VIB research hits bioRxiv’s most downloaded top 10

31 January 2019
Every year, Rxivist compiles a list of the most downloaded bioRxiv preprints. The organization has recently put together the list for 2018. And at number 10, we find a paper from the Yvan Saeys group at the VIB-UGent Center for Inflammation Research. This is the first time VIB research makes it into the top 10 of this list.

The paper, ‘A comparison of single-cell trajectory inference methods: towards more accurate and robust tools’ joins a highly impactful group of papers that go on to shape their field of research. As a matter of fact, the paper in question has recently been accepted by the prestigious journal Nature Biotechnology.
The study – spearheaded by PhD students Wouter Saelens and Robrecht Cannoodt – benchmarks several single-cell trajectory methods. In short: since the rise of single-cell-level -omics technologies, it has become possible to follow cells during their lifecycle, mapping the trajectory they take. This unprecedented level of detail will revolutionize our understanding of cellular dynamic processes. However, since 2014 over 70 methods to infer cell trajectories have been developed. This makes it very difficult to choose the appropriate method for a specific research question, since no one had previously compared the different cell trajectory inference methods.

Now, the team of Yvan Saeys, provides the first evaluation of these inference methods. They compared 45 methods using several metrics, including the accuracy of the inferred ordering, the correctness of the network topology, code quality, and user friendliness. They found that some methods outperform others, although their performance depended on the type of trajectory present in the data. Based on the benchmarking results, the team developed a set of user guidelines that can assist researchers in selecting the most suitable method for a specific research question, as well as an interactive app.

This is the first comprehensive assessment of trajectory inference methods. In the future, the team plans to add a detailed parameter tuning procedure.

The evaluation pipeline is available github.com/dynverse/dynverse, and the team welcomes discussion aimed at further development.

Full paper here available 


Research


Yvan Saeys
©VIB-Ine Dehandschutter