Gene trajectory inference for single-cell data by optimal transport metrics

Rihao Qu^#^{1

2

3}, Xiuyuan Cheng^#⁴, Esen Sefik³, Jay S Stanley Iii⁵, Boris Landa⁵, Francesco Strino⁶, Sarah Platt^{2

7}, James Garritano⁵, Ian D Odell^{3

7}, Ronald Coifman^{5

8

9}, Richard A Flavell^{3

10}, Peggy Myung^{2

7}, Yuval Kluger^{11

12

13}

Affiliations

¹ Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA.
² Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
³ Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.
⁴ Department of Mathematics, Duke University, Durham, NC, USA.
⁵ Program in Applied Mathematics, Yale University, New Haven, CT, USA.
⁶ PCMGF Limited, Watford, UK.
⁷ Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA.
⁸ Department of Mathematics, Yale University, New Haven, CT, USA.
⁹ Department of Electrical Engineering, Yale University, New Haven, CT, USA.
¹⁰ Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, CT, USA.
¹¹ Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA. yuval.kluger@yale.edu.
¹² Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. yuval.kluger@yale.edu.
¹³ Program in Applied Mathematics, Yale University, New Haven, CT, USA. yuval.kluger@yale.edu.

^# Contributed equally.

PMID: 38580861
DOI: 10.1038/s41587-024-02186-3

Gene trajectory inference for single-cell data by optimal transport metrics

Rihao Qu et al. Nat Biotechnol. 2024.

. 2024 Apr 5.

doi: 10.1038/s41587-024-02186-3. Online ahead of print.

Authors

Affiliations

¹ Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA.
² Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
³ Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA.
⁴ Department of Mathematics, Duke University, Durham, NC, USA.
⁵ Program in Applied Mathematics, Yale University, New Haven, CT, USA.
⁶ PCMGF Limited, Watford, UK.
⁷ Department of Dermatology, Yale University School of Medicine, New Haven, CT, USA.
⁸ Department of Mathematics, Yale University, New Haven, CT, USA.
⁹ Department of Electrical Engineering, Yale University, New Haven, CT, USA.
¹⁰ Howard Hughes Medical Institute, Yale University School of Medicine, New Haven, CT, USA.
¹¹ Computational Biology & Bioinformatics Program, Yale University, New Haven, CT, USA. yuval.kluger@yale.edu.
¹² Department of Pathology, Yale University School of Medicine, New Haven, CT, USA. yuval.kluger@yale.edu.
¹³ Program in Applied Mathematics, Yale University, New Haven, CT, USA. yuval.kluger@yale.edu.

^# Contributed equally.

PMID: 38580861
DOI: 10.1038/s41587-024-02186-3

Abstract

Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell-cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.

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References

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Gene trajectory inference for single-cell data by optimal transport metrics

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Gene trajectory inference for single-cell data by optimal transport metrics

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Abstract

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