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

Koen Van den Berge, View ORCID ProfileHector Roux de Bézieux, Kelly Street, View ORCID ProfileWouter Saelens, View ORCID ProfileRobrecht Cannoodt, View ORCID ProfileYvan Saeys, View ORCID ProfileSandrine Dudoit, View ORCID ProfileLieven Clement
doi: https://doi.org/10.1101/623397
Koen Van den Berge
1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
2Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
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Hector Roux de Bézieux
3Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
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Kelly Street
4Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
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Wouter Saelens
1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
5Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
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Robrecht Cannoodt
5Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
6Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium
7Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
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Yvan Saeys
1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
5Data mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
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Sandrine Dudoit
3Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA
8Department of Statistics, University of California, Berkeley, CA, USA
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  • For correspondence: sandrine@stat.berkeley.edu lieven.clement@ugent.be
Lieven Clement
1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
2Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
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  • For correspondence: sandrine@stat.berkeley.edu lieven.clement@ugent.be
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Abstract

Trajectory inference has radically enhanced single-cell RNA-seq research by enabling the study of dynamic changes in gene expression levels during biological processes such as the cell cycle, cell type differentiation, and cellular activation. Downstream of trajectory inference, it is vital to discover genes that are associated with the lineages in the trajectory to illuminate the underlying biological processes. Furthermore, genes that are differentially expressed between developmental/activational lineages might be highly relevant to further unravel the system under study. Current data analysis procedures, however, typically cluster cells and assess differential expression between the clusters, which fails to exploit the continuous resolution provided by trajectory inference to its full potential. The few available non-cluster-based methods only assess broad differences in gene expression between lineages, hence failing to pinpoint the exact types of divergence. We introduce a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of (i) within-lineage differential expression by detecting associations between gene expression and pseudotime over an entire lineage or by comparing gene expression between points/regions within the lineage and (ii) between-lineage differential expression by comparing gene expression between lineages over the entire lineages or at specific points/regions. By incorporating observation-level weights, the model additionally allows to account for zero inflation, commonly observed in single-cell RNA-seq data from full-length protocols. We evaluate the method on simulated and real datasets from droplet-based and full-length protocols, and show that the flexible inference framework is capable of yielding biological insights through a clear interpretation of the data.

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Posted May 02, 2019.
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Trajectory-based differential expression analysis for single-cell sequencing data
Koen Van den Berge, Hector Roux de Bézieux, Kelly Street, Wouter Saelens, Robrecht Cannoodt, Yvan Saeys, Sandrine Dudoit, Lieven Clement
bioRxiv 623397; doi: https://doi.org/10.1101/623397
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Trajectory-based differential expression analysis for single-cell sequencing data
Koen Van den Berge, Hector Roux de Bézieux, Kelly Street, Wouter Saelens, Robrecht Cannoodt, Yvan Saeys, Sandrine Dudoit, Lieven Clement
bioRxiv 623397; doi: https://doi.org/10.1101/623397

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