Cited by 1,170Single-cell and spatial transcriptomicsGenomics and Phylogenetic StudiesGene expression and cancer classification

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Computational and analytical challenges in single-cell transcriptomics

Stegle, Oliver, Teichmann, Sarah A., Marioni, John C.·2015·Nature Reviews Genetics

DOI 10.1038/nrg3833

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Abstract

The development of high-throughput RNA sequencing (RNA-seq) at the single-cell level has already led to profound new discoveries in biology, ranging from the identification of novel cell types to the study of global patterns of stochastic gene expression. Alongside the technological breakthroughs that have facilitated the large-scale generation of single-cell transcriptomic data, it is important to consider the specific computational and analytical challenges that still have to be overcome. Although some tools for analysing RNA-seq data from bulk cell populations can be readily applied to single-cell RNA-seq data, many new computational strategies are required to fully exploit this data type and to enable a comprehensive yet detailed study of gene expression at the single-cell level.

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“While unsupervised analysis of single-cell data has transformative potential to uncover heterogeneous cell types and states, cell-to-cell variation in technical factors can also confound these results [1, 2].”
Background
Determining cell type abundance and expression from bulk tissues with digital cytometry
Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D. F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0114-2 · Cited by 4,504

See all 1,170

Authors: Stegle, O., Teichmann, S. A., & Marioni, J. C.
Affiliations: European Bioinformatics Institute (GB)
Corresponding author: Marioni, John C.
ORCID: Stegle, Oliver
Teichmann, Sarah A.
Marioni, John C.
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2015-01-28
Volume: 16
Issue: 3
Pages: 133-145
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/nrg3833
OpenAlex: W1988391018
Cited by: 1,170
References: 90
Citations by year: 2026: 40 · 2025: 83 · 2024: 85 · 2023: 100 · 2022: 106 · 2021: 122 · 2020: 130 · 2019: 132 · 2018: 112 · 2017: 120 · 2016: 102 · 2015: 35
FWCI: 11.92
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Nature Reviews Genetics (publishedVersion)
PubMed (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: single-cell RNA sequencing, single-cell transcriptomics, computational challenges, data analysis, stochastic gene expression, cell type annotation, gene expression analysis
MeSH terms: Animals, Cell Differentiation, Eukaryotic Cells, Humans, Quality Control, RNA, Messenger, Software, Gene Expression Profiling, Gene Regulatory Networks, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome

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Computational and analytical challenges in single-cell transcriptomics

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