The DOI matches its Crossref record; 89 of the 90 references it cites were found in a registry or an index.
Computational and analytical challenges in single-cell transcriptomics
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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“cell-to-cell variation in technical factors can also confound these results [1, 2].”
From Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019) · cites this paper as [2]
In the citing paper
“Consequently, multiple replicates of C1 collections from the same biological condition are necessary to facilitate estimation of technical variability even with the presence of ERCC spike-in controls10.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [10]
In the citing paper
“It has been noted before that ERCCs can be problematic for modeling endogenous mRNAs (Risso et al., 2014), potentially due to their shorter length, shorter poly-A tail, and their missing 5 0 cap (Gr € un and van Oudenaarden, 2015; Stegle et al., 2015).”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Stegle et al., 2015
In the citing paper
“This extra Poisson variability includes biological variation (assumed to be the same across methods in our data) and technical variation, and the latter includes noise introduced by amplification (Brennecke et al., 2013; Gr € un et al., 2014; Stegle et al., 2015).”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Stegle et al., 2015
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Metadata
- Authors
- Stegle, O., Teichmann, S. A., & Marioni, J. C.
- Affiliations
- European Bioinformatics Institute (GB)
- Corresponding author
- Marioni, John C.
- Venue
- Nature Reviews Genetics
- Published
- 2015-01-28
- Volume
- 16
- Issue
- 3
- Pages
- 133-145
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- 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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