ReviewFrontiers in neuroscience2021
Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis.
Review in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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.
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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.
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Who cites it
75 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Single-cell RNA sequencing offers novel perspectives in viral infection research.Frontiers in cellular and infection microbiology · 2026Pooled it
- celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool.bioRxiv : the preprint server for biology · 2026Article
- Vimentin in Developing Human Adrenal Medulla Cells.Life (Basel, Switzerland) · 2026Article
- Decoding dry eye disease based on bioinformatics and in vitro experimental: the role of immune responses and natural product intervention.Human genomics · 2026Article
- SC-framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets.iScience · 2026Article
- Synthesizing Mechanistic Hypotheses from Single-Cell Omics via Discretized Feature Attribution and Empirical Language Model Grounding.bioRxiv : the preprint server for biology · 2026Article
- The applications of single-cell and spatial transcriptomics in neuroscience and brain disorders.Neuroscience and biobehavioral reviews · 2026Review
- High-sensitivity in situ detection of Sox21 reveals transcript asymmetry from the zygote stage.Developmental biology · 2026Article
- Article
- Deciphering the genetic control of immune cell function at single-cell resolution: Disease-Specific Cis-eQTLs analysis of COVID-19.Immunogenetics · 2026Article
- scII: Dual-Threshold Adaptive Integration of Single-Cell Multiomics Data Driven by Imputation.Journal of chemical information and modeling · 2026Article
- EnsembleRegNet: Interpretable deep learning for transcriptional network inference from single-cell RNA-seq.Computational biology and chemistry · 2026Article
- A copula-infused graph neural network for cell type classification in single-cell RNA sequencing data.Computational and structural biotechnology journal · 2026Article
- Single-cell dissection of hepatocellular carcinoma immunity: from heterogeneous subtypes to precision therapeutics.Frontiers in immunology · 2026Review
- Harnessing plasma transcriptomics for non-invasive cancer biomarker identification: a comprehensive review.Discover oncology · 2025Review
- Biomaterial-mediated Cell Atlas: an insight from single-cell and spatial transcriptomics.Bioactive materials · 2025Review
- Single-cell RNA sequencing reveals tumor cell and immune cell variations associated with lymphatic metastasis in papillary thyroid cancer.Endocrine connections · 2025Article
- Review
- Single-Cell Transcriptome Atlas and Dynamic Regulatory Mechanisms of Anther Development in Alfalfa (Medicago sativa L.).Plant biotechnology journal · 2025Article
- [Progress of scRNA-seq technology in nasopharyngeal carcinoma research].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.
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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.