ReviewGenomics, proteomics & bioinformatics2016
Single-cell Transcriptome Study as Big Data.
Review in Genomics, proteomics & bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Experimental Framework for Assessing Mouse Retinal Regeneration Through Single-Cell RNA-Sequencing.Methods in molecular biology (Clifton, N.J.) · 2025Article
- scRNA-seq for Microcephaly Research [III]: Computational Analysis of scRNA-seq Data.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Comprehensive multi-omics single-cell data integration reveals greater heterogeneity in the human immune system.iScience · 2022Article
- scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets.Genomics, proteomics & bioinformatics · 2021Article
- Evaluation of Cell Type Annotation R Packages on Single-cell RNA-seq Data.Genomics, proteomics & bioinformatics · 2021Article
- Unraveling the Immunopathogenesis and Genetic Variants in Vasculitis Toward Development of Personalized Medicine.Frontiers in cardiovascular medicine · 2021Review
- Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis.Frontiers in neuroscience · 2021Review
- RainDrop: Rapid activation matrix computation for droplet-based single-cell RNA-seq reads.BMC bioinformatics · 2020Article
- The basic and translational science year in review: Confucius in the era of Big Data.Seminars in arthritis and rheumatism · 2020Review
- Redefining Tumor-Associated Macrophage Subpopulations and Functions in the Tumor Microenvironment.Frontiers in immunology · 2020Review
- Single-cell transcriptome provides novel insights into antler stem cells, a cell type capable of mammalian organ regeneration.Functional & integrative genomics · 2019Article
- Single-cell Analysis of CAR-T Cell Activation Reveals A Mixed TGenomics, proteomics & bioinformatics · 2019Article
- Bioinformatics in Germany: toward a national-level infrastructure.Briefings in bioinformatics · 2019Article
- Uncovering the Transcriptional Correlates of Hub Connectivity in Neural Networks.Frontiers in neural circuits · 2019Review
- Uncovering the immune tumor microenvironment in non-small cell lung cancer to understand response rates to checkpoint blockade and radiation.Translational lung cancer research · 2017Review
- Scalability and Validation of Big Data Bioinformatics Software.Computational and structural biotechnology journal · 2017Review
- High Throughput Sequencing: An Overview of Sequencing Chemistry.Indian journal of microbiology · 2016Review
- Single-cell genome-wide studies give new insight into nongenetic cell-to-cell variability in animals.Histochemistry and cell biology · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid growth of single-cell RNA-seq studies (scRNA-seq) demands efficient data storage, processing, and analysis. Big-data technology provides a framework that facilitates the comprehensive discovery of biological signals from inter-institutional scRNA-seq datasets. The strategies to solve the stochastic and heterogeneous single-cell transcriptome signal are discussed in this article. After extensively reviewing the available big-data applications of next-generation sequencing (NGS)-based studies, we propose a workflow that accounts for the unique characteristics of scRNA-seq data and primary objectives of single-cell studies.
Indexed as
Identifiers
What OpenQuestion holds
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.