Enabling single-cell trajectory network enrichment

Alexander G. B. Grønning*, Mhaned Oubounyt, Kristiyan Kanev, Jesper Lund, Tim Kacprowski, Dietmar Zehn, Richard Röttger, Jan Baumbach*


Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review


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.
TidsskriftNature Computational Science
Udgave nummer2
Sider (fra-til)153-163
StatusUdgivet - 2021


Dyk ned i forskningsemnerne om 'Enabling single-cell trajectory network enrichment'. Sammen danner de et unikt fingeraftryk.