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Enabling single-cell trajectory network enrichment


Single-cell sequencing (scRNA-seq) technologies allow the investigation of cellular differentiation processes with unprecedented resolution. Although powerful software packages for scRNA-seq data analysis exist, systems biology-based tools for trajectory analysis are rare and typically difficult to handle. This hampers biological exploration and prevents researchers from gaining deeper insights into the molecular control of developmental processes. Here, to address this, we have developed Scellnetor; a network-constraint time-series clustering algorithm. It allows extraction of temporal differential gene expression network patterns (modules) that explain the difference in regulation of two developmental trajectories. Using well-characterized experimental model systems, we demonstrate the capacity of Scellnetor as a hypothesis generator to identify putative mechanisms driving haematopoiesis or mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Altogether, Scellnetor allows for single-cell trajectory network enrichment, which effectively lifts scRNA-seq data analysis to a systems biology level.

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Fig. 1: Workflow of Scellnetor.
Fig. 2: Computation of hyper-similarity matrix for gene module discovery.
Fig. 3: Comparison of neutrophil and erythrocyte differentiation trajectories.
Fig. 4: Differentiation of progenitor cells to dysfunctional CD8 T cells.

Data availability

The scRNA-seq data used for the Scellnetor haematopoiesis analysis is from GEO (GSE72857). The scRNA-seq used for the clustering of exhausted CD8 T cells in chronic infections is also from GEO (GSE137007). Scellnetor results can be downloaded from ref. 53 and from GitLab.

Code availability

Scellnetor is freely available as an online tool at and can be downloaded as a standalone program from ref. 53 and from GitLab.


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J.B. and A.G.B.G. received funding from J.B.’s VILLUM Young Investigator grant no. 13154. The work of J.B. and T.K. was further funded by H2020 project RepoTrial (no. 777111). The work of R.R. and J.B. was partially funded by H2020 project FeatureCloud (no. 826078). J.B. and T.K. are grateful for financial support from BMBF project Sys_Care. M.O. is grateful for financial support from the Collaborative Research Center SFB924.

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Authors and Affiliations



A.G.B.G. developed and implemented the clustering algorithm of the Scellnetor tool. A.G.B.G. developed and implemented all basic backend functionalities of the webtool. A.G.B.G., J.L. and M.O. further developed the webtool. K.K., T.K. and D.Z. tested the webtool, provided critical feedback and, together with A.G.B.G., used Scellnetor to generate the biomedical results presented in the manuscript. All authors contributed equally to writing and improving the paper. A.G.B.G., J.B. and R.R. conceived the idea of the Scellnetor pipeline.

Corresponding authors

Correspondence to Alexander G. B. Grønning or Jan Baumbach.

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The authors declare no competing interests.

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Peer review information Nature Computational Science thanks Florian Klimm and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Yann Sweeney was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Grønning, A.G.B., Oubounyt, M., Kanev, K. et al. Enabling single-cell trajectory network enrichment. Nat Comput Sci 1, 153–163 (2021).

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