A1 Refereed original research article in a scientific journal

Cell-connectivity-guided trajectory inference from single-cell data




AuthorsSmolander Johannes, Junttila Sini, Elo Laura L.

PublisherOxford University Press

Publication year2023

JournalBioinformatics

Article numberbtad515

Volume39

Issue9

eISSN1367-4811

DOIhttps://doi.org/10.1093/bioinformatics/btad515

Web address https://academic.oup.com/bioinformatics/article/39/9/btad515/7251030

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/181089309


Abstract

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.


Downloadable publication

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Last updated on 2025-27-03 at 21:55