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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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.
The authors declare no competing interests.
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). https://doi.org/10.1038/s43588-021-00025-y
Nature Computational Science (2021)