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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cites
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Molecular portraits of human breast tumours
Perou, C., Sørlie, T., Eisen, M., van de Rijn, M., Jeffrey, S., Rees, C. A., Pollack, J., Ross, D., Johnsen, H., Akslen, L., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,551
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
Robinson, J. T., Thorvaldsdóttir, H., Winckler, W., Guttman, M., Lander, E. S., Getz, G., & Mesirov, J. P. (2011) · Nature Biotechnology · doi:10.1038/nbt.1754 · Cited by 15,390
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Nishimura, R., Osako, T., Nishiyama, Y., Tashima, R., Nakano, M., Fujisue, M., Toyozumi, Y., & Arima, N. (2013) · Oncology · doi:10.1159/000353099 · Cited by 13,239
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174
Flagged first, then by citations
1 of the 90 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Krebs, J. E., Goldstein, E. S. & Kilpatrick, S. T. Lewin's Genes XI (Jones & Bartlett Publishers, 2014).
Cited by
Sorted by citations
Items 1,001–1,001 of 1,001
Computational Methods for Inferring Transcriptome Dynamics
Welch, J. D. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/yazw-zn17
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
