Trajectory-informed gene feature selection in single-cell analysis with SEEK-VFI

Abstract

The prioritization of highly-variable genes is an important step in single-cell trajectory inference. However, when variability arises from a continuous latent cell development trajectory, standard methods may fail to differentiate trajectory-relevant from uninformative genes. SEEK-VFI is an ensemble topic-modeling machine learning algorithm for trajectory inference preprocessing that prioritizes trajectory-relevant genes. It outperforms existing methods, and identifies key genes that improve trajectory topology reconstruction, enhance visualization, and augment downstream trajectory analyses.