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Cell-connectivity-guided trajectory inference from single-cell data
Tekijät: Smolander Johannes, Junttila Sini, Elo Laura L.
Kustantaja: Oxford University Press
Julkaisuvuosi: 2023
Journal: Bioinformatics
Artikkelin numero: btad515
Vuosikerta: 39
Numero: 9
eISSN: 1367-4811
DOI: https://doi.org/10.1093/bioinformatics/btad515
Verkko-osoite: https://academic.oup.com/bioinformatics/article/39/9/btad515/7251030
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/181089309
Motivation
Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise.
Results
We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree- shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge.
Availability and implementation
Totem is available as an R package at https://github.com/elolab/Totem.
Ladattava julkaisu This is an electronic reprint of the original article. |