Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 828

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Trajectory-based differential expression analysis for single-cell sequencing data

Open access
Van den Berge, Koen, Roux de Bézieux, Hector, Street, Kelly, Saelens, Wouter, Cannoodt, Robrecht, Saeys, Yvan, Dudoit, Sandrine, Clement, Lieven·2020·Nature Communications
DOI 10.1038/s41467-020-14766-3

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression. Downstream of trajectory inference, it is vital to discover genes that are (i) associated with the lineages in the trajectory, or (ii) differentially expressed between lineages, to illuminate the underlying biological processes. Current data analysis procedures, however, either fail to exploit the continuous resolution provided by trajectory inference, or fail to pinpoint the exact types of differential expression. We introduce tradeSeq, a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression. By incorporating observation-level weights, the model additionally allows to account for zero inflation. We evaluate the method on simulated datasets and on real datasets from droplet-based and full-length protocols, and show that it yields biological insights through a clear interpretation of the data.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-020-14766-3

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 11 references

Research works printed but not linked: 31

  1. Cannoodt, R., Saelens, W. & Saeys, Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur. J. Immunol. 46, 2496–2506 (2016). · DOI
  2. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018). · DOI · Paper page
  3. Lönnberg, T. et al. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria. Sci. Immunol. 2, 3 (2017). · DOI · Paper page
  4. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017). · DOI · Paper page
  5. Byrnes, L. E. et al. Lineage dynamics of murine pancreatic development at single-cell resolution. Nat. Commun. 9, 3922 (2018). · DOI
  6. Herring, C. A. et al. Unsupervised trajectory analysis of single-cell RNA-Seq and imaging data reveals alternative tuft cell origins in the gut. Cell Syst. 6, 37–51 (2018). · DOI
  7. Nueda, M. J., Tarazona, S. & Conesa, A. Next maSigPro: updating maSigPro bioconductor package for RNA-seq time series. Bioinformatics 30, 2598–2602 (2014). · DOI · Paper page
  8. Sanavia, T., Finotello, F. & Di Camillo, B. FunPat: function-based pattern analysis on RNA-seq time series data. BMC Genomics 16, S2 (2015). · DOI
  9. Bacher, R. et al. Trendy: segmented regression analysis of expression dynamics in high-throughput ordered profiling experiments. BMC Bioinform. 19, 380 (2018). · DOI
  10. Ji, Z. & Ji, H. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, e117–e117 (2016). · DOI · Paper page
  11. Lázaro-Gredilla, M., Van Vaerenbergh, S. & Lawrence, N. D. Overlapping mixtures of Gaussian processes for the data association problem. Pattern Recogn. 45, 1386–1395 (2012). · DOI
  12. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015). · DOI · Paper page
  13. Fletcher, R. B. et al. Deconstructing olfactory stem cell trajectories at single-cell resolution. Cell Stem Cell 20, 817–830 (2017). · DOI
  14. Hegg, C. C., Jia, C., Chick, W. S., Restrepo, D. & Hansen, A. Microvillous cells expressing IP3 receptor type 3 in the olfactory epithelium of mice. Eur. J. Neurosci. 32, 1632–1645 (2010). · DOI
  15. Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J.-P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018). · DOI · Paper page
  16. Boukouvalas, A., Hensman, J. & Rattray, M. BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process. Genome Biol. 19, 65 (2018). · DOI
  17. Alpert, A., Moore, L. S., Dubovik, T. & Shen-Orr, S. S. Alignment of single-cell trajectories to compare cellular expression dynamics. Nat. Methods 15, 267–270 (2018). · DOI · Paper page
  18. Chen, H. et al. Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat. Commun. 10, 1903 (2019). · DOI · Paper page
  19. Wood, S. N., Pya, N. & Säfken, B. Smoothing parameter and model selection for general smooth models. J. Am. Stat. Assoc. 111, 1548–1563 (2016). · DOI · Paper page
  20. Risso, D. clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets. PLoS Comput. Biol. 14, e1006378 (2018). · DOI
  21. Heller, R., Manduchi, E., Grant, G. R. & Ewens, W. J. A flexible two-stage procedure for identifying gene sets that are differentially expressed. Bioinformatics 25, 1019–1025 (2009). · DOI
  22. Van den Berge, K., Soneson, C., Robinson, M. D. & Clement, L. stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage. Genome Biol. 18, 151 (2017). · DOI · Paper page
  23. Lawrence, N. D. Gaussian process latent variable models for visualisation of high dimensional data. in NIPS’03 Proceedings of the 16th International Conference on Neural Information Processing Systems, Advances in neural information processing systems 16, 329–336 (2003).
  24. Fischer, D. S., Theis, F. J. & Yosef, N. Impulse model-based differential expression analysis of time course sequencing data. Nucleic Acids Res. 46, e119–e119 (2018). · DOI
  25. McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013). · DOI · Paper page
  26. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012). · DOI · Paper page
  27. Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–93 (2003). · DOI · Paper page
  28. Fisher, R. A. Statistical Methods for Research Workers. https://scholar.google.com/scholar_lookup?title=Statisticalmethodsforresearchworkers&author=RA.Fisher&publication_year=1932 (Oliver & Boyd, Edinburgh, 1925).
  29. Soneson, C. & Robinson, M. D. iCOBRA: open, reproducible, standardized and live method benchmarking. Nat. Methods 13, 283–283 (2016). · DOI
  30. Kiselev, V. Y. et al. Perturbations of PIP3 signalling trigger a global remodelling of mRNA landscape and reveal a transcriptional feedback loop. Nucleic Acids Res. 43, gkv1015 (2015). · DOI
  31. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019). · DOI · Paper page

Cited by

See all 828

Metadata

Authors
Van den Berge, K., Roux de Bézieux, H., Street, K., Saelens, W., Cannoodt, R., Saeys, Y., Dudoit, S., & Clement, L.
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-03-05
Volume
11
Issue
1
Pages
1201-1201
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-14766-3
OpenAlex
W3009370296
Cited by
828
References
11
FWCI
22.89
Type
journal article
Language
en
Open access
gold
Licence
Funders
  • Belgian American Educational Foundation
  • Fonds Wetenschappelijk Onderzoek (G062219N, 1246220N, 148095)

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Trajectory-based differential expression analysis for single-cell sequencing data. https://app.cochat.ai/papers/10.1038/s41467-020-14766-3
The paper
Van den Berge, K., Roux de Bézieux, H., Street, K., Saelens, W., Cannoodt, R., Saeys, Y., Dudoit, S., & Clement, L. (2020). Trajectory-based differential expression analysis for single-cell sequencing data. Nature Communications. https://doi.org/10.1038/s41467-020-14766-3

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.