human cerebral cortex.Frontiers in neuroanatomy · 2023Article
- Analysis of candidate genes for cleft lip ± cleft palate using murine single-cell expression data.Frontiers in cell and developmental biology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in biomedical research settings, allowing to profile the whole transcriptome of a very large number of individual cells and reveal the heterogeneity of complex clinical samples. Traditionally, cells have been classified by their morphology or by expression of certain proteins in functionally distinct settings. The advent of next generation sequencing (NGS) technologies paved the way for the detection and quantitative analysis of cellular content. In this context, transcriptome quantification techniques made their advent, starting from the bulk RNA sequencing, unable to dissect the heterogeneity of a sample, and moving to the first single cell techniques capable of analyzing a small number of cells (1-100), arriving at the current single cell techniques able to generate hundreds of thousands of cells. As experimental protocols have improved rapidly, computational workflows for processing the data have also been refined, opening up to novel methods capable of scaling computational times more favorably with the dataset size and making scRNA-seq much better suited for biomedical research. In this perspective, we will highlight the key technological and computational developments which have enabled the analysis of this growing data, making the scRNA-seq a handy tool in clinical applications.
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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.