Comparison of cell trajectory tools in Nature Biotechnology

A few months ago, a study of the prof. Yvan Saeys group, entitled ‘A comparison of single-cell trajectory inference methods’ hitted number 10 in the bioRxiv’s list of most downloaded preprints of 2018 (news item 08/02/2019). Now, their study is published in the top journal Nature Biotechnology.  

The intent of the study is nicely explained by prof. Yvan Saeys in an interview with VIB: "If you would take a random sample of thousands of cells that are changing, you would see that some are very similar, while others are really different. Trajectory inference methods are a novel class of Artificial Intelligence techniques that unveil complex structures such as cell trajectories in a data-driven way. In recent years there has been a proliferation of tools that construct such a trajectory. But the availability of a wide variety of such tools makes it very difficult for researchers to find the right one that will work in the biological system they are studying."

The extensive effort the researchers made to include and compare almost all methods available in detail, was put together in one large figure, their funky heatmap (picture). In the future, the team plans to add a detailed parameter tuning procedure. The pipeline and tools for creating trajectories are freely available on dynverse.org, and the team welcomes discussion aimed at further development.

More info on the VIB website.

The Nature Biotechnology article can be found here.